API documentation for vaex library

Quick lists

Opening/reading in your data.

vaex.open(path[, convert, shuffle, fs_options])

Open a DataFrame from file given by path.

vaex.concat(dfs[, resolver])

Concatenate a list of DataFrames.

vaex.from_arrow_table(table)

Creates a vaex DataFrame from an arrow Table.

vaex.from_arrays(**arrays)

Create an in memory DataFrame from numpy arrays.

vaex.from_dict(data)

Create an in memory dataset from a dict with column names as keys and list/numpy-arrays as values

vaex.from_csv(filename_or_buffer[, …])

Read a CSV file as a DataFrame, and optionally convert to an hdf5 file.

vaex.from_ascii(path[, seperator, names, …])

Create an in memory DataFrame from an ascii file (whitespace seperated by default).

vaex.from_pandas(df[, name, copy_index, …])

Create an in memory DataFrame from a pandas DataFrame.

vaex.from_astropy_table(table)

Create a vaex DataFrame from an Astropy Table.

Visualization.

Statistics.

vaex.dataframe.DataFrame.count([expression, …])

Count the number of non-NaN values (or all, if expression is None or “*”).

vaex.dataframe.DataFrame.mean(expression[, …])

Calculate the mean for expression, possibly on a grid defined by binby.

vaex.dataframe.DataFrame.std(expression[, …])

Calculate the standard deviation for the given expression, possible on a grid defined by binby

vaex.dataframe.DataFrame.var(expression[, …])

Calculate the sample variance for the given expression, possible on a grid defined by binby

vaex.dataframe.DataFrame.cov(x[, y, binby, …])

Calculate the covariance matrix for x and y or more expressions, possibly on a grid defined by binby.

vaex.dataframe.DataFrame.correlation(x[, y, …])

Calculate the correlation coefficient cov[x,y]/(std[x]*std[y]) between x and y, possibly on a grid defined by binby.

vaex.dataframe.DataFrame.median_approx(…)

Calculate the median, possibly on a grid defined by binby.

vaex.dataframe.DataFrame.mode(expression[, …])

Calculate/estimate the mode.

vaex.dataframe.DataFrame.min(expression[, …])

Calculate the minimum for given expressions, possibly on a grid defined by binby.

vaex.dataframe.DataFrame.max(expression[, …])

Calculate the maximum for given expressions, possibly on a grid defined by binby.

vaex.dataframe.DataFrame.minmax(expression)

Calculate the minimum and maximum for expressions, possibly on a grid defined by binby.

vaex.dataframe.DataFrame.mutual_information(x)

Estimate the mutual information between and x and y on a grid with shape mi_shape and mi_limits, possibly on a grid defined by binby.

vaex-core

Vaex is a library for dealing with larger than memory DataFrames (out of core).

The most important class (datastructure) in vaex is the DataFrame. A DataFrame is obtained by either opening the example dataset:

>>> import vaex
>>> df = vaex.example()

Or using open() to open a file.

>>> df1 = vaex.open("somedata.hdf5")
>>> df2 = vaex.open("somedata.fits")
>>> df2 = vaex.open("somedata.arrow")
>>> df4 = vaex.open("somedata.csv")

Or connecting to a remove server:

>>> df_remote = vaex.open("http://try.vaex.io/nyc_taxi_2015")

A few strong features of vaex are:

  • Performance: works with huge tabular data, process over a billion (> 109) rows/second.

  • Expression system / Virtual columns: compute on the fly, without wasting ram.

  • Memory efficient: no memory copies when doing filtering/selections/subsets.

  • Visualization: directly supported, a one-liner is often enough.

  • User friendly API: you will only need to deal with a DataFrame object, and tab completion + docstring will help you out: ds.mean<tab>, feels very similar to Pandas.

  • Very fast statistics on N dimensional grids such as histograms, running mean, heatmaps.

Follow the tutorial at https://docs.vaex.io/en/latest/tutorial.html to learn how to use vaex.

vaex.app(*args, **kwargs)[source]

Create a vaex app, the QApplication mainloop must be started.

In ipython notebook/jupyter do the following:

>>> import vaex.ui.main # this causes the qt api level to be set properly
>>> import vaex

Next cell:

>>> %gui qt

Next cell:

>>> app = vaex.app()

From now on, you can run the app along with jupyter

vaex.concat(dfs, resolver='flexible')vaex.dataframe.DataFrame[source]

Concatenate a list of DataFrames.

Parameters

resolver – How to resolve schema conflicts, see DataFrame.concat().

vaex.delayed(f)[source]

Decorator to transparantly accept delayed computation.

Example:

>>> delayed_sum = ds.sum(ds.E, binby=ds.x, limits=limits,
>>>                   shape=4, delay=True)
>>> @vaex.delayed
>>> def total_sum(sums):
>>>     return sums.sum()
>>> sum_of_sums = total_sum(delayed_sum)
>>> ds.execute()
>>> sum_of_sums.get()
See the tutorial for a more complete example https://docs.vaex.io/en/latest/tutorial.html#Parallel-computations
vaex.example()[source]

Returns an example DataFrame which comes with vaex for testing/learning purposes.

Return type

DataFrame

vaex.from_arrays(**arrays)[source]

Create an in memory DataFrame from numpy arrays.

Example

>>> import vaex, numpy as np
>>> x = np.arange(5)
>>> y = x ** 2
>>> vaex.from_arrays(x=x, y=y)
  #    x    y
  0    0    0
  1    1    1
  2    2    4
  3    3    9
  4    4   16
>>> some_dict = {'x': x, 'y': y}
>>> vaex.from_arrays(**some_dict)  # in case you have your columns in a dict
  #    x    y
  0    0    0
  1    1    1
  2    2    4
  3    3    9
  4    4   16
Parameters

arrays – keyword arguments with arrays

Return type

DataFrame

vaex.from_arrow_table(table)vaex.dataframe.DataFrame[source]

Creates a vaex DataFrame from an arrow Table.

Parameters

as_numpy – Will lazily cast columns to a NumPy ndarray.

Return type

DataFrame

vaex.from_ascii(path, seperator=None, names=True, skip_lines=0, skip_after=0, **kwargs)[source]

Create an in memory DataFrame from an ascii file (whitespace seperated by default).

>>> ds = vx.from_ascii("table.asc")
>>> ds = vx.from_ascii("table.csv", seperator=",", names=["x", "y", "z"])
Parameters
  • path – file path

  • seperator – value seperator, by default whitespace, use “,” for comma seperated values.

  • names – If True, the first line is used for the column names, otherwise provide a list of strings with names

  • skip_lines – skip lines at the start of the file

  • skip_after – skip lines at the end of the file

  • kwargs

Return type

DataFrame

vaex.from_astropy_table(table)[source]

Create a vaex DataFrame from an Astropy Table.

vaex.from_csv(filename_or_buffer, copy_index=False, chunk_size=None, convert=False, fs_options={}, **kwargs)[source]

Read a CSV file as a DataFrame, and optionally convert to an hdf5 file.

Parameters
  • or file filename_or_buffer (str) – CSV file path or file-like

  • copy_index (bool) – copy index when source is read via Pandas

  • chunk_size (int) –

    if the CSV file is too big to fit in the memory this parameter can be used to read CSV file in chunks. For example:

    >>> import vaex
    >>> for i, df in enumerate(vaex.from_csv('taxi.csv', chunk_size=100_000)):
    >>>     df = df[df.passenger_count < 6]
    >>>     df.export_hdf5(f'taxi_{i:02}.hdf5')
    

  • or str convert (bool) – convert files to an hdf5 file for optimization, can also be a path. The CSV file will be read in chunks: either using the provided chunk_size argument, or a default size. Each chunk will be saved as a separate hdf5 file, then all of them will be combined into one hdf5 file. So for a big CSV file you will need at least double of extra space on the disk. Default chunk_size for converting is 5 million rows, which corresponds to around 1Gb memory on an example of NYC Taxi dataset.

  • kwargs – extra keyword arguments, currently passed to Pandas read_csv function, but the implementation might change in future versions.

Returns

DataFrame

vaex.from_dict(data)[source]

Create an in memory dataset from a dict with column names as keys and list/numpy-arrays as values

Example

>>> data = {'A':[1,2,3],'B':['a','b','c']}
>>> vaex.from_dict(data)
  #    A    B
  0    1   'a'
  1    2   'b'
  2    3   'c'
Parameters

data – A dict of {column:[value, value,…]}

Return type

DataFrame

vaex.from_items(*items)[source]

Create an in memory DataFrame from numpy arrays, in contrast to from_arrays this keeps the order of columns intact (for Python < 3.6).

Example

>>> import vaex, numpy as np
>>> x = np.arange(5)
>>> y = x ** 2
>>> vaex.from_items(('x', x), ('y', y))
  #    x    y
  0    0    0
  1    1    1
  2    2    4
  3    3    9
  4    4   16
Parameters

items – list of [(name, numpy array), …]

Return type

DataFrame

vaex.from_pandas(df, name='pandas', copy_index=False, index_name='index')[source]

Create an in memory DataFrame from a pandas DataFrame.

Param

pandas.DataFrame df: Pandas DataFrame

Param

name: unique for the DataFrame

>>> import vaex, pandas as pd
>>> df_pandas = pd.from_csv('test.csv')
>>> df = vaex.from_pandas(df_pandas)
Return type

DataFrame

vaex.from_samp(username=None, password=None)[source]

Connect to a SAMP Hub and wait for a single table load event, disconnect, download the table and return the DataFrame.

Useful if you want to send a single table from say TOPCAT to vaex in a python console or notebook.

vaex.open(path, convert=False, shuffle=False, fs_options={}, *args, **kwargs)[source]

Open a DataFrame from file given by path.

Example:

>>> df = vaex.open('sometable.hdf5')
>>> df = vaex.open('somedata*.csv', convert='bigdata.hdf5')
Parameters
  • or list path (str) – local or absolute path to file, or glob string, or list of paths

  • convert – Uses dataframe.export when convert is a path. If True, convert=path+'.hdf5' The conversion is skipped if the input file or conversion argument did not change.

  • shuffle (bool) – shuffle converted DataFrame or not

  • fs_options (dict) –

    Extra arguments passed to an optional file system if needed: * Amazon AWS S3

    • anonymous - access file without authentication (public files)

    • access_key - AWS access key, if not provided will use the standard env vars, or the ~/.aws/credentials file

    • secret_key - AWS secret key, similar to access_key

    • profile - If multiple profiles are present in ~/.aws/credentials, pick this one instead of ‘default’, see https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html

    • region - AWS Region, e.g. ‘us-east-1`, will be determined automatically if not provided.

    • endpoint_override - URL/ip to connect to, instead of AWS, e.g. ‘localhost:9000’ for minio

    In addition you can pass the boolean “cache” option.

  • args – extra arguments for file readers that need it

  • kwargs – extra keyword arguments

Returns

return a DataFrame on success, otherwise None

Return type

DataFrame

Cloud storage support:

Vaex supports streaming of HDF5 files from Amazon AWS S3 and Google Cloud Storage. Files are by default cached in $HOME/.vaex/file-cache/(s3|gs) such that successive access is as fast as native disk access.

The following common fs_options are used for S3 access:

  • anon: Use anonymous access or not (false by default). (Allowed values are: true,True,1,false,False,0)

  • cache: Use the disk cache or not, only set to false if the data should be accessed once. (Allowed values are: true,True,1,false,False,0)

All fs_options can also be encoded in the file path as a query string.

Examples:

>>> df = vaex.open('s3://vaex/taxi/yellow_taxi_2015_f32s.hdf5', fs_options={'anonymous': True})
>>> df = vaex.open('s3://vaex/taxi/yellow_taxi_2015_f32s.hdf5?anon=true')
>>> df = vaex.open('s3://mybucket/path/to/file.hdf5', fs_options={'access_key': my_key, 'secret_key': my_secret_key})
>>> df = vaex.open(f's3://mybucket/path/to/file.hdf5?access_key={{my_key}}&secret_key={{my_secret_key}}')
>>> df = vaex.open('s3://mybucket/path/to/file.hdf5?profile=myproject')

Google Cloud Storage support:

The following fs_options are used for GCP access:

Examples:

>>> df = vaex.open('gs://vaex-data/airlines/us_airline_data_1988_2019.hdf5', fs_options={'token': None})
>>> df = vaex.open('gs://vaex-data/airlines/us_airline_data_1988_2019.hdf5?token=anon')
>>> df = vaex.open('gs://vaex-data/testing/xys.hdf5?token=anon&cache=False')
vaex.open_many(filenames)[source]

Open a list of filenames, and return a DataFrame with all DataFrames concatenated.

Parameters

filenames (list[str]) – list of filenames/paths

Return type

DataFrame

vaex.register_function(scope=None, as_property=False, name=None, on_expression=True, df_accessor=None, multiprocessing=False)[source]

Decorator to register a new function with vaex.

If on_expression is True, the function will be available as a method on an Expression, where the first argument will be the expression itself.

If df_accessor is given, it is added as a method to that dataframe accessor (see e.g. vaex/geo.py)

Example:

>>> import vaex
>>> df = vaex.example()
>>> @vaex.register_function()
>>> def invert(x):
>>>     return 1/x
>>> df.x.invert()
>>> import numpy as np
>>> df = vaex.from_arrays(departure=np.arange('2015-01-01', '2015-12-05', dtype='datetime64'))
>>> @vaex.register_function(as_property=True, scope='dt')
>>> def dt_relative_day(x):
>>>     return vaex.functions.dt_dayofyear(x)/365.
>>> df.departure.dt.relative_day

DataFrame class

class vaex.dataframe.DataFrame(name=None, executor=None)[source]

Bases: object

All local or remote datasets are encapsulated in this class, which provides a pandas like API to your dataset.

Each DataFrame (df) has a number of columns, and a number of rows, the length of the DataFrame.

All DataFrames have multiple ‘selection’, and all calculations are done on the whole DataFrame (default) or for the selection. The following example shows how to use the selection.

>>> df.select("x < 0")
>>> df.sum(df.y, selection=True)
>>> df.sum(df.y, selection=[df.x < 0, df.x > 0])
__delitem__(item)[source]

Alias of df.drop(item, inplace=True)

__getitem__(item)[source]

Convenient way to get expressions, (shallow) copies of a few columns, or to apply filtering.

Example:

>>> df['Lz']  # the expression 'Lz
>>> df['Lz/2'] # the expression 'Lz/2'
>>> df[["Lz", "E"]] # a shallow copy with just two columns
>>> df[df.Lz < 0]  # a shallow copy with the filter Lz < 0 applied
__init__(name=None, executor=None)[source]

Initialize self. See help(type(self)) for accurate signature.

__iter__()[source]

Iterator over the column names.

__len__()[source]

Returns the number of rows in the DataFrame (filtering applied).

__repr__()[source]

Return repr(self).

__setitem__(name, value)[source]

Convenient way to add a virtual column / expression to this DataFrame.

Example:

>>> import vaex, numpy as np
>>> df = vaex.example()
>>> df['r'] = np.sqrt(df.x**2 + df.y**2 + df.z**2)
>>> df.r
<vaex.expression.Expression(expressions='r')> instance at 0x121687e80 values=[2.9655450396553587, 5.77829281049018, 6.99079603950256, 9.431842752707537, 0.8825613121347967 ... (total 330000 values) ... 7.453831761514681, 15.398412491068198, 8.864250273925633, 17.601047186042507, 14.540181524970293]
__str__()[source]

Return str(self).

__weakref__

list of weak references to the object (if defined)

add_column(name, f_or_array, dtype=None)[source]

Add an in memory array as a column.

add_variable(name, expression, overwrite=True, unique=True)[source]

Add a variable to a DataFrame.

A variable may refer to other variables, and virtual columns and expression may refer to variables.

Example

>>> df.add_variable('center', 0)
>>> df.add_virtual_column('x_prime', 'x-center')
>>> df.select('x_prime < 0')
Param

str name: name of virtual varible

Param

expression: expression for the variable

add_virtual_column(name, expression, unique=False)[source]

Add a virtual column to the DataFrame.

Example:

>>> df.add_virtual_column("r", "sqrt(x**2 + y**2 + z**2)")
>>> df.select("r < 10")
Param

str name: name of virtual column

Param

expression: expression for the column

Parameters

unique (str) – if name is already used, make it unique by adding a postfix, e.g. _1, or _2

apply(f, arguments=None, vectorize=False, multiprocessing=True)[source]

Apply a function on a per row basis across the entire DataFrame.

Example:

>>> import vaex
>>> df = vaex.example()
>>> def func(x, y):
...     return (x+y)/(x-y)
...
>>> df.apply(func, arguments=[df.x, df.y])
Expression = lambda_function(x, y)
Length: 330,000 dtype: float64 (expression)
-------------------------------------------
     0  -0.460789
     1    3.90038
     2  -0.642851
     3   0.685768
     4  -0.543357
Parameters
  • f – The function to be applied

  • arguments – List of arguments to be passed on to the function f.

  • vectorize – Call f with arrays instead of a scalars (for better performance).

  • multiprocessing (bool) – Use multiple processes to avoid the GIL (Global interpreter lock).

Returns

A function that is lazily evaluated.

byte_size(selection=False, virtual=False)[source]

Return the size in bytes the whole DataFrame requires (or the selection), respecting the active_fraction.

cat(i1, i2, format='html')[source]

Display the DataFrame from row i1 till i2

For format, see https://pypi.org/project/tabulate/

Parameters
  • i1 (int) – Start row

  • i2 (int) – End row.

  • format (str) – Format to use, e.g. ‘html’, ‘plain’, ‘latex’

close()[source]

Close any possible open file handles or other resources, the DataFrame will not be in a usable state afterwards.

property col

Gives direct access to the columns only (useful for tab completion).

Convenient when working with ipython in combination with small DataFrames, since this gives tab-completion.

Columns can be accessed by their names, which are attributes. The attributes are currently expressions, so you can do computations with them.

Example

>>> ds = vaex.example()
>>> df.plot(df.col.x, df.col.y)
column_count(hidden=False)[source]

Returns the number of columns (including virtual columns).

Parameters

hidden (bool) – If True, include hidden columns in the tally

Returns

Number of columns in the DataFrame

combinations(expressions_list=None, dimension=2, exclude=None, **kwargs)[source]

Generate a list of combinations for the possible expressions for the given dimension.

Parameters
  • expressions_list – list of list of expressions, where the inner list defines the subspace

  • dimensions – if given, generates a subspace with all possible combinations for that dimension

  • exclude – list of

correlation(x, y=None, binby=[], limits=None, shape=128, sort=False, sort_key=<ufunc 'absolute'>, selection=False, delay=False, progress=None)[source]

Calculate the correlation coefficient cov[x,y]/(std[x]*std[y]) between x and y, possibly on a grid defined by binby.

Example:

>>> df.correlation("x**2+y**2+z**2", "-log(-E+1)")
array(0.6366637382215669)
>>> df.correlation("x**2+y**2+z**2", "-log(-E+1)", binby="Lz", shape=4)
array([ 0.40594394,  0.69868851,  0.61394099,  0.65266318])
Parameters
  • x – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • y – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic

count(expression=None, binby=[], limits=None, shape=128, selection=False, delay=False, edges=False, progress=None, array_type=None)[source]

Count the number of non-NaN values (or all, if expression is None or “*”).

Example:

>>> df.count()
330000
>>> df.count("*")
330000.0
>>> df.count("*", binby=["x"], shape=4)
array([  10925.,  155427.,  152007.,   10748.])
Parameters
  • expression – Expression or column for which to count non-missing values, or None or ‘*’ for counting the rows

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • edges – Currently for internal use only (it includes nan’s and values outside the limits at borders, nan and 0, smaller than at 1, and larger at -1

  • array_type – Type of output array, possible values are None/”numpy” (ndarray), “xarray” for a xarray.DataArray, or “list” for a Python list

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic

cov(x, y=None, binby=[], limits=None, shape=128, selection=False, delay=False, progress=None)[source]

Calculate the covariance matrix for x and y or more expressions, possibly on a grid defined by binby.

Either x and y are expressions, e.g.:

>>> df.cov("x", "y")

Or only the x argument is given with a list of expressions, e.g.:

>>> df.cov(["x, "y, "z"])

Example:

>>> df.cov("x", "y")
array([[ 53.54521742,  -3.8123135 ],
[ -3.8123135 ,  60.62257881]])
>>> df.cov(["x", "y", "z"])
array([[ 53.54521742,  -3.8123135 ,  -0.98260511],
[ -3.8123135 ,  60.62257881,   1.21381057],
[ -0.98260511,   1.21381057,  25.55517638]])
>>> df.cov("x", "y", binby="E", shape=2)
array([[[  9.74852878e+00,  -3.02004780e-02],
[ -3.02004780e-02,   9.99288215e+00]],
[[  8.43996546e+01,  -6.51984181e+00],
[ -6.51984181e+00,   9.68938284e+01]]])
Parameters
  • x – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • y – if previous argument is not a list, this argument should be given

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic, the last dimensions are of shape (2,2)

covar(x, y, binby=[], limits=None, shape=128, selection=False, delay=False, progress=None)[source]

Calculate the covariance cov[x,y] between x and y, possibly on a grid defined by binby.

Example:

>>> df.covar("x**2+y**2+z**2", "-log(-E+1)")
array(52.69461456005138)
>>> df.covar("x**2+y**2+z**2", "-log(-E+1)")/(df.std("x**2+y**2+z**2") * df.std("-log(-E+1)"))
0.63666373822156686
>>> df.covar("x**2+y**2+z**2", "-log(-E+1)", binby="Lz", shape=4)
array([ 10.17387143,  51.94954078,  51.24902796,  20.2163929 ])
Parameters
  • x – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • y – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic

data_type(expression, array_type=None, internal=False)[source]

Return the datatype for the given expression, if not a column, the first row will be evaluated to get the data type.

Example:

>>> df = vaex.from_scalars(x=1, s='Hi')
Parameters

array_type (str) – ‘numpy’, ‘arrow’ or None, to indicate if the data type should be converted

delete_variable(name)[source]

Deletes a variable from a DataFrame.

delete_virtual_column(name)[source]

Deletes a virtual column from a DataFrame.

describe(strings=True, virtual=True, selection=None)[source]

Give a description of the DataFrame.

>>> import vaex
>>> df = vaex.example()[['x', 'y', 'z']]
>>> df.describe()
                 x          y          z
dtype      float64    float64    float64
count       330000     330000     330000
missing          0          0          0
mean    -0.0671315 -0.0535899  0.0169582
std        7.31746    7.78605    5.05521
min       -128.294   -71.5524   -44.3342
max        271.366    146.466    50.7185
>>> df.describe(selection=df.x > 0)
                   x         y          z
dtype        float64   float64    float64
count         164060    164060     164060
missing       165940    165940     165940
mean         5.13572 -0.486786 -0.0868073
std          5.18701   7.61621    5.02831
min      1.51635e-05  -71.5524   -44.3342
max          271.366   78.0724    40.2191
Parameters
  • strings (bool) – Describe string columns or not

  • virtual (bool) – Describe virtual columns or not

  • selection – Optional selection to use.

Returns

Pandas dataframe

drop(columns, inplace=False, check=True)[source]

Drop columns (or a single column).

Parameters
  • columns – List of columns or a single column name

  • inplace – Make modifications to self or return a new DataFrame

  • check – When true, it will check if the column is used in virtual columns or the filter, and hide it instead.

drop_filter(inplace=False)[source]

Removes all filters from the DataFrame

dropinf(column_names=None)[source]

Create a shallow copy of a DataFrame, with filtering set using isinf. :param column_names: The columns to consider, default: all (real, non-virtual) columns :rtype: DataFrame

dropmissing(column_names=None)[source]

Create a shallow copy of a DataFrame, with filtering set using ismissing.

Parameters

column_names – The columns to consider, default: all (real, non-virtual) columns

Return type

DataFrame

dropna(column_names=None)[source]

Create a shallow copy of a DataFrame, with filtering set using isna.

Parameters

column_names – The columns to consider, default: all (real, non-virtual) columns

Return type

DataFrame

dropnan(column_names=None)[source]

Create a shallow copy of a DataFrame, with filtering set using isnan.

Parameters

column_names – The columns to consider, default: all (real, non-virtual) columns

Return type

DataFrame

property dtypes

Gives a Pandas series object containing all numpy dtypes of all columns (except hidden).

evaluate(expression, i1=None, i2=None, out=None, selection=None, filtered=True, array_type=None, parallel=True, chunk_size=None)[source]

Evaluate an expression, and return a numpy array with the results for the full column or a part of it.

Note that this is not how vaex should be used, since it means a copy of the data needs to fit in memory.

To get partial results, use i1 and i2

Parameters
  • expression (str) – Name/expression to evaluate

  • i1 (int) – Start row index, default is the start (0)

  • i2 (int) – End row index, default is the length of the DataFrame

  • out (ndarray) – Output array, to which the result may be written (may be used to reuse an array, or write to a memory mapped array)

  • selection – selection to apply

Returns

evaluate_iterator(expression, s1=None, s2=None, out=None, selection=None, filtered=True, array_type=None, parallel=True, chunk_size=None, prefetch=True)[source]

Generator to efficiently evaluate expressions in chunks (number of rows).

See DataFrame.evaluate() for other arguments.

Example:

>>> import vaex
>>> df = vaex.example()
>>> for i1, i2, chunk in df.evaluate_iterator(df.x, chunk_size=100_000):
...     print(f"Total of {i1} to {i2} = {chunk.sum()}")
...
Total of 0 to 100000 = -7460.610158279056
Total of 100000 to 200000 = -4964.85827154921
Total of 200000 to 300000 = -7303.271340043915
Total of 300000 to 330000 = -2424.65234724951
Parameters

prefetch – Prefetch/compute the next chunk in parallel while the current value is yielded/returned.

evaluate_variable(name)[source]

Evaluates the variable given by name.

execute()[source]

Execute all delayed jobs.

async execute_async()[source]

Async version of execute

extract()[source]

Return a DataFrame containing only the filtered rows.

Note

Note that no copy of the underlying data is made, only a view/reference is made.

The resulting DataFrame may be more efficient to work with when the original DataFrame is heavily filtered (contains just a small number of rows).

If no filtering is applied, it returns a trimmed view. For the returned df, len(df) == df.length_original() == df.length_unfiltered()

Return type

DataFrame

fillna(value, column_names=None, prefix='__original_', inplace=False)[source]

Return a DataFrame, where missing values/NaN are filled with ‘value’.

The original columns will be renamed, and by default they will be hidden columns. No data is lost.

Note

Note that no copy of the underlying data is made, only a view/reference is made.

Note

Note that filtering will be ignored (since they may change), you may want to consider running extract() first.

Example:

>>> import vaex
>>> import numpy as np
>>> x = np.array([3, 1, np.nan, 10, np.nan])
>>> df = vaex.from_arrays(x=x)
>>> df_filled = df.fillna(value=-1, column_names=['x'])
>>> df_filled
  #    x
  0    3
  1    1
  2   -1
  3   10
  4   -1
Parameters
  • value (float) – The value to use for filling nan or masked values.

  • fill_na (bool) – If True, fill np.nan values with value.

  • fill_masked (bool) – If True, fill masked values with values.

  • column_names (list) – List of column names in which to fill missing values.

  • prefix (str) – The prefix to give the original columns.

  • inplace – Make modifications to self or return a new DataFrame

filter(expression, mode='and')[source]

General version of df[<boolean expression>] to modify the filter applied to the DataFrame.

See DataFrame.select() for usage of selection.

Note that using df = df[<boolean expression>], one can only narrow the filter (i.e. only less rows can be selected). Using the filter method, and a different boolean mode (e.g. “or”) one can actually cause more rows to be selected. This differs greatly from numpy and pandas for instance, which can only narrow the filter.

Example:

>>> import vaex
>>> import numpy as np
>>> x = np.arange(10)
>>> df = vaex.from_arrays(x=x, y=x**2)
>>> df
#    x    y
0    0    0
1    1    1
2    2    4
3    3    9
4    4   16
5    5   25
6    6   36
7    7   49
8    8   64
9    9   81
>>> dff = df[df.x<=2]
>>> dff
#    x    y
0    0    0
1    1    1
2    2    4
>>> dff = dff.filter(dff.x >=7, mode="or")
>>> dff
#    x    y
0    0    0
1    1    1
2    2    4
3    7   49
4    8   64
5    9   81
first(expression, order_expression, binby=[], limits=None, shape=128, selection=False, delay=False, edges=False, progress=None, array_type=None)[source]

Return the first element of a binned expression, where the values each bin are sorted by order_expression.

Example:

>>> import vaex
>>> df = vaex.example()
>>> df.first(df.x, df.y, shape=8)
>>> df.first(df.x, df.y, shape=8, binby=[df.y])
>>> df.first(df.x, df.y, shape=8, binby=[df.y])
array([-4.81883764, 11.65378   ,  9.70084476, -7.3025589 ,  4.84954977,
        8.47446537, -5.73602629, 10.18783   ])
Parameters
  • expression – The value to be placed in the bin.

  • order_expression – Order the values in the bins by this expression.

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • edges – Currently for internal use only (it includes nan’s and values outside the limits at borders, nan and 0, smaller than at 1, and larger at -1

  • array_type – Type of output array, possible values are None/”numpy” (ndarray), “xarray” for a xarray.DataArray, or “list” for a Python list

Returns

Ndarray containing the first elements.

Return type

numpy.array

get_active_fraction()[source]

Value in the range (0, 1], to work only with a subset of rows.

get_column_names(virtual=True, strings=True, hidden=False, regex=None)[source]

Return a list of column names

Example:

>>> import vaex
>>> df = vaex.from_scalars(x=1, x2=2, y=3, s='string')
>>> df['r'] = (df.x**2 + df.y**2)**2
>>> df.get_column_names()
['x', 'x2', 'y', 's', 'r']
>>> df.get_column_names(virtual=False)
['x', 'x2', 'y', 's']
>>> df.get_column_names(regex='x.*')
['x', 'x2']
Parameters
  • virtual – If False, skip virtual columns

  • hidden – If False, skip hidden columns

  • strings – If False, skip string columns

  • regex – Only return column names matching the (optional) regular expression

  • alias – Return the alias (True) or internal name (False).

Return type

list of str

get_current_row()[source]

Individual rows can be ‘picked’, this is the index (integer) of the current row, or None there is nothing picked.

get_names(hidden=False)[source]

Return a list of column names and variable names.

get_private_dir(create=False)[source]

Each DataFrame has a directory where files are stored for metadata etc.

Example

>>> import vaex
>>> ds = vaex.example()
>>> vaex.get_private_dir()
'/Users/users/breddels/.vaex/dfs/_Users_users_breddels_vaex-testing_data_helmi-dezeeuw-2000-10p.hdf5'
Parameters

create (bool) – is True, it will create the directory if it does not exist

get_selection(name='default')[source]

Get the current selection object (mostly for internal use atm).

get_variable(name)[source]

Returns the variable given by name, it will not evaluate it.

For evaluation, see DataFrame.evaluate_variable(), see also DataFrame.set_variable()

has_current_row()[source]

Returns True/False if there currently is a picked row.

has_selection(name='default')[source]

Returns True if there is a selection with the given name.

head(n=10)[source]

Return a shallow copy a DataFrame with the first n rows.

head_and_tail_print(n=5)[source]

Display the first and last n elements of a DataFrame.

healpix_count(expression=None, healpix_expression=None, healpix_max_level=12, healpix_level=8, binby=None, limits=None, shape=128, delay=False, progress=None, selection=None)[source]

Count non missing value for expression on an array which represents healpix data.

Parameters
  • expression – Expression or column for which to count non-missing values, or None or ‘*’ for counting the rows

  • healpix_expression – {healpix_max_level}

  • healpix_max_level – {healpix_max_level}

  • healpix_level – {healpix_level}

  • binby – {binby}, these dimension follow the first healpix dimension.

  • limits – {limits}

  • shape – {shape}

  • selection – {selection}

  • delay – {delay}

  • progress – {progress}

Returns

is_category(column)[source]

Returns true if column is a category.

is_local()[source]

Returns True if the DataFrame is local, False when a DataFrame is remote.

is_masked(column)[source]

Return if a column is a masked (numpy.ma) column.

length_original()[source]

the full length of the DataFrame, independent what active_fraction is, or filtering. This is the real length of the underlying ndarrays.

length_unfiltered()[source]

The length of the arrays that should be considered (respecting active range), but without filtering.

limits(expression, value=None, square=False, selection=None, delay=False, shape=None)[source]

Calculate the [min, max] range for expression, as described by value, which is ‘minmax’ by default.

If value is a list of the form [minvalue, maxvalue], it is simply returned, this is for convenience when using mixed forms.

Example:

>>> import vaex
>>> df = vaex.example()
>>> df.limits("x")
array([-128.293991,  271.365997])
>>> df.limits("x", "99.7%")
array([-28.86381927,  28.9261226 ])
>>> df.limits(["x", "y"])
(array([-128.293991,  271.365997]), array([ -71.5523682,  146.465836 ]))
>>> df.limits(["x", "y"], "99.7%")
(array([-28.86381927,  28.9261226 ]), array([-28.60476934,  28.96535249]))
>>> df.limits(["x", "y"], ["minmax", "90%"])
(array([-128.293991,  271.365997]), array([-13.37438402,  13.4224423 ]))
>>> df.limits(["x", "y"], ["minmax", [0, 10]])
(array([-128.293991,  271.365997]), [0, 10])
Parameters
  • expression – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • value – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

Returns

List in the form [[xmin, xmax], [ymin, ymax], …. ,[zmin, zmax]] or [xmin, xmax] when expression is not a list

limits_percentage(expression, percentage=99.73, square=False, selection=False, delay=False)[source]

Calculate the [min, max] range for expression, containing approximately a percentage of the data as defined by percentage.

The range is symmetric around the median, i.e., for a percentage of 90, this gives the same results as:

Example:

>>> df.limits_percentage("x", 90)
array([-12.35081376,  12.14858052]
>>> df.percentile_approx("x", 5), df.percentile_approx("x", 95)
(array([-12.36813152]), array([ 12.13275818]))

NOTE: this value is approximated by calculating the cumulative distribution on a grid. NOTE 2: The values above are not exactly the same, since percentile and limits_percentage do not share the same code

Parameters
  • expression – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • percentage (float) – Value between 0 and 100

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

Returns

List in the form [[xmin, xmax], [ymin, ymax], …. ,[zmin, zmax]] or [xmin, xmax] when expression is not a list

materialize(virtual_column, inplace=False)[source]

Returns a new DataFrame where the virtual column is turned into an in memory numpy array.

Example:

>>> x = np.arange(1,4)
>>> y = np.arange(2,5)
>>> df = vaex.from_arrays(x=x, y=y)
>>> df['r'] = (df.x**2 + df.y**2)**0.5 # 'r' is a virtual column (computed on the fly)
>>> df = df.materialize('r')  # now 'r' is a 'real' column (i.e. a numpy array)
Parameters

inplace – {inplace}

max(expression, binby=[], limits=None, shape=128, selection=False, delay=False, progress=None, edges=False, array_type=None)[source]

Calculate the maximum for given expressions, possibly on a grid defined by binby.

Example:

>>> df.max("x")
array(271.365997)
>>> df.max(["x", "y"])
array([ 271.365997,  146.465836])
>>> df.max("x", binby="x", shape=5, limits=[-10, 10])
array([-6.00010443, -2.00002384,  1.99998057,  5.99983597,  9.99984646])
Parameters
  • expression – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • array_type – Type of output array, possible values are None/”numpy” (ndarray), “xarray” for a xarray.DataArray, or “list” for a Python list

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic, the last dimension is of shape (2)

mean(expression, binby=[], limits=None, shape=128, selection=False, delay=False, progress=None, edges=False, array_type=None)[source]

Calculate the mean for expression, possibly on a grid defined by binby.

Example:

>>> df.mean("x")
-0.067131491264005971
>>> df.mean("(x**2+y**2)**0.5", binby="E", shape=4)
array([  2.43483742,   4.41840721,   8.26742458,  15.53846476])
Parameters
  • expression – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • array_type – Type of output array, possible values are None/”numpy” (ndarray), “xarray” for a xarray.DataArray, or “list” for a Python list

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic

median_approx(expression, percentage=50.0, binby=[], limits=None, shape=128, percentile_shape=256, percentile_limits='minmax', selection=False, delay=False)[source]

Calculate the median, possibly on a grid defined by binby.

NOTE: this value is approximated by calculating the cumulative distribution on a grid defined by percentile_shape and percentile_limits

Parameters
  • expression – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • percentile_limits – description for the min and max values to use for the cumulative histogram, should currently only be ‘minmax’

  • percentile_shape – shape for the array where the cumulative histogram is calculated on, integer type

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic

min(expression, binby=[], limits=None, shape=128, selection=False, delay=False, progress=None, edges=False, array_type=None)[source]

Calculate the minimum for given expressions, possibly on a grid defined by binby.

Example:

>>> df.min("x")
array(-128.293991)
>>> df.min(["x", "y"])
array([-128.293991 ,  -71.5523682])
>>> df.min("x", binby="x", shape=5, limits=[-10, 10])
array([-9.99919128, -5.99972439, -1.99991322,  2.0000093 ,  6.0004878 ])
Parameters
  • expression – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • array_type – Type of output array, possible values are None/”numpy” (ndarray), “xarray” for a xarray.DataArray, or “list” for a Python list

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic, the last dimension is of shape (2)

minmax(expression, binby=[], limits=None, shape=128, selection=False, delay=False, progress=None)[source]

Calculate the minimum and maximum for expressions, possibly on a grid defined by binby.

Example:

>>> df.minmax("x")
array([-128.293991,  271.365997])
>>> df.minmax(["x", "y"])
array([[-128.293991 ,  271.365997 ],
           [ -71.5523682,  146.465836 ]])
>>> df.minmax("x", binby="x", shape=5, limits=[-10, 10])
array([[-9.99919128, -6.00010443],
           [-5.99972439, -2.00002384],
           [-1.99991322,  1.99998057],
           [ 2.0000093 ,  5.99983597],
           [ 6.0004878 ,  9.99984646]])
Parameters
  • expression – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic, the last dimension is of shape (2)

mode(expression, binby=[], limits=None, shape=256, mode_shape=64, mode_limits=None, progressbar=False, selection=None)[source]

Calculate/estimate the mode.

mutual_information(x, y=None, mi_limits=None, mi_shape=256, binby=[], limits=None, shape=128, sort=False, selection=False, delay=False)[source]

Estimate the mutual information between and x and y on a grid with shape mi_shape and mi_limits, possibly on a grid defined by binby.

If sort is True, the mutual information is returned in sorted (descending) order and the list of expressions is returned in the same order.

Example:

>>> df.mutual_information("x", "y")
array(0.1511814526380327)
>>> df.mutual_information([["x", "y"], ["x", "z"], ["E", "Lz"]])
array([ 0.15118145,  0.18439181,  1.07067379])
>>> df.mutual_information([["x", "y"], ["x", "z"], ["E", "Lz"]], sort=True)
(array([ 1.07067379,  0.18439181,  0.15118145]),
[['E', 'Lz'], ['x', 'z'], ['x', 'y']])
Parameters
  • x – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • y – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • sort – return mutual information in sorted (descending) order, and also return the correspond list of expressions when sorted is True

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic,

property nbytes

Alias for df.byte_size(), see DataFrame.byte_size().

nop(expression=None, progress=False, delay=False)[source]

Evaluates expression, and drop the result, usefull for benchmarking, since vaex is usually lazy

percentile_approx(expression, percentage=50.0, binby=[], limits=None, shape=128, percentile_shape=1024, percentile_limits='minmax', selection=False, delay=False)[source]

Calculate the percentile given by percentage, possibly on a grid defined by binby.

NOTE: this value is approximated by calculating the cumulative distribution on a grid defined by percentile_shape and percentile_limits.

Example:

>>> df.percentile_approx("x", 10), df.percentile_approx("x", 90)
(array([-8.3220355]), array([ 7.92080358]))
>>> df.percentile_approx("x", 50, binby="x", shape=5, limits=[-10, 10])
array([[-7.56462982],
           [-3.61036641],
           [-0.01296306],
           [ 3.56697863],
           [ 7.45838367]])
Parameters
  • expression – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • percentile_limits – description for the min and max values to use for the cumulative histogram, should currently only be ‘minmax’

  • percentile_shape – shape for the array where the cumulative histogram is calculated on, integer type

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic

plot2d_contour(x=None, y=None, what='count(*)', limits=None, shape=256, selection=None, f='identity', figsize=None, xlabel=None, ylabel=None, aspect='auto', levels=None, fill=False, colorbar=False, colorbar_label=None, colormap=None, colors=None, linewidths=None, linestyles=None, vmin=None, vmax=None, grid=None, show=None, **kwargs)

Plot conting contours on 2D grid.

Parameters
  • x – {expression}

  • y – {expression}

  • what – What to plot, count(*) will show a N-d histogram, mean(‘x’), the mean of the x column, sum(‘x’) the sum, std(‘x’) the standard deviation, correlation(‘vx’, ‘vy’) the correlation coefficient. Can also be a list of values, like [‘count(x)’, std(‘vx’)], (by default maps to column)

  • limits – {limits}

  • shape – {shape}

  • selection – {selection}

  • f – transform values by: ‘identity’ does nothing ‘log’ or ‘log10’ will show the log of the value

  • figsize – (x, y) tuple passed to pylab.figure for setting the figure size

  • xlabel – label of the x-axis (defaults to param x)

  • ylabel – label of the y-axis (defaults to param y)

  • aspect – the aspect ratio of the figure

  • levels – the contour levels to be passed on pylab.contour or pylab.contourf

  • colorbar – plot a colorbar or not

  • colorbar_label – the label of the colourbar (defaults to param what)

  • colormap – matplotlib colormap to pass on to pylab.contour or pylab.contourf

  • colors – the colours of the contours

  • linewidths – the widths of the contours

  • linestyles – the style of the contour lines

  • vmin – instead of automatic normalization, scale the data between vmin and vmax

  • vmax – see vmin

  • grid – {grid}

  • show

plot3d(x, y, z, vx=None, vy=None, vz=None, vwhat=None, limits=None, grid=None, what='count(*)', shape=128, selection=[None, True], f=None, vcount_limits=None, smooth_pre=None, smooth_post=None, grid_limits=None, normalize='normalize', colormap='afmhot', figure_key=None, fig=None, lighting=True, level=[0.1, 0.5, 0.9], opacity=[0.01, 0.05, 0.1], level_width=0.1, show=True, **kwargs)[source]

Use at own risk, requires ipyvolume

plot_bq(x, y, grid=None, shape=256, limits=None, what='count(*)', figsize=None, f='identity', figure_key=None, fig=None, axes=None, xlabel=None, ylabel=None, title=None, show=True, selection=[None, True], colormap='afmhot', grid_limits=None, normalize='normalize', grid_before=None, what_kwargs={}, type='default', scales=None, tool_select=False, bq_cleanup=True, **kwargs)[source]

Deprecated: use plot_widget

plot_widget(x, y, limits=None, f='identity', **kwargs)[source]

Deprecated: use df.widget.heatmap

propagate_uncertainties(columns, depending_variables=None, cov_matrix='auto', covariance_format='{}_{}_covariance', uncertainty_format='{}_uncertainty')[source]

Propagates uncertainties (full covariance matrix) for a set of virtual columns.

Covariance matrix of the depending variables is guessed by finding columns prefixed by “e” or “e_” or postfixed by “_error”, “_uncertainty”, “e” and “_e”. Off diagonals (covariance or correlation) by postfixes with “_correlation” or “_corr” for correlation or “_covariance” or “_cov” for covariances. (Note that x_y_cov = x_e * y_e * x_y_correlation.)

Example

>>> df = vaex.from_scalars(x=1, y=2, e_x=0.1, e_y=0.2)
>>> df["u"] = df.x + df.y
>>> df["v"] = np.log10(df.x)
>>> df.propagate_uncertainties([df.u, df.v])
>>> df.u_uncertainty, df.v_uncertainty
Parameters
  • columns – list of columns for which to calculate the covariance matrix.

  • depending_variables – If not given, it is found out automatically, otherwise a list of columns which have uncertainties.

  • cov_matrix – List of list with expressions giving the covariance matrix, in the same order as depending_variables. If ‘full’ or ‘auto’, the covariance matrix for the depending_variables will be guessed, where ‘full’ gives an error if an entry was not found.

remove_virtual_meta()[source]

Removes the file with the virtual column etc, it does not change the current virtual columns etc.

rename(name, new_name, unique=False)[source]

Renames a column or variable, and rewrite expressions such that they refer to the new name

sample(n=None, frac=None, replace=False, weights=None, random_state=None)[source]

Returns a DataFrame with a random set of rows

Note

Note that no copy of the underlying data is made, only a view/reference is made.

Provide either n or frac.

Example:

>>> import vaex, numpy as np
>>> df = vaex.from_arrays(s=np.array(['a', 'b', 'c', 'd']), x=np.arange(1,5))
>>> df
  #  s      x
  0  a      1
  1  b      2
  2  c      3
  3  d      4
>>> df.sample(n=2, random_state=42) # 2 random rows, fixed seed
  #  s      x
  0  b      2
  1  d      4
>>> df.sample(frac=1, random_state=42) # 'shuffling'
  #  s      x
  0  c      3
  1  a      1
  2  d      4
  3  b      2
>>> df.sample(frac=1, replace=True, random_state=42) # useful for bootstrap (may contain repeated samples)
  #  s      x
  0  d      4
  1  a      1
  2  a      1
  3  d      4
Parameters
  • n (int) – number of samples to take (default 1 if frac is None)

  • frac (float) – fractional number of takes to take

  • replace (bool) – If true, a row may be drawn multiple times

  • or expression weights (str) – (unnormalized) probability that a row can be drawn

  • or RandomState (int) – seed or RandomState for reproducability, when None a random seed it chosen

Returns

Returns a new DataFrame with a shallow copy/view of the underlying data

Return type

DataFrame

select(boolean_expression, mode='replace', name='default', executor=None)[source]

Perform a selection, defined by the boolean expression, and combined with the previous selection using the given mode.

Selections are recorded in a history tree, per name, undo/redo can be done for them separately.

Parameters
  • boolean_expression (str) – Any valid column expression, with comparison operators

  • mode (str) – Possible boolean operator: replace/and/or/xor/subtract

  • name (str) – history tree or selection ‘slot’ to use

  • executor

Returns

select_box(spaces, limits, mode='replace', name='default')[source]

Select a n-dimensional rectangular box bounded by limits.

The following examples are equivalent:

>>> df.select_box(['x', 'y'], [(0, 10), (0, 1)])
>>> df.select_rectangle('x', 'y', [(0, 10), (0, 1)])
Parameters
  • spaces – list of expressions

  • limits – sequence of shape [(x1, x2), (y1, y2)]

  • mode

  • name

Returns

select_circle(x, y, xc, yc, r, mode='replace', name='default', inclusive=True)[source]

Select a circular region centred on xc, yc, with a radius of r.

Example:

>>> df.select_circle('x','y',2,3,1)
Parameters
  • x – expression for the x space

  • y – expression for the y space

  • xc – location of the centre of the circle in x

  • yc – location of the centre of the circle in y

  • r – the radius of the circle

  • name – name of the selection

  • mode

Returns

select_ellipse(x, y, xc, yc, width, height, angle=0, mode='replace', name='default', radians=False, inclusive=True)[source]

Select an elliptical region centred on xc, yc, with a certain width, height and angle.

Example:

>>> df.select_ellipse('x','y', 2, -1, 5,1, 30, name='my_ellipse')
Parameters
  • x – expression for the x space

  • y – expression for the y space

  • xc – location of the centre of the ellipse in x

  • yc – location of the centre of the ellipse in y

  • width – the width of the ellipse (diameter)

  • height – the width of the ellipse (diameter)

  • angle – (degrees) orientation of the ellipse, counter-clockwise measured from the y axis

  • name – name of the selection

  • mode

Returns

select_inverse(name='default', executor=None)[source]

Invert the selection, i.e. what is selected will not be, and vice versa

Parameters
  • name (str) –

  • executor

Returns

select_lasso(expression_x, expression_y, xsequence, ysequence, mode='replace', name='default', executor=None)[source]

For performance reasons, a lasso selection is handled differently.

Parameters
  • expression_x (str) – Name/expression for the x coordinate

  • expression_y (str) – Name/expression for the y coordinate

  • xsequence – list of x numbers defining the lasso, together with y

  • ysequence

  • mode (str) – Possible boolean operator: replace/and/or/xor/subtract

  • name (str) –

  • executor

Returns

select_non_missing(drop_nan=True, drop_masked=True, column_names=None, mode='replace', name='default')[source]

Create a selection that selects rows having non missing values for all columns in column_names.

The name reflects Pandas, no rows are really dropped, but a mask is kept to keep track of the selection

Parameters
  • drop_nan – drop rows when there is a NaN in any of the columns (will only affect float values)

  • drop_masked – drop rows when there is a masked value in any of the columns

  • column_names – The columns to consider, default: all (real, non-virtual) columns

  • mode (str) – Possible boolean operator: replace/and/or/xor/subtract

  • name (str) – history tree or selection ‘slot’ to use

Returns

select_nothing(name='default')[source]

Select nothing.

select_rectangle(x, y, limits, mode='replace', name='default')[source]

Select a 2d rectangular box in the space given by x and y, bounded by limits.

Example:

>>> df.select_box('x', 'y', [(0, 10), (0, 1)])
Parameters
  • x – expression for the x space

  • y – expression fo the y space

  • limits – sequence of shape [(x1, x2), (y1, y2)]

  • mode

selected_length()[source]

Returns the number of rows that are selected.

selection_can_redo(name='default')[source]

Can selection name be redone?

selection_can_undo(name='default')[source]

Can selection name be undone?

selection_redo(name='default', executor=None)[source]

Redo selection, for the name.

selection_undo(name='default', executor=None)[source]

Undo selection, for the name.

set_active_fraction(value)[source]

Sets the active_fraction, set picked row to None, and remove selection.

TODO: we may be able to keep the selection, if we keep the expression, and also the picked row

set_active_range(i1, i2)[source]

Sets the active_fraction, set picked row to None, and remove selection.

TODO: we may be able to keep the selection, if we keep the expression, and also the picked row

set_current_row(value)[source]

Set the current row, and emit the signal signal_pick.

set_selection(selection, name='default', executor=None)[source]

Sets the selection object

Parameters
  • selection – Selection object

  • name – selection ‘slot’

  • executor

Returns

set_variable(name, expression_or_value, write=True)[source]

Set the variable to an expression or value defined by expression_or_value.

Example

>>> df.set_variable("a", 2.)
>>> df.set_variable("b", "a**2")
>>> df.get_variable("b")
'a**2'
>>> df.evaluate_variable("b")
4.0
Parameters
  • name – Name of the variable

  • write – write variable to meta file

  • expression – value or expression

shuffle()[source]

Shuffle order of rows (equivalent to df.sample(frac=1))

sort(by, ascending=True, kind='quicksort')[source]

Return a sorted DataFrame, sorted by the expression ‘by’

The kind keyword is ignored if doing multi-key sorting.

Note

Note that no copy of the underlying data is made, only a view/reference is made.

Note

Note that filtering will be ignored (since they may change), you may want to consider running extract() first.

Example:

>>> import vaex, numpy as np
>>> df = vaex.from_arrays(s=np.array(['a', 'b', 'c', 'd']), x=np.arange(1,5))
>>> df['y'] = (df.x-1.8)**2
>>> df
  #  s      x     y
  0  a      1  0.64
  1  b      2  0.04
  2  c      3  1.44
  3  d      4  4.84
>>> df.sort('y', ascending=False)  # Note: passing '(x-1.8)**2' gives the same result
  #  s      x     y
  0  d      4  4.84
  1  c      3  1.44
  2  a      1  0.64
  3  b      2  0.04
Parameters
  • or expression by (str) – expression to sort by

  • ascending (bool) – ascending (default, True) or descending (False)

  • kind (str) – kind of algorithm to use (passed to numpy.argsort)

split(into=None)[source]

Returns a list containing ordered subsets of the DataFrame.

Note

Note that no copy of the underlying data is made, only a view/reference is made.

Example:

>>> import vaex
>>> df = vaex.from_arrays(x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> for dfs in df.split(into=0.3):
...     print(dfs.x.values)
...
[0 1 3]
[3 4 5 6 7 8 9]
>>> for split in df.split(into=[0.2, 0.3, 0.5]):
...     print(dfs.x.values)
[0 1]
[2 3 4]
[5 6 7 8 9]
Parameters

into (int/float/list) – If float will split the DataFrame in two, the first of which will have a relative length as specified by this parameter. When a list, will split into as many portions as elements in the list, where each element defines the relative length of that portion. Note that such a list of fractions will always be re-normalized to 1. When an int, split DataFrame into n dataframes of equal length (last one may deviate), if len(df) < n, it will return len(df) DataFrames.

split_random(into, random_state=None)[source]

Returns a list containing random portions of the DataFrame.

Note

Note that no copy of the underlying data is made, only a view/reference is made.

Example:

>>> import vaex, import numpy as np
>>> np.random.seed(111)
>>> df = vaex.from_arrays(x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> for dfs in df.split_random(into=0.3, random_state=42):
...     print(dfs.x.values)
...
[8 1 5]
[0 7 2 9 4 3 6]
>>> for split in df.split_random(into=[0.2, 0.3, 0.5], random_state=42):
...     print(dfs.x.values)
[8 1]
[5 0 7]
[2 9 4 3 6]
Parameters
  • into (int/float/list) – If float will split the DataFrame in two, the first of which will have a relative length as specified by this parameter. When a list, will split into as many portions as elements in the list, where each element defines the relative length of that portion. Note that such a list of fractions will always be re-normalized to 1. When an int, split DataFrame into n dataframes of equal length (last one may deviate), if len(df) < n, it will return len(df) DataFrames.

  • random_state (int) – (default, None) Random number seed for reproducibility.

Returns

A list of DataFrames.

Return type

list

state_get()[source]

Return the internal state of the DataFrame in a dictionary

Example:

>>> import vaex
>>> df = vaex.from_scalars(x=1, y=2)
>>> df['r'] = (df.x**2 + df.y**2)**0.5
>>> df.state_get()
{'active_range': [0, 1],
'column_names': ['x', 'y', 'r'],
'description': None,
'descriptions': {},
'functions': {},
'renamed_columns': [],
'selections': {'__filter__': None},
'ucds': {},
'units': {},
'variables': {},
'virtual_columns': {'r': '(((x ** 2) + (y ** 2)) ** 0.5)'}}
state_load(file, use_active_range=False, fs_options=None)[source]

Load a state previously stored by DataFrame.state_write(), see also DataFrame.state_set().

Parameters
  • file (str) – filename (ending in .json or .yaml)

  • fs_options (dict) – arguments to pass the the file system handler (s3fs or gcsfs)

state_set(state, use_active_range=False, trusted=True)[source]

Sets the internal state of the df

Example:

>>> import vaex
>>> df = vaex.from_scalars(x=1, y=2)
>>> df
  #    x    y        r
  0    1    2  2.23607
>>> df['r'] = (df.x**2 + df.y**2)**0.5
>>> state = df.state_get()
>>> state
{'active_range': [0, 1],
'column_names': ['x', 'y', 'r'],
'description': None,
'descriptions': {},
'functions': {},
'renamed_columns': [],
'selections': {'__filter__': None},
'ucds': {},
'units': {},
'variables': {},
'virtual_columns': {'r': '(((x ** 2) + (y ** 2)) ** 0.5)'}}
>>> df2 = vaex.from_scalars(x=3, y=4)
>>> df2.state_set(state)  # now the virtual functions are 'copied'
>>> df2
  #    x    y    r
  0    3    4    5
Parameters
state_write(file, fs_options=None)[source]

Write the internal state to a json or yaml file (see DataFrame.state_get())

Example

>>> import vaex
>>> df = vaex.from_scalars(x=1, y=2)
>>> df['r'] = (df.x**2 + df.y**2)**0.5
>>> df.state_write('state.json')
>>> print(open('state.json').read())
{
"virtual_columns": {
    "r": "(((x ** 2) + (y ** 2)) ** 0.5)"
},
"column_names": [
    "x",
    "y",
    "r"
],
"renamed_columns": [],
"variables": {
    "pi": 3.141592653589793,
    "e": 2.718281828459045,
    "km_in_au": 149597870.7,
    "seconds_per_year": 31557600
},
"functions": {},
"selections": {
    "__filter__": null
},
"ucds": {},
"units": {},
"descriptions": {},
"description": null,
"active_range": [
    0,
    1
]
}
>>> df.state_write('state.yaml')
>>> print(open('state.yaml').read())
active_range:
- 0
- 1
column_names:
- x
- y
- r
description: null
descriptions: {}
functions: {}
renamed_columns: []
selections:
__filter__: null
ucds: {}
units: {}
variables:
pi: 3.141592653589793
e: 2.718281828459045
km_in_au: 149597870.7
seconds_per_year: 31557600
virtual_columns:
r: (((x ** 2) + (y ** 2)) ** 0.5)
Parameters
  • file (str) – filename (ending in .json or .yaml)

  • fs_options (dict) – arguments to pass the the file system handler (s3fs or gcsfs)

std(expression, binby=[], limits=None, shape=128, selection=False, delay=False, progress=None, array_type=None)[source]

Calculate the standard deviation for the given expression, possible on a grid defined by binby

>>> df.std("vz")
110.31773397535071
>>> df.std("vz", binby=["(x**2+y**2)**0.5"], shape=4)
array([ 123.57954851,   85.35190177,   61.14345748,   38.0740619 ])
Parameters
  • expression – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • array_type – Type of output array, possible values are None/”numpy” (ndarray), “xarray” for a xarray.DataArray, or “list” for a Python list

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic

sum(expression, binby=[], limits=None, shape=128, selection=False, delay=False, progress=None, edges=False, array_type=None)[source]

Calculate the sum for the given expression, possible on a grid defined by binby

Example:

>>> df.sum("L")
304054882.49378014
>>> df.sum("L", binby="E", shape=4)
array([  8.83517994e+06,   5.92217598e+07,   9.55218726e+07,
                 1.40008776e+08])
Parameters
  • expression – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • array_type – Type of output array, possible values are None/”numpy” (ndarray), “xarray” for a xarray.DataArray, or “list” for a Python list

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic

tail(n=10)[source]

Return a shallow copy a DataFrame with the last n rows.

take(indices, filtered=True, dropfilter=True)[source]

Returns a DataFrame containing only rows indexed by indices

Note

Note that no copy of the underlying data is made, only a view/reference is made.

Example:

>>> import vaex, numpy as np
>>> df = vaex.from_arrays(s=np.array(['a', 'b', 'c', 'd']), x=np.arange(1,5))
>>> df.take([0,2])
 #  s      x
 0  a      1
 1  c      3
Parameters
  • indices – sequence (list or numpy array) with row numbers

  • filtered – (for internal use) The indices refer to the filtered data.

  • dropfilter – (for internal use) Drop the filter, set to False when indices refer to unfiltered, but may contain rows that still need to be filtered out.

Returns

DataFrame which is a shallow copy of the original data.

Return type

DataFrame

to_arrays(column_names=None, selection=None, strings=True, virtual=True, parallel=True, chunk_size=None, array_type=None)[source]

Return a list of ndarrays

Parameters
  • column_names – list of column names, to export, when None DataFrame.get_column_names(strings=strings, virtual=virtual) is used

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • strings – argument passed to DataFrame.get_column_names when column_names is None

  • virtual – argument passed to DataFrame.get_column_names when column_names is None

  • parallel – Evaluate the (virtual) columns in parallel

  • chunk_size – Return an iterator with cuts of the object in lenght of this size

  • array_type – Type of output array, possible values are None/”numpy” (ndarray), “xarray” for a xarray.DataArray, or “list” for a Python list

Returns

list of arrays

to_arrow_table(column_names=None, selection=None, strings=True, virtual=True, parallel=True, chunk_size=None, reduce_large=False)[source]

Returns an arrow Table object containing the arrays corresponding to the evaluated data

Parameters
  • column_names – list of column names, to export, when None DataFrame.get_column_names(strings=strings, virtual=virtual) is used

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • strings – argument passed to DataFrame.get_column_names when column_names is None

  • virtual – argument passed to DataFrame.get_column_names when column_names is None

  • parallel – Evaluate the (virtual) columns in parallel

  • chunk_size – Return an iterator with cuts of the object in lenght of this size

  • reduce_large (bool) – If possible, cast large_string to normal string

Returns

pyarrow.Table object or iterator of

to_astropy_table(column_names=None, selection=None, strings=True, virtual=True, index=None, parallel=True)[source]

Returns a astropy table object containing the ndarrays corresponding to the evaluated data

Parameters
  • column_names – list of column names, to export, when None DataFrame.get_column_names(strings=strings, virtual=virtual) is used

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • strings – argument passed to DataFrame.get_column_names when column_names is None

  • virtual – argument passed to DataFrame.get_column_names when column_names is None

  • index – if this column is given it is used for the index of the DataFrame

Returns

astropy.table.Table object

to_copy(column_names=None, selection=None, strings=True, virtual=True, selections=True)[source]

Return a copy of the DataFrame, if selection is None, it does not copy the data, it just has a reference

Parameters
  • column_names – list of column names, to copy, when None DataFrame.get_column_names(strings=strings, virtual=virtual) is used

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • strings – argument passed to DataFrame.get_column_names when column_names is None

  • virtual – argument passed to DataFrame.get_column_names when column_names is None

  • selections – copy selections to a new DataFrame

Returns

dict

to_dask_array(chunks='auto')[source]

Lazily expose the DataFrame as a dask.array

Example

>>> df = vaex.example()
>>> A = df[['x', 'y', 'z']].to_dask_array()
>>> A
dask.array<vaex-df-1f048b40-10ec-11ea-9553, shape=(330000, 3), dtype=float64, chunksize=(330000, 3), chunktype=numpy.ndarray>
>>> A+1
dask.array<add, shape=(330000, 3), dtype=float64, chunksize=(330000, 3), chunktype=numpy.ndarray>
Parameters

chunks – How to chunk the array, similar to dask.array.from_array().

Returns

dask.array.Array object.

to_dict(column_names=None, selection=None, strings=True, virtual=True, parallel=True, chunk_size=None, array_type=None)[source]

Return a dict containing the ndarray corresponding to the evaluated data

Parameters
  • column_names – list of column names, to export, when None DataFrame.get_column_names(strings=strings, virtual=virtual) is used

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • strings – argument passed to DataFrame.get_column_names when column_names is None

  • virtual – argument passed to DataFrame.get_column_names when column_names is None

  • parallel – Evaluate the (virtual) columns in parallel

  • chunk_size – Return an iterator with cuts of the object in lenght of this size

  • array_type – Type of output array, possible values are None/”numpy” (ndarray), “xarray” for a xarray.DataArray, or “list” for a Python list

Returns

dict

to_items(column_names=None, selection=None, strings=True, virtual=True, parallel=True, chunk_size=None, array_type=None)[source]

Return a list of [(column_name, ndarray), …)] pairs where the ndarray corresponds to the evaluated data

Parameters
  • column_names – list of column names, to export, when None DataFrame.get_column_names(strings=strings, virtual=virtual) is used

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • strings – argument passed to DataFrame.get_column_names when column_names is None

  • virtual – argument passed to DataFrame.get_column_names when column_names is None

  • parallel – Evaluate the (virtual) columns in parallel

  • chunk_size – Return an iterator with cuts of the object in lenght of this size

  • array_type – Type of output array, possible values are None/”numpy” (ndarray), “xarray” for a xarray.DataArray, or “list” for a Python list

Returns

list of (name, ndarray) pairs or iterator of

to_pandas_df(column_names=None, selection=None, strings=True, virtual=True, index_name=None, parallel=True, chunk_size=None)[source]

Return a pandas DataFrame containing the ndarray corresponding to the evaluated data

If index is given, that column is used for the index of the dataframe.

Example

>>> df_pandas = df.to_pandas_df(["x", "y", "z"])
>>> df_copy = vaex.from_pandas(df_pandas)
Parameters
  • column_names – list of column names, to export, when None DataFrame.get_column_names(strings=strings, virtual=virtual) is used

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • strings – argument passed to DataFrame.get_column_names when column_names is None

  • virtual – argument passed to DataFrame.get_column_names when column_names is None

  • index_column – if this column is given it is used for the index of the DataFrame

  • parallel – Evaluate the (virtual) columns in parallel

  • chunk_size – Return an iterator with cuts of the object in lenght of this size

Returns

pandas.DataFrame object or iterator of

trim(inplace=False)[source]

Return a DataFrame, where all columns are ‘trimmed’ by the active range.

For the returned DataFrame, df.get_active_range() returns (0, df.length_original()).

Note

Note that no copy of the underlying data is made, only a view/reference is made.

Parameters

inplace – Make modifications to self or return a new DataFrame

Return type

DataFrame

ucd_find(ucds, exclude=[])[source]

Find a set of columns (names) which have the ucd, or part of the ucd.

Prefixed with a ^, it will only match the first part of the ucd.

Example

>>> df.ucd_find('pos.eq.ra', 'pos.eq.dec')
['RA', 'DEC']
>>> df.ucd_find('pos.eq.ra', 'doesnotexist')
>>> df.ucds[df.ucd_find('pos.eq.ra')]
'pos.eq.ra;meta.main'
>>> df.ucd_find('meta.main')]
'dec'
>>> df.ucd_find('^meta.main')]
unit(expression, default=None)[source]

Returns the unit (an astropy.unit.Units object) for the expression.

Example

>>> import vaex
>>> ds = vaex.example()
>>> df.unit("x")
Unit("kpc")
>>> df.unit("x*L")
Unit("km kpc2 / s")
Parameters
  • expression – Expression, which can be a column name

  • default – if no unit is known, it will return this

Returns

The resulting unit of the expression

Return type

astropy.units.Unit

validate_expression(expression)[source]

Validate an expression (may throw Exceptions)

var(expression, binby=[], limits=None, shape=128, selection=False, delay=False, progress=None, array_type=None)[source]

Calculate the sample variance for the given expression, possible on a grid defined by binby

Example:

>>> df.var("vz")
12170.002429456246
>>> df.var("vz", binby=["(x**2+y**2)**0.5"], shape=4)
array([ 15271.90481083,   7284.94713504,   3738.52239232,   1449.63418988])
>>> df.var("vz", binby=["(x**2+y**2)**0.5"], shape=4)**0.5
array([ 123.57954851,   85.35190177,   61.14345748,   38.0740619 ])
>>> df.std("vz", binby=["(x**2+y**2)**0.5"], shape=4)
array([ 123.57954851,   85.35190177,   61.14345748,   38.0740619 ])
Parameters
  • expression – expression or list of expressions, e.g. df.x, ‘x’, or [‘x, ‘y’]

  • binby – List of expressions for constructing a binned grid

  • limits – description for the min and max values for the expressions, e.g. ‘minmax’ (default), ‘99.7%’, [0, 10], or a list of, e.g. [[0, 10], [0, 20], ‘minmax’]

  • shape – shape for the array where the statistic is calculated on, if only an integer is given, it is used for all dimensions, e.g. shape=128, shape=[128, 256]

  • selection – Name of selection to use (or True for the ‘default’), or all the data (when selection is None or False), or a list of selections

  • delay – Do not return the result, but a proxy for delayhronous calculations (currently only for internal use)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • array_type – Type of output array, possible values are None/”numpy” (ndarray), “xarray” for a xarray.DataArray, or “list” for a Python list

Returns

Numpy array with the given shape, or a scalar when no binby argument is given, with the statistic

DataFrameLocal class

class vaex.dataframe.DataFrameLocal(dataset=None)[source]

Bases: vaex.dataframe.DataFrame

Base class for DataFrames that work with local file/data

__array__(dtype=None, parallel=True)[source]

Gives a full memory copy of the DataFrame into a 2d numpy array of shape (n_rows, n_columns). Note that the memory order is fortran, so all values of 1 column are contiguous in memory for performance reasons.

Note this returns the same result as:

>>> np.array(ds)

If any of the columns contain masked arrays, the masks are ignored (i.e. the masked elements are returned as well).

__call__(*expressions, **kwargs)[source]

The local implementation of DataFrame.__call__()

__init__(dataset=None)[source]

Initialize self. See help(type(self)) for accurate signature.

as_arrow()[source]

Lazily cast all columns to arrow, except object types.

as_numpy(strict=False)[source]

Lazily cast all numerical columns to numpy.

If strict is True, it will also cast non-numerical types.

binby(by=None, agg=None)[source]

Return a BinBy or DataArray object when agg is not None

The binby operation does not return a ‘flat’ DataFrame, instead it returns an N-d grid in the form of an xarray.

Parameters

list or agg agg (dict,) – Aggregate operation in the form of a string, vaex.agg object, a dictionary where the keys indicate the target column names, and the values the operations, or the a list of aggregates. When not given, it will return the binby object.

Returns

DataArray or BinBy object.

categorize(column, min_value=0, max_value=None, labels=None, inplace=False)[source]

Mark column as categorical.

This may help speed up calculations using integer columns between a range of [min_value, max_value].

If max_value is not given, the [min_value and max_value] are calcuated from the data.

Example:

>>> import vaex
>>> df = vaex.from_arrays(year=[2012, 2015, 2019], weekday=[0, 4, 6])
>>> df.categorize('year', min_value=2020, max_value=2019)
>>> df.categorize('weekday', labels=['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'])
Parameters
  • column – column to assume is categorical.

  • labels – labels to associate to the values between min_value and max_value

  • min_value – minimum integer value (if max_value is not given, this is calculated)

  • max_value – maximum integer value (if max_value is not given, this is calculated)

  • labels – Labels to associate to each value, list(range(min_value, max_value+1)) by default

  • inplace – Make modifications to self or return a new DataFrame

compare(other, report_missing=True, report_difference=False, show=10, orderby=None, column_names=None)[source]

Compare two DataFrames and report their difference, use with care for large DataFrames

concat(*others, resolver='flexible')vaex.dataframe.DataFrame[source]

Concatenates multiple DataFrames, adding the rows of the other DataFrame to the current, returned in a new DataFrame.

In the case of resolver=’flexible’, when not all columns has the same names, the missing data is filled with missing values.

In the case of resolver=’strict’ all datasets need to have matching column names.

Parameters
  • others – The other DataFrames that are concatenated with this DataFrame

  • resolver (str) – How to resolve schema conflicts, ‘flexible’ or ‘strict’.

Returns

New DataFrame with the rows concatenated

property data

Gives direct access to the data as numpy arrays.

Convenient when working with IPython in combination with small DataFrames, since this gives tab-completion. Only real columns (i.e. no virtual) columns can be accessed, for getting the data from virtual columns, use DataFrame.evaluate(…).

Columns can be accessed by their names, which are attributes. The attributes are of type numpy.ndarray.

Example:

>>> df = vaex.example()
>>> r = np.sqrt(df.data.x**2 + df.data.y**2)
export(path, progress=None, chunk_size=1048576, parallel=True, fs_options=None)[source]

Exports the DataFrame to a file depending on the file extension.

E.g if the filename ends on .hdf5, df.export_hdf5 is called.

Parameters
  • path (str) – path for file

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • chunk_size (int) – Number of rows to be written to disk in a single iteration, if supported.

  • parallel (bool) – Evaluate the (virtual) columns in parallel

  • fs_options (dict) – see vaex.open() e.g. for S3 {“profile”: “myproject”}

Returns

export_arrow(to, progress=None, chunk_size=1048576, parallel=True, reduce_large=True, fs_options=None, as_stream=True)[source]

Exports the DataFrame to a file of stream written with arrow

Parameters
  • to – filename, file object, or pyarrow.RecordBatchStreamWriter, py:data:pyarrow.RecordBatchFileWriter or pyarrow.parquet.ParquetWriter

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • chunk_size (int) – Number of rows to be written to disk in a single iteration

  • parallel (bool) – Evaluate the (virtual) columns in parallel

  • reduce_large (bool) – If True, convert arrow large_string type to string type

  • as_stream (bool) – Write as an Arrow stream if true, else a file. see also https://arrow.apache.org/docs/format/Columnar.html?highlight=arrow1#ipc-file-format

  • fs_options (dict) – see vaex.open() e.g. for S3 {“profile”: “myproject”}

Returns

export_csv(path, progress=None, chunk_size=1048576, parallel=True, **kwargs)[source]

Exports the DataFrame to a CSV file.

Parameters
  • path (str) – Path for file

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • chunk_size (int) – Number of rows to be written to disk in a single iteration

  • parallel – Evaluate the (virtual) columns in parallel

  • **kwargs

    Extra keyword arguments to be passed on pandas.DataFrame.to_csv()

Returns

export_fits(path, progress=None)[source]

Exports the DataFrame to a fits file that is compatible with TOPCAT colfits format

Parameters
  • path (str) – path for file

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

Returns

export_hdf5(path, byteorder='=', progress=None, parallel=True)[source]

Exports the DataFrame to a vaex hdf5 file

Parameters
  • path (str) – path for file

  • byteorder (str) – = for native, < for little endian and > for big endian

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • parallel (bool) – Evaluate the (virtual) columns in parallel

Returns

export_many(path, progress=None, chunk_size=1048576, parallel=True, max_workers=None, fs_options=None)[source]

Export the DataFrame to multiple files of the same type in parallel.

The path will be formatted using the i parameter (which is the chunk index).

Example:

>>> import vaex
>>> df = vaex.open('my_big_dataset.hdf5')
>>> print(f'number of rows: {len(df):,}')
number of rows: 193,938,982
>>> df.export_many(path='my/destination/folder/chunk-{i:03}.arrow')
>>> df_single_chunk = vaex.open('my/destination/folder/chunk-00001.arrow')
>>> print(f'number of rows: {len(df_single_chunk):,}')
number of rows: 1,048,576
>>> df_all_chunks = vaex.open('my/destination/folder/chunk-*.arrow')
>>> print(f'number of rows: {len(df_all_chunks):,}')
number of rows: 193,938,982
Parameters
  • path (str) – Path for file, formatted by chunk index i (e.g. ‘chunk-{i:05}.parquet’)

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • chunk_size (int) – Number of rows to be written to disk in a single iteration

  • parallel (bool) – Evaluate the (virtual) columns in parallel

  • max_workers (int) – Number of workers/threads to use for writing in parallel

  • fs_options (dict) – see vaex.open() e.g. for S3 {“profile”: “myproject”}

export_parquet(path, progress=None, chunk_size=1048576, parallel=True, fs_options=None, **kwargs)[source]

Exports the DataFrame to a parquet file.

Note: This may require that all of the data fits into memory (memory mapped data is an exception).

Use :py:`DataFrame.export_chunks` to write to multiple files in parallel.

Parameters
  • path (str) – path for file

  • progress – A callable that takes one argument (a floating point value between 0 and 1) indicating the progress, calculations are cancelled when this callable returns False

  • chunk_size (int) – Number of rows to be written to disk in a single iteration

  • parallel (bool) – Evaluate the (virtual) columns in parallel

  • fs_options (dict) – see vaex.open() e.g. for S3 {“profile”: “myproject”}

  • **kwargs

    Extra keyword arguments to be passed on to py:data:pyarrow.parquet.ParquetWriter.

Returns

groupby(by=None, agg=None)[source]

Return a GroupBy or DataFrame object when agg is not None

Examples:

>>> import vaex
>>> import numpy as np
>>> np.random.seed(42)
>>> x = np.random.randint(1, 5, 10)
>>> y = x**2
>>> df = vaex.from_arrays(x=x, y=y)
>>> df.groupby(df.x, agg='count')
#    x    y_count
0    3          4
1    4          2
2    1          3
3    2          1
>>> df.groupby(df.x, agg=[vaex.agg.count('y'), vaex.agg.mean('y')])
#    x    y_count    y_mean
0    3          4         9
1    4          2        16
2    1          3         1
3    2          1         4
>>> df.groupby(df.x, agg={'z': [vaex.agg.count('y'), vaex.agg.mean('y')]})
#    x    z_count    z_mean
0    3          4         9
1    4          2        16
2    1          3         1
3    2          1         4

Example using datetime:

>>> import vaex
>>> import numpy as np
>>> t = np.arange('2015-01-01', '2015-02-01', dtype=np.datetime64)
>>> y = np.arange(len(t))
>>> df = vaex.from_arrays(t=t, y=y)
>>> df.groupby(vaex.BinnerTime.per_week(df.t)).agg({'y' : 'sum'})
#  t                      y
0  2015-01-01 00:00:00   21
1  2015-01-08 00:00:00   70
2  2015-01-15 00:00:00  119
3  2015-01-22 00:00:00  168
4  2015-01-29 00:00:00   87
Parameters

list or agg agg (dict,) – Aggregate operation in the form of a string, vaex.agg object, a dictionary where the keys indicate the target column names, and the values the operations, or the a list of aggregates. When not given, it will return the groupby object.

Returns

DataFrame or GroupBy object.

hashed()vaex.dataframe.DataFrame[source]

Return a DataFrame with a hashed dataset

is_local()[source]

The local implementation of DataFrame.evaluate(), always returns True.

join(other, on=None, left_on=None, right_on=None, lprefix='', rprefix='', lsuffix='', rsuffix='', how='left', allow_duplication=False, prime_growth=False, cardinality_other=None, inplace=False)[source]

Return a DataFrame joined with other DataFrames, matched by columns/expression on/left_on/right_on

If neither on/left_on/right_on is given, the join is done by simply adding the columns (i.e. on the implicit row index).

Note: The filters will be ignored when joining, the full DataFrame will be joined (since filters may change). If either DataFrame is heavily filtered (contains just a small number of rows) consider running DataFrame.extract() first.

Example:

>>> a = np.array(['a', 'b', 'c'])
>>> x = np.arange(1,4)
>>> ds1 = vaex.from_arrays(a=a, x=x)
>>> b = np.array(['a', 'b', 'd'])
>>> y = x**2
>>> ds2 = vaex.from_arrays(b=b, y=y)
>>> ds1.join(ds2, left_on='a', right_on='b')
Parameters
  • other – Other DataFrame to join with (the right side)

  • on – default key for the left table (self)

  • left_on – key for the left table (self), overrides on

  • right_on – default key for the right table (other), overrides on

  • lprefix – prefix to add to the left column names in case of a name collision

  • rprefix – similar for the right

  • lsuffix – suffix to add to the left column names in case of a name collision

  • rsuffix – similar for the right

  • how – how to join, ‘left’ keeps all rows on the left, and adds columns (with possible missing values) ‘right’ is similar with self and other swapped. ‘inner’ will only return rows which overlap.

  • allow_duplication (bool) – Allow duplication of rows when the joined column contains non-unique values.

  • cardinality_other (int) – Number of unique elements (or estimate of) for the other table.

  • prime_growth (bool) – Growth strategy for the hashmaps used internally, can improve performance in some case (e.g. integers with low bits unused).

  • inplace – Make modifications to self or return a new DataFrame

Returns

label_encode(column, values=None, inplace=False)

Deprecated: use is_category

Encode column as ordinal values and mark it as categorical.

The existing column is renamed to a hidden column and replaced by a numerical columns with values between [0, len(values)-1].

length(selection=False)[source]

Get the length of the DataFrames, for the selection of the whole DataFrame.

If selection is False, it returns len(df).

TODO: Implement this in DataFrameRemote, and move the method up in DataFrame.length()

Parameters

selection – When True, will return the number of selected rows

Returns

ordinal_encode(column, values=None, inplace=False)[source]

Deprecated: use is_category

Encode column as ordinal values and mark it as categorical.

The existing column is renamed to a hidden column and replaced by a numerical columns with values between [0, len(values)-1].

selected_length(selection='default')[source]

The local implementation of DataFrame.selected_length()

shallow_copy(virtual=True, variables=True)[source]

Creates a (shallow) copy of the DataFrame.

It will link to the same data, but will have its own state, e.g. virtual columns, variables, selection etc.

property values

Gives a full memory copy of the DataFrame into a 2d numpy array of shape (n_rows, n_columns). Note that the memory order is fortran, so all values of 1 column are contiguous in memory for performance reasons.

Note this returns the same result as:

>>> np.array(ds)

If any of the columns contain masked arrays, the masks are ignored (i.e. the masked elements are returned as well).

Expression class

class vaex.expression.Expression(ds, expression, ast=None)[source]

Bases: object

Expression class

__abs__()[source]

Returns the absolute value of the expression

__bool__()[source]

Cast expression to boolean. Only supports (<expr1> == <expr2> and <expr1> != <expr2>)

The main use case for this is to support assigning to traitlets. e.g.:

>>> bool(expr1 == expr2)

This will return True when expr1 and expr2 are exactly the same (in string representation). And similarly for:

>>> bool(expr != expr2)

All other cases will return True.

__init__(ds, expression, ast=None)[source]

Initialize self. See help(type(self)) for accurate signature.

__repr__()[source]

Return repr(self).

__str__()[source]

Return str(self).

__weakref__

list of weak references to the object (if defined)

abs(**kwargs)

Lazy wrapper around numpy.abs

apply(f, vectorize=False, multiprocessing=True)[source]

Apply a function along all values of an Expression.

Shorthand for df.apply(f, arguments=[expression]), see DataFrame.apply()

Example:

>>> df = vaex.example()
>>> df.x
Expression = x
Length: 330,000 dtype: float64 (column)
---------------------------------------
     0  -0.777471
     1    3.77427
     2    1.37576
     3   -7.06738
     4   0.243441
>>> def func(x):
...     return x**2
>>> df.x.apply(func)
Expression = lambda_function(x)
Length: 330,000 dtype: float64 (expression)
-------------------------------------------
     0   0.604461
     1    14.2451
     2    1.89272
     3    49.9478
     4  0.0592637
Parameters
  • f – A function to be applied on the Expression values

  • vectorize – Call f with arrays instead of a scalars (for better performance).

  • multiprocessing (bool) – Use multiple processes to avoid the GIL (Global interpreter lock).

Returns

A function that is lazily evaluated when called.

arccos(**kwargs)

Lazy wrapper around numpy.arccos

arccosh(**kwargs)

Lazy wrapper around numpy.arccosh

arcsin(**kwargs)

Lazy wrapper around numpy.arcsin

arcsinh(**kwargs)

Lazy wrapper around numpy.arcsinh

arctan(**kwargs)

Lazy wrapper around numpy.arctan

arctan2(**kwargs)

Lazy wrapper around numpy.arctan2

arctanh(**kwargs)

Lazy wrapper around numpy.arctanh

as_arrow()

Lazily convert to Apache Arrow array type

as_numpy(strict=False)

Lazily convert to NumPy ndarray type

property ast

Returns the abstract syntax tree (AST) of the expression

clip(**kwargs)

Lazy wrapper around numpy.clip

copy(df=None)[source]

Efficiently copies an expression.

Expression objects have both a string and AST representation. Creating the AST representation involves parsing the expression, which is expensive.

Using copy will deepcopy the AST when the expression was already parsed.

Parameters

df – DataFrame for which the expression will be evaluated (self.df if None)

cos(**kwargs)

Lazy wrapper around numpy.cos

cosh(**kwargs)

Lazy wrapper around numpy.cosh

count(binby=[], limits=None, shape=128, selection=False, delay=False, edges=False, progress=None)[source]

Shortcut for ds.count(expression, …), see Dataset.count

countmissing()[source]

Returns the number of missing values in the expression.

countna()[source]

Returns the number of Not Availiable (N/A) values in the expression. This includes missing values and np.nan values.

countnan()[source]

Returns the number of NaN values in the expression.

deg2rad(**kwargs)

Lazy wrapper around numpy.deg2rad

digitize(**kwargs)

Lazy wrapper around numpy.digitize

property dt

Gives access to datetime operations via DateTime

exp(**kwargs)

Lazy wrapper around numpy.exp

expand(stop=[])[source]

Expand the expression such that no virtual columns occurs, only normal columns.

Example:

>>> df = vaex.example()
>>> r = np.sqrt(df.data.x**2 + df.data.y**2)
>>> r.expand().expression
'sqrt(((x ** 2) + (y ** 2)))'
expm1(**kwargs)

Lazy wrapper around numpy.expm1

fillmissing(value)

Returns an array where missing values are replaced by value. See :ismissing for the definition of missing values.

fillna(value)

Returns an array where NA values are replaced by value. See :isna for the definition of missing values.

fillnan(value)

Returns an array where nan values are replaced by value. See :isnan for the definition of missing values.

format(format)

Uses http://www.cplusplus.com/reference/string/to_string/ for formatting

isfinite(**kwargs)

Lazy wrapper around numpy.isfinite

isin(values, use_hashmap=True)[source]

Lazily tests if each value in the expression is present in values.

Parameters
  • values – List/array of values to check

  • use_hashmap – use a hashmap or not (especially faster when values contains many elements)

Returns

Expression with the lazy expression.

isinf(**kwargs)

Lazy wrapper around numpy.isinf

ismissing()

Returns True where there are missing values (masked arrays), missing strings or None

isna()

Returns a boolean expression indicating if the values are Not Availiable (missing or NaN).

isnan()

Returns an array where there are NaN values

log(**kwargs)

Lazy wrapper around numpy.log

log10(**kwargs)

Lazy wrapper around numpy.log10

log1p(**kwargs)

Lazy wrapper around numpy.log1p

map(mapper, nan_value=None, missing_value=None, default_value=None, allow_missing=False)[source]

Map values of an expression or in memory column according to an input dictionary or a custom callable function.

Example:

>>> import vaex
>>> df = vaex.from_arrays(color=['red', 'red', 'blue', 'red', 'green'])
>>> mapper = {'red': 1, 'blue': 2, 'green': 3}
>>> df['color_mapped'] = df.color.map(mapper)
>>> df
#  color      color_mapped
0  red                   1
1  red                   1
2  blue                  2
3  red                   1
4  green                 3
>>> import numpy as np
>>> df = vaex.from_arrays(type=[0, 1, 2, 2, 2, np.nan])
>>> df['role'] = df['type'].map({0: 'admin', 1: 'maintainer', 2: 'user', np.nan: 'unknown'})
>>> df
#    type  role
0       0  admin
1       1  maintainer
2       2  user
3       2  user
4       2  user
5     nan  unknown
>>> import vaex
>>> import numpy as np
>>> df = vaex.from_arrays(type=[0, 1, 2, 2, 2, 4])
>>> df['role'] = df['type'].map({0: 'admin', 1: 'maintainer', 2: 'user'}, default_value='unknown')
>>> df
#    type  role
0       0  admin
1       1  maintainer
2       2  user
3       2  user
4       2  user
5       4  unknown
:param mapper: dict like object used to map the values from keys to values
:param nan_value: value to be used when a nan is present (and not in the mapper)
:param missing_value: value to use used when there is a missing value
:param default_value: value to be used when a value is not in the mapper (like dict.get(key, default))
:param allow_missing: used to signal that values in the mapper should map to a masked array with missing values,
    assumed True when default_value is not None.
:return: A vaex expression
:rtype: vaex.expression.Expression
property masked

Alias to df.is_masked(expression)

max(binby=[], limits=None, shape=128, selection=False, delay=False, progress=None)[source]

Shortcut for ds.max(expression, …), see Dataset.max

maximum(**kwargs)

Lazy wrapper around numpy.maximum

mean(binby=[], limits=None, shape=128, selection=False, delay=False, progress=None)[source]

Shortcut for ds.mean(expression, …), see Dataset.mean

min(binby=[], limits=None, shape=128, selection=False, delay=False, progress=None)[source]

Shortcut for ds.min(expression, …), see Dataset.min

minimum(**kwargs)

Lazy wrapper around numpy.minimum

minmax(binby=[], limits=None, shape=128, selection=False, delay=False, progress=None)[source]

Shortcut for ds.minmax(expression, …), see Dataset.minmax

nop()[source]

Evaluates expression, and drop the result, usefull for benchmarking, since vaex is usually lazy

notna()

Opposite of isna

nunique(dropna=False, dropnan=False, dropmissing=False, selection=None, delay=False)[source]

Counts number of unique values, i.e. len(df.x.unique()) == df.x.nunique().

Parameters
  • dropmissing – do not count missing values

  • dropnan – do not count nan values

  • dropna – short for any of the above, (see Expression.isna())

rad2deg(**kwargs)

Lazy wrapper around numpy.rad2deg

round(**kwargs)

Lazy wrapper around numpy.round

searchsorted(**kwargs)

Lazy wrapper around numpy.searchsorted

sin(**kwargs)

Lazy wrapper around numpy.sin

sinc(**kwargs)

Lazy wrapper around numpy.sinc

sinh(**kwargs)

Lazy wrapper around numpy.sinh

sqrt(**kwargs)

Lazy wrapper around numpy.sqrt

std(binby=[], limits=None, shape=128, selection=False, delay=False, progress=None)[source]

Shortcut for ds.std(expression, …), see Dataset.std

property str

Gives access to string operations via StringOperations

property str_pandas

Gives access to string operations via StringOperationsPandas (using Pandas Series)

sum(binby=[], limits=None, shape=128, selection=False, delay=False, progress=None)[source]

Shortcut for ds.sum(expression, …), see Dataset.sum

tan(**kwargs)

Lazy wrapper around numpy.tan

tanh(**kwargs)

Lazy wrapper around numpy.tanh

property td

Gives access to timedelta operations via TimeDelta

to_arrow(convert_to_native=False)[source]

Convert to Apache Arrow array (will byteswap/copy if convert_to_native=True).

to_numpy(strict=True)[source]

Return a numpy representation of the data

to_pandas_series()[source]

Return a pandas.Series representation of the expression.

Note: Pandas is likely to make a memory copy of the data.

tolist(i1=None, i2=None)[source]

Short for expr.evaluate().tolist()

property transient

If this expression is not transient (e.g. on disk) optimizations can be made

unique(dropna=False, dropnan=False, dropmissing=False, selection=None, delay=False)[source]

Returns all unique values.

Parameters
  • dropmissing – do not count missing values

  • dropnan – do not count nan values

  • dropna – short for any of the above, (see Expression.isna())

value_counts(dropna=False, dropnan=False, dropmissing=False, ascending=False, progress=False)[source]

Computes counts of unique values.

WARNING:
  • If the expression/column is not categorical, it will be converted on the fly

  • dropna is False by default, it is True by default in pandas

Parameters
  • dropna – when True, it will not report the NA (see Expression.isna())

  • dropnan – when True, it will not report the nans(see Expression.isnan())

  • dropmissing – when True, it will not report the missing values (see Expression.ismissing())

  • ascending – when False (default) it will report the most frequent occuring item first

Returns

Pandas series containing the counts

var(binby=[], limits=None, shape=128, selection=False, delay=False, progress=None)[source]

Shortcut for ds.std(expression, …), see Dataset.var

variables(ourself=False, expand_virtual=True, include_virtual=True)[source]

Return a set of variables this expression depends on.

Example:

>>> df = vaex.example()
>>> r = np.sqrt(df.data.x**2 + df.data.y**2)
>>> r.variables()
{'x', 'y'}
where(**kwargs)

Lazy wrapper around numpy.where

Aggregation and statistics

class vaex.stat.Expression[source]

Bases: object

Describes an expression for a statistic

calculate(ds, binby=[], shape=256, limits=None, selection=None)[source]

Calculate the statistic for a Dataset

vaex.stat.correlation(x, y)[source]

Creates a standard deviation statistic

vaex.stat.count(expression='*')[source]

Creates a count statistic

vaex.stat.covar(x, y)[source]

Creates a standard deviation statistic

vaex.stat.mean(expression)[source]

Creates a mean statistic

vaex.stat.std(expression)[source]

Creates a standard deviation statistic

vaex.stat.sum(expression)[source]

Creates a sum statistic

class vaex.agg.AggregatorDescriptorMean(name, expression, short_name='mean', selection=None, edges=False)[source]

Bases: vaex.agg.AggregatorDescriptorMulti

class vaex.agg.AggregatorDescriptorMulti(name, expression, short_name, selection=None, edges=False)[source]

Bases: vaex.agg.AggregatorDescriptor

Uses multiple operations/aggregation to calculate the final aggretation

class vaex.agg.AggregatorDescriptorStd(name, expression, short_name='var', ddof=0, selection=None, edges=False)[source]

Bases: vaex.agg.AggregatorDescriptorVar

class vaex.agg.AggregatorDescriptorVar(name, expression, short_name='var', ddof=0, selection=None, edges=False)[source]

Bases: vaex.agg.AggregatorDescriptorMulti

vaex.agg.count(expression='*', selection=None, edges=False)[source]

Creates a count aggregation

vaex.agg.first(expression, order_expression, selection=None, edges=False)[source]

Creates a max aggregation

vaex.agg.max(expression, selection=None, edges=False)[source]

Creates a max aggregation

vaex.agg.mean(expression, selection=None, edges=False)[source]

Creates a mean aggregation

vaex.agg.min(expression, selection=None, edges=False)[source]

Creates a min aggregation

vaex.agg.nunique(expression, dropna=False, dropnan=False, dropmissing=False, selection=None, edges=False)[source]

Aggregator that calculates the number of unique items per bin.

Parameters
  • expression – Expression for which to calculate the unique items

  • dropmissing – do not count missing values

  • dropnan – do not count nan values

  • dropna – short for any of the above, (see Expression.isna())

vaex.agg.std(expression, ddof=0, selection=None, edges=False)[source]

Creates a standard deviation aggregation

vaex.agg.sum(expression, selection=None, edges=False)[source]

Creates a sum aggregation

vaex.agg.var(expression, ddof=0, selection=None, edges=False)[source]

Creates a variance aggregation

Extensions

String operations

class vaex.expression.StringOperations(expression)[source]

Bases: object

String operations.

Usually accessed using e.g. df.name.str.lower()

__init__(expression)[source]

Initialize self. See help(type(self)) for accurate signature.

__weakref__

list of weak references to the object (if defined)

byte_length()

Returns the number of bytes in a string sample.

Returns

an expression contains the number of bytes in each sample of a string column.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.byte_length()
Expression = str_byte_length(text)
Length: 5 dtype: int64 (expression)
-----------------------------------
0   9
1  11
2   9
3   3
4   4
capitalize()

Capitalize the first letter of a string sample.

Returns

an expression containing the capitalized strings.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.capitalize()
Expression = str_capitalize(text)
Length: 5 dtype: str (expression)
---------------------------------
0    Something
1  Very pretty
2    Is coming
3          Our
4         Way.
cat(other)

Concatenate two string columns on a row-by-row basis.

Parameters

other (expression) – The expression of the other column to be concatenated.

Returns

an expression containing the concatenated columns.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.cat(df.text)
Expression = str_cat(text, text)
Length: 5 dtype: str (expression)
---------------------------------
0      SomethingSomething
1  very prettyvery pretty
2      is comingis coming
3                  ourour
4                way.way.
center(width, fillchar=' ')

Fills the left and right side of the strings with additional characters, such that the sample has a total of width characters.

Parameters
  • width (int) – The total number of characters of the resulting string sample.

  • fillchar (str) – The character used for filling.

Returns

an expression containing the filled strings.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.center(width=11, fillchar='!')
Expression = str_center(text, width=11, fillchar='!')
Length: 5 dtype: str (expression)
---------------------------------
0  !Something!
1  very pretty
2  !is coming!
3  !!!!our!!!!
4  !!!!way.!!!
contains(pattern, regex=True)

Check if a string pattern or regex is contained within a sample of a string column.

Parameters
  • pattern (str) – A string or regex pattern

  • regex (bool) – If True,

Returns

an expression which is evaluated to True if the pattern is found in a given sample, and it is False otherwise.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.contains('very')
Expression = str_contains(text, 'very')
Length: 5 dtype: bool (expression)
----------------------------------
0  False
1   True
2  False
3  False
4  False
count(pat, regex=False)

Count the occurences of a pattern in sample of a string column.

Parameters
  • pat (str) – A string or regex pattern

  • regex (bool) – If True,

Returns

an expression containing the number of times a pattern is found in each sample.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.count(pat="et", regex=False)
Expression = str_count(text, pat='et', regex=False)
Length: 5 dtype: int64 (expression)
-----------------------------------
0  1
1  1
2  0
3  0
4  0
endswith(pat)

Check if the end of each string sample matches the specified pattern.

Parameters

pat (str) – A string pattern or a regex

Returns

an expression evaluated to True if the pattern is found at the end of a given sample, False otherwise.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.endswith(pat="ing")
Expression = str_endswith(text, pat='ing')
Length: 5 dtype: bool (expression)
----------------------------------
0   True
1  False
2   True
3  False
4  False
equals(y)

Tests if strings x and y are the same

Returns

a boolean expression

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.equals(df.text)
Expression = str_equals(text, text)
Length: 5 dtype: bool (expression)
----------------------------------
0  True
1  True
2  True
3  True
4  True
>>> df.text.str.equals('our')
Expression = str_equals(text, 'our')
Length: 5 dtype: bool (expression)
----------------------------------
0  False
1  False
2  False
3   True
4  False
find(sub, start=0, end=None)

Returns the lowest indices in each string in a column, where the provided substring is fully contained between within a sample. If the substring is not found, -1 is returned.

Parameters
  • sub (str) – A substring to be found in the samples

  • start (int) –

  • end (int) –

Returns

an expression containing the lowest indices specifying the start of the substring.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.find(sub="et")
Expression = str_find(text, sub='et')
Length: 5 dtype: int64 (expression)
-----------------------------------
0   3
1   7
2  -1
3  -1
4  -1
get(i)

Extract a character from each sample at the specified position from a string column. Note that if the specified position is out of bound of the string sample, this method returns ‘’, while pandas retunrs nan.

Parameters

i (int) – The index location, at which to extract the character.

Returns

an expression containing the extracted characters.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.get(5)
Expression = str_get(text, 5)
Length: 5 dtype: str (expression)
---------------------------------
0    h
1    p
2    m
3
4
index(sub, start=0, end=None)

Returns the lowest indices in each string in a column, where the provided substring is fully contained between within a sample. If the substring is not found, -1 is returned. It is the same as str.find.

Parameters
  • sub (str) – A substring to be found in the samples

  • start (int) –

  • end (int) –

Returns

an expression containing the lowest indices specifying the start of the substring.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.index(sub="et")
Expression = str_find(text, sub='et')
Length: 5 dtype: int64 (expression)
-----------------------------------
0   3
1   7
2  -1
3  -1
4  -1
isalnum(ascii=False)

Check if all characters in a string sample are alphanumeric.

Parameters

ascii (bool) – Transform only ascii characters (usually faster).

Returns

an expression evaluated to True if a sample contains only alphanumeric characters, otherwise False.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.isalnum()
Expression = str_isalnum(text)
Length: 5 dtype: bool (expression)
----------------------------------
0   True
1  False
2  False
3   True
4  False
isalpha()

Check if all characters in a string sample are alphabetic.

Returns

an expression evaluated to True if a sample contains only alphabetic characters, otherwise False.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.isalpha()
Expression = str_isalpha(text)
Length: 5 dtype: bool (expression)
----------------------------------
0   True
1  False
2  False
3   True
4  False
isdigit()

Check if all characters in a string sample are digits.

Returns

an expression evaluated to True if a sample contains only digits, otherwise False.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', '6']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  6
>>> df.text.str.isdigit()
Expression = str_isdigit(text)
Length: 5 dtype: bool (expression)
----------------------------------
0  False
1  False
2  False
3  False
4   True
islower()

Check if all characters in a string sample are lowercase characters.

Returns

an expression evaluated to True if a sample contains only lowercase characters, otherwise False.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.islower()
Expression = str_islower(text)
Length: 5 dtype: bool (expression)
----------------------------------
0  False
1   True
2   True
3   True
4   True
isspace()

Check if all characters in a string sample are whitespaces.

Returns

an expression evaluated to True if a sample contains only whitespaces, otherwise False.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', '      ', ' ']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3
  4
>>> df.text.str.isspace()
Expression = str_isspace(text)
Length: 5 dtype: bool (expression)
----------------------------------
0  False
1  False
2  False
3   True
4   True
istitle(ascii=False)

TODO

isupper()

Check if all characters in a string sample are lowercase characters.

Returns

an expression evaluated to True if a sample contains only lowercase characters, otherwise False.

Example:

>>> import vaex
>>> text = ['SOMETHING', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  SOMETHING
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.isupper()
Expression = str_isupper(text)
Length: 5 dtype: bool (expression)
----------------------------------
0   True
1  False
2  False
3  False
4  False
join(sep)

Same as find (difference with pandas is that it does not raise a ValueError)

len()

Returns the length of a string sample.

Returns

an expression contains the length of each sample of a string column.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.len()
Expression = str_len(text)
Length: 5 dtype: int64 (expression)
-----------------------------------
0   9
1  11
2   9
3   3
4   4
ljust(width, fillchar=' ')

Fills the right side of string samples with a specified character such that the strings are right-hand justified.

Parameters
  • width (int) – The minimal width of the strings.

  • fillchar (str) – The character used for filling.

Returns

an expression containing the filled strings.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.ljust(width=10, fillchar='!')
Expression = str_ljust(text, width=10, fillchar='!')
Length: 5 dtype: str (expression)
---------------------------------
0   Something!
1  very pretty
2   is coming!
3   our!!!!!!!
4   way.!!!!!!
lower()

Converts string samples to lower case.

Returns

an expression containing the converted strings.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.lower()
Expression = str_lower(text)
Length: 5 dtype: str (expression)
---------------------------------
0    something
1  very pretty
2    is coming
3          our
4         way.
lstrip(to_strip=None)

Remove leading characters from a string sample.

Parameters

to_strip (str) – The string to be removed

Returns

an expression containing the modified string column.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.lstrip(to_strip='very ')
Expression = str_lstrip(text, to_strip='very ')
Length: 5 dtype: str (expression)
---------------------------------
0  Something
1     pretty
2  is coming
3        our
4       way.
match(pattern)

Check if a string sample matches a given regular expression.

Parameters

pattern (str) – a string or regex to match to a string sample.

Returns

an expression which is evaluated to True if a match is found, False otherwise.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.match(pattern='our')
Expression = str_match(text, pattern='our')
Length: 5 dtype: bool (expression)
----------------------------------
0  False
1  False
2  False
3   True
4  False
pad(width, side='left', fillchar=' ')

Pad strings in a given column.

Parameters
  • width (int) – The total width of the string

  • side (str) – If ‘left’ than pad on the left, if ‘right’ than pad on the right side the string.

  • fillchar (str) – The character used for padding.

Returns

an expression containing the padded strings.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.pad(width=10, side='left', fillchar='!')
Expression = str_pad(text, width=10, side='left', fillchar='!')
Length: 5 dtype: str (expression)
---------------------------------
0   !Something
1  very pretty
2   !is coming
3   !!!!!!!our
4   !!!!!!way.
repeat(repeats)

Duplicate each string in a column.

Parameters

repeats (int) – number of times each string sample is to be duplicated.

Returns

an expression containing the duplicated strings

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.repeat(3)
Expression = str_repeat(text, 3)
Length: 5 dtype: str (expression)
---------------------------------
0        SomethingSomethingSomething
1  very prettyvery prettyvery pretty
2        is comingis comingis coming
3                          ourourour
4                       way.way.way.
replace(pat, repl, n=- 1, flags=0, regex=False)

Replace occurences of a pattern/regex in a column with some other string.

Parameters
  • pattern (str) – string or a regex pattern

  • replace (str) – a replacement string

  • n (int) – number of replacements to be made from the start. If -1 make all replacements.

  • flags (int) –

    ??

  • regex (bool) – If True, …?

Returns

an expression containing the string replacements.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.replace(pat='et', repl='__')
Expression = str_replace(text, pat='et', repl='__')
Length: 5 dtype: str (expression)
---------------------------------
0    Som__hing
1  very pr__ty
2    is coming
3          our
4         way.
rfind(sub, start=0, end=None)

Returns the highest indices in each string in a column, where the provided substring is fully contained between within a sample. If the substring is not found, -1 is returned.

Parameters
  • sub (str) – A substring to be found in the samples

  • start (int) –

  • end (int) –

Returns

an expression containing the highest indices specifying the start of the substring.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.rfind(sub="et")
Expression = str_rfind(text, sub='et')
Length: 5 dtype: int64 (expression)
-----------------------------------
0   3
1   7
2  -1
3  -1
4  -1
rindex(sub, start=0, end=None)

Returns the highest indices in each string in a column, where the provided substring is fully contained between within a sample. If the substring is not found, -1 is returned. Same as str.rfind.

Parameters
  • sub (str) – A substring to be found in the samples

  • start (int) –

  • end (int) –

Returns

an expression containing the highest indices specifying the start of the substring.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.rindex(sub="et")
Expression = str_rindex(text, sub='et')
Length: 5 dtype: int64 (expression)
-----------------------------------
0   3
1   7
2  -1
3  -1
4  -1
rjust(width, fillchar=' ')

Fills the left side of string samples with a specified character such that the strings are left-hand justified.

Parameters
  • width (int) – The minimal width of the strings.

  • fillchar (str) – The character used for filling.

Returns

an expression containing the filled strings.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.rjust(width=10, fillchar='!')
Expression = str_rjust(text, width=10, fillchar='!')
Length: 5 dtype: str (expression)
---------------------------------
0   !Something
1  very pretty
2   !is coming
3   !!!!!!!our
4   !!!!!!way.
rstrip(to_strip=None)

Remove trailing characters from a string sample.

Parameters

to_strip (str) – The string to be removed

Returns

an expression containing the modified string column.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.rstrip(to_strip='ing')
Expression = str_rstrip(text, to_strip='ing')
Length: 5 dtype: str (expression)
---------------------------------
0       Someth
1  very pretty
2       is com
3          our
4         way.
slice(start=0, stop=None)

Slice substrings from each string element in a column.

Parameters
  • start (int) – The start position for the slice operation.

  • end (int) – The stop position for the slice operation.

Returns

an expression containing the sliced substrings.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.slice(start=2, stop=5)
Expression = str_pandas_slice(text, start=2, stop=5)
Length: 5 dtype: str (expression)
---------------------------------
0  met
1   ry
2   co
3    r
4   y.
startswith(pat)

Check if a start of a string matches a pattern.

Parameters

pat (str) – A string pattern. Regular expressions are not supported.

Returns

an expression which is evaluated to True if the pattern is found at the start of a string sample, False otherwise.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.startswith(pat='is')
Expression = str_startswith(text, pat='is')
Length: 5 dtype: bool (expression)
----------------------------------
0  False
1  False
2   True
3  False
4  False
strip(to_strip=None)

Removes leading and trailing characters.

Strips whitespaces (including new lines), or a set of specified characters from each string saple in a column, both from the left right sides.

Parameters
  • to_strip (str) – The characters to be removed. All combinations of the characters will be removed. If None, it removes whitespaces.

  • returns – an expression containing the modified string samples.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.strip(to_strip='very')
Expression = str_strip(text, to_strip='very')
Length: 5 dtype: str (expression)
---------------------------------
0  Something
1      prett
2  is coming
3         ou
4       way.
title()

Converts all string samples to titlecase.

Returns

an expression containing the converted strings.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.title()
Expression = str_title(text)
Length: 5 dtype: str (expression)
---------------------------------
0    Something
1  Very Pretty
2    Is Coming
3          Our
4         Way.
upper(ascii=False)

Converts all strings in a column to uppercase.

Parameters

ascii (bool) – Transform only ascii characters (usually faster).

Returns

an expression containing the converted strings.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.upper()
Expression = str_upper(text)
Length: 5 dtype: str (expression)
---------------------------------
0    SOMETHING
1  VERY PRETTY
2    IS COMING
3          OUR
4         WAY.
zfill(width)

Pad strings in a column by prepanding “0” characters.

Parameters

width (int) – The minimum length of the resulting string. Strings shorter less than width will be prepended with zeros.

Returns

an expression containing the modified strings.

Example:

>>> import vaex
>>> text = ['Something', 'very pretty', 'is coming', 'our', 'way.']
>>> df = vaex.from_arrays(text=text)
>>> df
  #  text
  0  Something
  1  very pretty
  2  is coming
  3  our
  4  way.
>>> df.text.str.zfill(width=12)
Expression = str_zfill(text, width=12)
Length: 5 dtype: str (expression)
---------------------------------
0  000Something
1  0very pretty
2  000is coming
3  000000000our
4  00000000way.

String (pandas) operations

class vaex.expression.StringOperationsPandas(expression)[source]

Bases: object

String operations using Pandas Series (much slower)

__init__(expression)[source]

Initialize self. See help(type(self)) for accurate signature.

__weakref__

list of weak references to the object (if defined)

byte_length(**kwargs)

Wrapper around pandas.Series.byte_length

capitalize(**kwargs)

Wrapper around pandas.Series.capitalize

cat(**kwargs)

Wrapper around pandas.Series.cat

center(**kwargs)

Wrapper around pandas.Series.center

contains(**kwargs)

Wrapper around pandas.Series.contains

count(**kwargs)

Wrapper around pandas.Series.count

endswith(**kwargs)

Wrapper around pandas.Series.endswith

equals(**kwargs)

Wrapper around pandas.Series.equals

find(**kwargs)

Wrapper around pandas.Series.find

get(**kwargs)

Wrapper around pandas.Series.get

index(**kwargs)

Wrapper around pandas.Series.index

isalnum(**kwargs)

Wrapper around pandas.Series.isalnum

isalpha(**kwargs)

Wrapper around pandas.Series.isalpha

isdigit(**kwargs)

Wrapper around pandas.Series.isdigit

islower(**kwargs)

Wrapper around pandas.Series.islower

isspace(**kwargs)

Wrapper around pandas.Series.isspace

istitle(**kwargs)

Wrapper around pandas.Series.istitle

isupper(**kwargs)

Wrapper around pandas.Series.isupper

join(**kwargs)

Wrapper around pandas.Series.join

len(**kwargs)

Wrapper around pandas.Series.len

ljust(**kwargs)

Wrapper around pandas.Series.ljust

lower(**kwargs)

Wrapper around pandas.Series.lower

lstrip(**kwargs)

Wrapper around pandas.Series.lstrip

match(**kwargs)

Wrapper around pandas.Series.match

pad(**kwargs)

Wrapper around pandas.Series.pad

repeat(**kwargs)

Wrapper around pandas.Series.repeat

replace(**kwargs)

Wrapper around pandas.Series.replace

rfind(**kwargs)

Wrapper around pandas.Series.rfind

rindex(**kwargs)

Wrapper around pandas.Series.rindex

rjust(**kwargs)

Wrapper around pandas.Series.rjust

rstrip(**kwargs)

Wrapper around pandas.Series.rstrip

slice(**kwargs)

Wrapper around pandas.Series.slice

split(**kwargs)

Wrapper around pandas.Series.split

startswith(**kwargs)

Wrapper around pandas.Series.startswith

strip(**kwargs)

Wrapper around pandas.Series.strip

title(**kwargs)

Wrapper around pandas.Series.title

upper(**kwargs)

Wrapper around pandas.Series.upper

zfill(**kwargs)

Wrapper around pandas.Series.zfill

Date/time operations

class vaex.expression.DateTime(expression)[source]

Bases: object

DateTime operations

Usually accessed using e.g. df.birthday.dt.dayofweek

__init__(expression)[source]

Initialize self. See help(type(self)) for accurate signature.

__weakref__

list of weak references to the object (if defined)

property date

Return the date part of the datetime value

Returns

an expression containing the date portion of a datetime value

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.date
Expression = dt_date(date)
Length: 3 dtype: datetime64[D] (expression)
-------------------------------------------
0  2009-10-12
1  2016-02-11
2  2015-11-12
property day

Extracts the day from a datetime sample.

Returns

an expression containing the day extracted from a datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.day
Expression = dt_day(date)
Length: 3 dtype: int64 (expression)
-----------------------------------
0  12
1  11
2  12
property day_name

Returns the day names of a datetime sample in English.

Returns

an expression containing the day names extracted from a datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.day_name
Expression = dt_day_name(date)
Length: 3 dtype: str (expression)
---------------------------------
0    Monday
1  Thursday
2  Thursday
property dayofweek

Obtain the day of the week with Monday=0 and Sunday=6

Returns

an expression containing the day of week.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.dayofweek
Expression = dt_dayofweek(date)
Length: 3 dtype: int64 (expression)
-----------------------------------
0  0
1  3
2  3
property dayofyear

The ordinal day of the year.

Returns

an expression containing the ordinal day of the year.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.dayofyear
Expression = dt_dayofyear(date)
Length: 3 dtype: int64 (expression)
-----------------------------------
0  285
1   42
2  316
floor(freq, *args)

Perform floor operation on an expression for a given frequency.

Parameters

freq – The frequency level to floor the index to. Must be a fixed frequency like ‘S’ (second), or ‘H’ (hour), but not ‘ME’ (month end).

Returns

an expression containing the floored datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.floor("H")
Expression = dt_floor(date, 'H')
Length: 3 dtype: datetime64[ns] (expression)
--------------------------------------------
0  2009-10-12 03:00:00.000000000
1  2016-02-11 10:00:00.000000000
2  2015-11-12 11:00:00.000000000
property hour

Extracts the hour out of a datetime samples.

Returns

an expression containing the hour extracted from a datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.hour
Expression = dt_hour(date)
Length: 3 dtype: int64 (expression)
-----------------------------------
0   3
1  10
2  11
property is_leap_year

Check whether a year is a leap year.

Returns

an expression which evaluates to True if a year is a leap year, and to False otherwise.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.is_leap_year
Expression = dt_is_leap_year(date)
Length: 3 dtype: bool (expression)
----------------------------------
0  False
1   True
2  False
property minute

Extracts the minute out of a datetime samples.

Returns

an expression containing the minute extracted from a datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.minute
Expression = dt_minute(date)
Length: 3 dtype: int64 (expression)
-----------------------------------
0  31
1  17
2  34
property month

Extracts the month out of a datetime sample.

Returns

an expression containing the month extracted from a datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.month
Expression = dt_month(date)
Length: 3 dtype: int64 (expression)
-----------------------------------
0  10
1   2
2  11
property month_name

Returns the month names of a datetime sample in English.

Returns

an expression containing the month names extracted from a datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.month_name
Expression = dt_month_name(date)
Length: 3 dtype: str (expression)
---------------------------------
0   October
1  February
2  November
property quarter

Extracts the quarter from a datetime sample.

Returns

an expression containing the number of the quarter extracted from a datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.quarter
Expression = dt_quarter(date)
Length: 3 dtype: int64 (expression)
-----------------------------------
0  4
1  1
2  4
property second

Extracts the second out of a datetime samples.

Returns

an expression containing the second extracted from a datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.second
Expression = dt_second(date)
Length: 3 dtype: int64 (expression)
-----------------------------------
0   0
1  34
2  22
strftime(date_format)

Returns a formatted string from a datetime sample.

Returns

an expression containing a formatted string extracted from a datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.strftime("%Y-%m")
Expression = dt_strftime(date, '%Y-%m')
Length: 3 dtype: object (expression)
------------------------------------
0  2009-10
1  2016-02
2  2015-11
property weekofyear

Returns the week ordinal of the year.

Returns

an expression containing the week ordinal of the year, extracted from a datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.weekofyear
Expression = dt_weekofyear(date)
Length: 3 dtype: int64 (expression)
-----------------------------------
0  42
1   6
2  46
property year

Extracts the year out of a datetime sample.

Returns

an expression containing the year extracted from a datetime column.

Example:

>>> import vaex
>>> import numpy as np
>>> date = np.array(['2009-10-12T03:31:00', '2016-02-11T10:17:34', '2015-11-12T11:34:22'], dtype=np.datetime64)
>>> df = vaex.from_arrays(date=date)
>>> df
  #  date
  0  2009-10-12 03:31:00
  1  2016-02-11 10:17:34
  2  2015-11-12 11:34:22
>>> df.date.dt.year
Expression = dt_year(date)
Length: 3 dtype: int64 (expression)
-----------------------------------
0  2009
1  2016
2  2015

Timedelta operations

class vaex.expression.TimeDelta(expression)[source]

Bases: object

TimeDelta operations

Usually accessed using e.g. df.delay.td.days

__init__(expression)[source]

Initialize self. See help(type(self)) for accurate signature.

__weakref__

list of weak references to the object (if defined)

property days

Number of days in each timedelta sample.

Returns

an expression containing the number of days in a timedelta sample.

Example:

>>> import vaex
>>> import numpy as np
>>> delta = np.array([17658720110,   11047049384039, 40712636304958, -18161254954], dtype='timedelta64[s]')
>>> df = vaex.from_arrays(delta=delta)
>>> df
  #  delta
  0  204 days +9:12:00
  1  1 days +6:41:10
  2  471 days +5:03:56
  3  -22 days +23:31:15
>>> df.delta.td.days
Expression = td_days(delta)
Length: 4 dtype: int64 (expression)
-----------------------------------
0  204
1    1
2  471
3  -22
property microseconds

Number of microseconds (>= 0 and less than 1 second) in each timedelta sample.

Returns

an expression containing the number of microseconds in a timedelta sample.

Example:

>>> import vaex
>>> import numpy as np
>>> delta = np.array([17658720110,   11047049384039, 40712636304958, -18161254954], dtype='timedelta64[s]')
>>> df = vaex.from_arrays(delta=delta)
>>> df
  #  delta
  0  204 days +9:12:00
  1  1 days +6:41:10
  2  471 days +5:03:56
  3  -22 days +23:31:15
>>> df.delta.td.microseconds
Expression = td_microseconds(delta)
Length: 4 dtype: int64 (expression)
-----------------------------------
0  290448
1  978582
2   19583
3  709551
property nanoseconds

Number of nanoseconds (>= 0 and less than 1 microsecond) in each timedelta sample.

Returns

an expression containing the number of nanoseconds in a timedelta sample.

Example:

>>> import vaex
>>> import numpy as np
>>> delta = np.array([17658720110,   11047049384039, 40712636304958, -18161254954], dtype='timedelta64[s]')
>>> df = vaex.from_arrays(delta=delta)
>>> df
  #  delta
  0  204 days +9:12:00
  1  1 days +6:41:10
  2  471 days +5:03:56
  3  -22 days +23:31:15
>>> df.delta.td.nanoseconds
Expression = td_nanoseconds(delta)
Length: 4 dtype: int64 (expression)
-----------------------------------
0  384
1   16
2  488
3  616
property seconds

Number of seconds (>= 0 and less than 1 day) in each timedelta sample.

Returns

an expression containing the number of seconds in a timedelta sample.

Example:

>>> import vaex
>>> import numpy as np
>>> delta = np.array([17658720110,   11047049384039, 40712636304958, -18161254954], dtype='timedelta64[s]')
>>> df = vaex.from_arrays(delta=delta)
>>> df
  #  delta
  0  204 days +9:12:00
  1  1 days +6:41:10
  2  471 days +5:03:56
  3  -22 days +23:31:15
>>> df.delta.td.seconds
Expression = td_seconds(delta)
Length: 4 dtype: int64 (expression)
-----------------------------------
0  30436
1  39086
2  28681
3  23519
total_seconds()

Total duration of each timedelta sample expressed in seconds.

Returns

an expression containing the total number of seconds in a timedelta sample.

Example: >>> import vaex >>> import numpy as np >>> delta = np.array([17658720110, 11047049384039, 40712636304958, -18161254954], dtype=’timedelta64[s]’) >>> df = vaex.from_arrays(delta=delta) >>> df

# delta 0 204 days +9:12:00 1 1 days +6:41:10 2 471 days +5:03:56 3 -22 days +23:31:15

>>> df.delta.td.total_seconds()
Expression = td_total_seconds(delta)
Length: 4 dtype: float64 (expression)
-------------------------------------
0  -7.88024e+08
1  -2.55032e+09
2   6.72134e+08
3   2.85489e+08

Geo operations

class vaex.geo.DataFrameAccessorGeo(df)[source]

Bases: object

Geometry/geographic helper methods

Example:

>>> df_xyz = df.geo.spherical2cartesian(df.longitude, df.latitude, df.distance)
>>> df_xyz.x.mean()
__init__(df)[source]

Initialize self. See help(type(self)) for accurate signature.

__weakref__

list of weak references to the object (if defined)

bearing(lon1, lat1, lon2, lat2, bearing='bearing', inplace=False)[source]

Calculates a bearing, based on http://www.movable-type.co.uk/scripts/latlong.html

cartesian2spherical(x='x', y='y', z='z', alpha='l', delta='b', distance='distance', radians=False, center=None, center_name='solar_position', inplace=False)[source]

Convert cartesian to spherical coordinates.

Parameters
  • x

  • y

  • z

  • alpha

  • delta – name for polar angle, ranges from -90 to 90 (or -pi to pi when radians is True).

  • distance

  • radians

  • center

  • center_name

Returns

cartesian_to_polar(x='x', y='y', radius_out='r_polar', azimuth_out='phi_polar', propagate_uncertainties=False, radians=False, inplace=False)[source]

Convert cartesian to polar coordinates

Parameters
  • x – expression for x

  • y – expression for y

  • radius_out – name for the virtual column for the radius

  • azimuth_out – name for the virtual column for the azimuth angle

  • propagate_uncertainties – {propagate_uncertainties}

  • radians – if True, azimuth is in radians, defaults to degrees

Returns

inside_polygon(y, px, py)

Test if points defined by x and y are inside the polygon px, py

Example:

>>> import vaex
>>> import numpy as np
>>> df = vaex.from_arrays(x=[1, 2, 3], y=[2, 3, 4])
>>> px = np.array([1.5, 2.5, 2.5, 1.5])
>>> py = np.array([2.5, 2.5, 3.5, 3.5])
>>> df['inside'] = df.geo.inside_polygon(df.x, df.y, px, py)
>>> df
#    x    y  inside
0    1    2  False
1    2    3  True
2    3    4  False
Parameters
  • x – {expression_one}

  • y – {expression_one}

  • px – list of x coordinates for the polygon

  • px – list of y coordinates for the polygon

Returns

Expression, which is true if point is inside, else false.

inside_polygons(y, pxs, pys, any=True)

Test if points defined by x and y are inside all or any of the the polygons px, py

Example:

>>> import vaex
>>> import numpy as np
>>> df = vaex.from_arrays(x=[1, 2, 3], y=[2, 3, 4])
>>> px = np.array([1.5, 2.5, 2.5, 1.5])
>>> py = np.array([2.5, 2.5, 3.5, 3.5])
>>> df['inside'] = df.geo.inside_polygons(df.x, df.y, [px, px + 1], [py, py + 1], any=True)
>>> df
#    x    y  inside
0    1    2  False
1    2    3  True
2    3    4  True
Parameters
  • x – {expression_one}

  • y – {expression_one}

  • pxs – list of N ndarrays with x coordinates for the polygon, N is the number of polygons

  • pxs – list of N ndarrays with y coordinates for the polygon

  • any – return true if in any polygon, or all polygons

Returns

Expression , which is true if point is inside, else false.

inside_which_polygon(y, pxs, pys)

Find in which polygon (0 based index) a point resides

Example:

>>> import vaex
>>> import numpy as np
>>> df = vaex.from_arrays(x=[1, 2, 3], y=[2, 3, 4])
>>> px = np.array([1.5, 2.5, 2.5, 1.5])
>>> py = np.array([2.5, 2.5, 3.5, 3.5])
>>> df['polygon_index'] = df.geo.inside_which_polygon(df.x, df.y, [px, px + 1], [py, py + 1])
>>> df
#    x    y  polygon_index
0    1    2  --
1    2    3  0
2    3    4  1
Parameters
  • x – {expression_one}

  • y – {expression_one}

  • px – list of N ndarrays with x coordinates for the polygon, N is the number of polygons

  • px – list of N ndarrays with y coordinates for the polygon

Returns

Expression, 0 based index to which polygon the point belongs (or missing/masked value)

inside_which_polygons(x, y, pxss, pyss=None, any=True)[source]

Find in which set of polygons (0 based index) a point resides.

If any=True, it will be the first matching polygon set index, if any=False, it will be the first index that matches all polygons in the set.

>>> import vaex
>>> import numpy as np
>>> df = vaex.from_arrays(x=[1, 2, 3], y=[2, 3, 4])
>>> px = np.array([1.5, 2.5, 2.5, 1.5])
>>> py = np.array([2.5, 2.5, 3.5, 3.5])
>>> polygonA = [px, py]
>>> polygonB = [px + 1, py + 1]
>>> pxs = [[polygonA, polygonB], [polygonA]]
>>> df['polygon_index'] = df.geo.inside_which_polygons(df.x, df.y, pxs, any=True)
>>> df
#    x    y  polygon_index
0    1    2  --
1    2    3  0
2    3    4  0
>>> df['polygon_index'] = df.geo.inside_which_polygons(df.x, df.y, pxs, any=False)
>>> df
#    x    y  polygon_index
0    1    2  --
1    2    3  1
2    3    4  --
Parameters
  • x – expression in the form of a string, e.g. ‘x’ or ‘x+y’ or vaex expression object, e.g. df.x or df.x+df.y

  • y – expression in the form of a string, e.g. ‘x’ or ‘x+y’ or vaex expression object, e.g. df.x or df.x+df.y

  • px – list of N ndarrays with x coordinates for the polygon, N is the number of polygons

  • px – list of N ndarrays with y coordinates for the polygon, if None, the shape of the ndarrays of the last dimention of the x arrays should be 2 (i.e. have the x and y coordinates)

  • any – test if point it in any polygon (logically or), or all polygons (logically and)

Returns

Expression, 0 based index to which polygon the point belongs (or missing/masked value)

project_aitoff(alpha, delta, x, y, radians=True, inplace=False)[source]

Add aitoff (https://en.wikipedia.org/wiki/Aitoff_projection) projection

Parameters
  • alpha – azimuth angle

  • delta – polar angle

  • x – output name for x coordinate

  • y – output name for y coordinate

  • radians – input and output in radians (True), or degrees (False)

Returns

project_gnomic(alpha, delta, alpha0=0, delta0=0, x='x', y='y', radians=False, postfix='', inplace=False)[source]

Adds a gnomic projection to the DataFrame

rotation_2d(x, y, xnew, ynew, angle_degrees, propagate_uncertainties=False, inplace=False)[source]

Rotation in 2d.

Parameters
  • x (str) – Name/expression of x column

  • y (str) – idem for y

  • xnew (str) – name of transformed x column

  • ynew (str) –

  • angle_degrees (float) – rotation in degrees, anti clockwise

Returns

spherical2cartesian(alpha, delta, distance, xname='x', yname='y', zname='z', propagate_uncertainties=False, center=[0, 0, 0], radians=False, inplace=False)[source]

Convert spherical to cartesian coordinates.

Parameters
  • alpha

  • delta – polar angle, ranging from the -90 (south pole) to 90 (north pole)

  • distance – radial distance, determines the units of x, y and z

  • xname

  • yname

  • zname

  • propagate_uncertainties – If true, will propagate errors for the new virtual columns, see propagate_uncertainties() for details

  • center

  • radians

Returns

New dataframe (in inplace is False) with new x,y,z columns

velocity_cartesian2polar(x='x', y='y', vx='vx', radius_polar=None, vy='vy', vr_out='vr_polar', vazimuth_out='vphi_polar', propagate_uncertainties=False, inplace=False)[source]

Convert cartesian to polar velocities.

Parameters
  • x

  • y

  • vx

  • radius_polar – Optional expression for the radius, may lead to a better performance when given.

  • vy

  • vr_out

  • vazimuth_out

  • propagate_uncertainties – If true, will propagate errors for the new virtual columns, see propagate_uncertainties() for details

Returns

velocity_cartesian2spherical(x='x', y='y', z='z', vx='vx', vy='vy', vz='vz', vr='vr', vlong='vlong', vlat='vlat', distance=None, inplace=False)[source]

Convert velocities from a cartesian to a spherical coordinate system

TODO: uncertainty propagation

Parameters
  • x – name of x column (input)

  • y – y

  • z – z

  • vx – vx

  • vy – vy

  • vz – vz

  • vr – name of the column for the radial velocity in the r direction (output)

  • vlong – name of the column for the velocity component in the longitude direction (output)

  • vlat – name of the column for the velocity component in the latitude direction, positive points to the north pole (output)

  • distance – Expression for distance, if not given defaults to sqrt(x**2+y**2+z**2), but if this column already exists, passing this expression may lead to a better performance

Returns

velocity_polar2cartesian(x='x', y='y', azimuth=None, vr='vr_polar', vazimuth='vphi_polar', vx_out='vx', vy_out='vy', propagate_uncertainties=False, inplace=False)[source]

Convert cylindrical polar velocities to Cartesian.

Parameters
  • x

  • y

  • azimuth – Optional expression for the azimuth in degrees , may lead to a better performance when given.

  • vr

  • vazimuth

  • vx_out

  • vy_out

  • propagate_uncertainties – If true, will propagate errors for the new virtual columns, see propagate_uncertainties() for details

GraphQL operations

class vaex.graphql.DataFrameAccessorGraphQL(df)[source]

Bases: object

Exposes a GraphQL layer to a DataFrame

See the GraphQL example for more usage.

The easiest way to learn to use the GraphQL language/vaex interface is to launch a server, and play with the GraphiQL graphical interface, its autocomplete, and the schema explorer.

We try to stay close to the Hasura API: https://docs.hasura.io/1.0/graphql/manual/api-reference/graphql-api/query.html

__init__(df)[source]

Initialize self. See help(type(self)) for accurate signature.

__weakref__

list of weak references to the object (if defined)

execute(*args, **kwargs)[source]

Creates a schema, and execute the query (first argument)

query(name='df')[source]

Creates a graphene query object exposing this DataFrame named name

schema(name='df', auto_camelcase=False, **kwargs)[source]

Creates a graphene schema for this DataFrame

serve(port=9001, address='', name='df', verbose=True)[source]

Serve the DataFrame via a http server

Jupyter widgets accessor

class vaex.jupyter.DataFrameAccessorWidget(df)[source]

Bases: object

__init__(df)[source]

Initialize self. See help(type(self)) for accurate signature.

__weakref__

list of weak references to the object (if defined)

data_array(axes=[], selection=None, shared=False, display_function=<function display>, **kwargs)[source]

Create a vaex.jupyter.model.DataArray() model and vaex.jupyter.view.DataArray() widget and links them.

This is a convenience method to create the model and view, and hook them up.

execute_debounced()[source]

Schedules an execution of dataframe tasks in the near future (debounced).

expression(value=None, label='Custom expression')[source]

Create a widget to edit a vaex expression.

If value is an :py:`vaex.jupyter.model.Axis` object, its expression will be (bi-directionally) linked to the widget.

Parameters

value – Valid expression (string or Expression object), or Axis

Viz accessors

class vaex.viz.DataFrameAccessorViz(df)[source]

Bases: object

__init__(df)[source]

Initialize self. See help(type(self)) for accurate signature.

__weakref__

list of weak references to the object (if defined)

healpix_heatmap(healpix_expression='source_id/34359738368', healpix_max_level=12, healpix_level=8, what='count(*)', selection=None, grid=None, healpix_input='equatorial', healpix_output='galactic', f=None, colormap='afmhot', grid_limits=None, image_size=800, nest=True, figsize=None, interactive=False, title='', smooth=None, show=False, colorbar=True, rotation=(0, 0, 0), **kwargs)

Viz data in 2d using a healpix column.

Parameters
  • healpix_expression – {healpix_max_level}

  • healpix_max_level – {healpix_max_level}

  • healpix_level – {healpix_level}

  • what – {what}

  • selection – {selection}

  • grid – {grid}

  • healpix_input – Specificy if the healpix index is in “equatorial”, “galactic” or “ecliptic”.

  • healpix_output – Plot in “equatorial”, “galactic” or “ecliptic”.

  • f – function to apply to the data

  • colormap – matplotlib colormap

  • grid_limits – Optional sequence [minvalue, maxvalue] that determine the min and max value that map to the colormap (values below and above these are clipped to the the min/max). (default is [min(f(grid)), max(f(grid)))

  • image_size – size for the image that healpy uses for rendering

  • nest – If the healpix data is in nested (True) or ring (False)

  • figsize – If given, modify the matplotlib figure size. Example (14,9)

  • interactive – (Experimental, uses healpy.mollzoom is True)

  • title – Title of figure

  • smooth – apply gaussian smoothing, in degrees

  • show – Call matplotlib’s show (True) or not (False, defaut)

  • rotation – Rotatate the plot, in format (lon, lat, psi) such that (lon, lat) is the center, and rotate on the screen by angle psi. All angles are degrees.

Returns

heatmap(x=None, y=None, z=None, what='count(*)', vwhat=None, reduce=['colormap'], f=None, normalize='normalize', normalize_axis='what', vmin=None, vmax=None, shape=256, vshape=32, limits=None, grid=None, colormap='afmhot', figsize=None, xlabel=None, ylabel=None, aspect='auto', tight_layout=True, interpolation='nearest', show=False, colorbar=True, colorbar_label=None, selection=None, selection_labels=None, title=None, background_color='white', pre_blend=False, background_alpha=1.0, visual={'column': 'what', 'fade': 'selection', 'layer': 'z', 'row': 'subspace', 'x': 'x', 'y': 'y'}, smooth_pre=None, smooth_post=None, wrap=True, wrap_columns=4, return_extra=False, hardcopy=None)

Viz data in a 2d histogram/heatmap.

Declarative plotting of statistical plots using matplotlib, supports subplots, selections, layers.

Instead of passing x and y, pass a list as x argument for multiple panels. Give what a list of options to have multiple panels. When both are present then will be origanized in a column/row order.

This methods creates a 6 dimensional ‘grid’, where each dimension can map the a visual dimension. The grid dimensions are:

  • x: shape determined by shape, content by x argument or the first dimension of each space

  • y: ,,

  • z: related to the z argument

  • selection: shape equals length of selection argument

  • what: shape equals length of what argument

  • space: shape equals length of x argument if multiple values are given

By default, this its shape is (1, 1, 1, 1, shape, shape) (where x is the last dimension)

The visual dimensions are

  • x: x coordinate on a plot / image (default maps to grid’s x)

  • y: y ,, (default maps to grid’s y)

  • layer: each image in this dimension is blended togeher to one image (default maps to z)

  • fade: each image is shown faded after the next image (default mapt to selection)

  • row: rows of subplots (default maps to space)

  • columns: columns of subplot (default maps to what)

All these mappings can be changes by the visual argument, some examples:

>>> df.plot('x', 'y', what=['mean(x)', 'correlation(vx, vy)'])

Will plot each ‘what’ as a column.

>>> df.plot('x', 'y', selection=['FeH < -3', '(FeH >= -3) & (FeH < -2)'], visual=dict(column='selection'))

Will plot each selection as a column, instead of a faded on top of each other.

Parameters
  • x – Expression to bin in the x direction (by default maps to x), or list of pairs, like [[‘x’, ‘y’], [‘x’, ‘z’]], if multiple pairs are given, this dimension maps to rows by default

  • y – y (by default maps to y)

  • z – Expression to bin in the z direction, followed by a :start,end,shape signature, like ‘FeH:-3,1:5’ will produce 5 layers between -10 and 10 (by default maps to layer)

  • what – What to plot, count(*) will show a N-d histogram, mean(‘x’), the mean of the x column, sum(‘x’) the sum, std(‘x’) the standard deviation, correlation(‘vx’, ‘vy’) the correlation coefficient. Can also be a list of values, like [‘count(x)’, std(‘vx’)], (by default maps to column)

  • reduce

  • f – transform values by: ‘identity’ does nothing ‘log’ or ‘log10’ will show the log of the value

  • normalize – normalization function, currently only ‘normalize’ is supported

  • normalize_axis – which axes to normalize on, None means normalize by the global maximum.

  • vmin – instead of automatic normalization, (using normalize and normalization_axis) scale the data between vmin and vmax to [0, 1]

  • vmax – see vmin

  • shape – shape/size of the n-D histogram grid

  • limits – list of [[xmin, xmax], [ymin, ymax]], or a description such as ‘minmax’, ‘99%’

  • grid – if the binning is done before by yourself, you can pass it

  • colormap – matplotlib colormap to use

  • figsize – (x, y) tuple passed to pylab.figure for setting the figure size

  • xlabel

  • ylabel

  • aspect

  • tight_layout – call pylab.tight_layout or not

  • colorbar – plot a colorbar or not

  • interpolation – interpolation for imshow, possible options are: ‘nearest’, ‘bilinear’, ‘bicubic’, see matplotlib for more

  • return_extra

Returns

histogram(x=None, what='count(*)', grid=None, shape=64, facet=None, limits=None, figsize=None, f='identity', n=None, normalize_axis=None, xlabel=None, ylabel=None, label=None, selection=None, show=False, tight_layout=True, hardcopy=None, progress=None, **kwargs)

Plot a histogram.

Example:

>>> df.histogram(df.x)
>>> df.histogram(df.x, limits=[0, 100], shape=100)
>>> df.histogram(df.x, what='mean(y)', limits=[0, 100], shape=100)

If you want to do a computation yourself, pass the grid argument, but you are responsible for passing the same limits arguments:

>>> counts = df.mean(df.y, binby=df.x, limits=[0, 100], shape=100)/100.
>>> df.histogram(df.x, limits=[0, 100], shape=100, grid=means, label='mean(y)/100')
Parameters
  • x – Expression to bin in the x direction

  • what – What to plot, count(*) will show a N-d histogram, mean(‘x’), the mean of the x column, sum(‘x’) the sum

  • grid – If the binning is done before by yourself, you can pass it

  • facet – Expression to produce facetted plots ( facet=’x:0,1,12’ will produce 12 plots with x in a range between 0 and 1)

  • limits – list of [xmin, xmax], or a description such as ‘minmax’, ‘99%’

  • figsize – (x, y) tuple passed to pylab.figure for setting the figure size

  • f – transform values by: ‘identity’ does nothing ‘log’ or ‘log10’ will show the log of the value

  • n – normalization function, currently only ‘normalize’ is supported, or None for no normalization

  • normalize_axis – which axes to normalize on, None means normalize by the global maximum.

  • normalize_axis

  • xlabel – String for label on x axis (may contain latex)

  • ylabel – Same for y axis

  • kwargs – extra argument passed to pylab.plot

Param

tight_layout: call pylab.tight_layout or not

Returns

scatter(x, y, xerr=None, yerr=None, cov=None, corr=None, s_expr=None, c_expr=None, labels=None, selection=None, length_limit=50000, length_check=True, label=None, xlabel=None, ylabel=None, errorbar_kwargs={}, ellipse_kwargs={}, **kwargs)

Viz (small amounts) of data in 2d using a scatter plot

Convenience wrapper around pylab.scatter when for working with small DataFrames or selections

Parameters
  • x – Expression for x axis

  • y – Idem for y

  • s_expr – When given, use if for the s (size) argument of pylab.scatter

  • c_expr – When given, use if for the c (color) argument of pylab.scatter

  • labels – Annotate the points with these text values

  • selection – Single selection expression, or None

  • length_limit – maximum number of rows it will plot

  • length_check – should we do the maximum row check or not?

  • label – label for the legend

  • xlabel – label for x axis, if None .label(x) is used

  • ylabel – label for y axis, if None .label(y) is used

  • errorbar_kwargs – extra dict with arguments passed to plt.errorbar

  • kwargs – extra arguments passed to pylab.scatter

Returns

class vaex.viz.ExpressionAccessorViz(expression)[source]

Bases: object

__init__(expression)[source]

Initialize self. See help(type(self)) for accurate signature.

__weakref__

list of weak references to the object (if defined)

histogram(what='count(*)', grid=None, shape=64, facet=None, limits=None, figsize=None, f='identity', n=None, normalize_axis=None, xlabel=None, ylabel=None, label=None, selection=None, show=False, tight_layout=True, hardcopy=None, progress=None, **kwargs)[source]

Plot a histogram of the expression. This is a convenience method for df.histogram(…)

Example:

>>> df.x.histogram()
>>> df.x.histogram(limits=[0, 100], shape=100)
>>> df.x.histogram(what='mean(y)', limits=[0, 100], shape=100)

If you want to do a computation yourself, pass the grid argument, but you are responsible for passing the same limits arguments:

>>> counts = df.mean(df.y, binby=df.x, limits=[0, 100], shape=100)/100.
>>> df.plot1d(df.x, limits=[0, 100], shape=100, grid=means, label='mean(y)/100')
Parameters
  • x – Expression to bin in the x direction

  • what – What to plot, count(*) will show a N-d histogram, mean(‘x’), the mean of the x column, sum(‘x’) the sum

  • grid – If the binning is done before by yourself, you can pass it

  • facet – Expression to produce facetted plots ( facet=’x:0,1,12’ will produce 12 plots with x in a range between 0 and 1)

  • limits – list of [xmin, xmax], or a description such as ‘minmax’, ‘99%’

  • figsize – (x, y) tuple passed to pylab.figure for setting the figure size

  • f – transform values by: ‘identity’ does nothing ‘log’ or ‘log10’ will show the log of the value

  • n – normalization function, currently only ‘normalize’ is supported, or None for no normalization

  • normalize_axis – which axes to normalize on, None means normalize by the global maximum.

  • normalize_axis

  • xlabel – String for label on x axis (may contain latex)

  • ylabel – Same for y axis

  • kwargs – extra argument passed to pylab.plot

Param

tight_layout: call pylab.tight_layout or not

Returns

vaex-jupyter

vaex.jupyter.debounced(delay_seconds=0.5, skip_gather=False, on_error=None, reentrant=True)[source]

A decorator to debounce many method/function call into 1 call.

Note: this only works in an async environment, such as a Jupyter notebook context. Outside of this context, calling flush() will execute pending calls.

Parameters
  • delay_seconds (float) – The amount of seconds that should pass without any call, before the (final) call will be executed.

  • method (bool) – The decorator should know if the callable is a a method or not, otherwise the debounced is on a per-class basis.

  • skip_gather (bool) – The decorated function will be be waited for when calling vaex.jupyter.gather()

  • on_error – callback function that takes an exception as argument.

  • reentrant (bool) – reentrant function or not

vaex.jupyter.flush(recursive_counts=- 1, ignore_exceptions=False, all=False)[source]

Run all non-executed debounced functions.

If execution of debounced calls lead to scheduling of new calls, they will be recursively executed, with a limit or recursive_counts calls. recursive_counts=-1 means infinite.

vaex.jupyter.interactive_selection(df)[source]

vaex.jupyter.model

class vaex.jupyter.model.Axis(**kwargs)[source]

Bases: vaex.jupyter.model._HasState

class Status(value)[source]

Bases: enum.Enum

State transitions NO_LIMITS -> STAGED_CALCULATING_LIMITS -> CALCULATING_LIMITS -> CALCULATED_LIMITS -> READY

when expression changes:
STAGED_CALCULATING_LIMITS:

calculation.cancel() ->NO_LIMITS

CALCULATING_LIMITS:

calculation.cancel() ->NO_LIMITS

when min/max changes:
STAGED_CALCULATING_LIMITS:

calculation.cancel() ->NO_LIMITS

CALCULATING_LIMITS:

calculation.cancel() ->NO_LIMITS

ABORTED = 7
CALCULATED_LIMITS = 4
CALCULATING_LIMITS = 3
EXCEPTION = 6
NO_LIMITS = 1
READY = 5
STAGED_CALCULATING_LIMITS = 2
bin_centers

A trait which allows any value.

computation()[source]
df

A trait whose value must be an instance of a specified class.

The value can also be an instance of a subclass of the specified class.

Subclasses can declare default classes by overriding the klass attribute

exception

A trait which allows any value.

expression
property has_missing_limit
max

A casting version of the float trait.

min

A casting version of the float trait.

on_change_expression(change)[source]
on_change_limits
on_change_shape(change)[source]
on_change_shape_default(change)[source]
shape

A casting version of the int trait.

shape_default

A casting version of the int trait.

slice

A casting version of the int trait.

status

Use a Enum class as model for the data type description. Note that if no default-value is provided, the first enum-value is used as default-value.

# -- SINCE: Python 3.4 (or install backport: pip install enum34)
import enum
from traitlets import HasTraits, UseEnum

class Color(enum.Enum):
    red = 1         # -- IMPLICIT: default_value
    blue = 2
    green = 3

class MyEntity(HasTraits):
    color = UseEnum(Color, default_value=Color.blue)

entity = MyEntity(color=Color.red)
entity.color = Color.green    # USE: Enum-value (preferred)
entity.color = "green"        # USE: name (as string)
entity.color = "Color.green"  # USE: scoped-name (as string)
entity.color = 3              # USE: number (as int)
assert entity.color is Color.green
class vaex.jupyter.model.DataArray(**kwargs)[source]

Bases: vaex.jupyter.model._HasState

class Status(value)[source]

Bases: enum.Enum

An enumeration.

CALCULATED_GRID = 9
CALCULATED_LIMITS = 5
CALCULATING_GRID = 8
CALCULATING_LIMITS = 4
EXCEPTION = 11
MISSING_LIMITS = 1
NEEDS_CALCULATING_GRID = 6
READY = 10
STAGED_CALCULATING_GRID = 7
STAGED_CALCULATING_LIMITS = 3
axes

An instance of a Python list.

df

A trait whose value must be an instance of a specified class.

The value can also be an instance of a subclass of the specified class.

Subclasses can declare default classes by overriding the klass attribute

exception

A trait which allows any value.

grid

A trait whose value must be an instance of a specified class.

The value can also be an instance of a subclass of the specified class.

Subclasses can declare default classes by overriding the klass attribute

grid_sliced

A trait whose value must be an instance of a specified class.

The value can also be an instance of a subclass of the specified class.

Subclasses can declare default classes by overriding the klass attribute

property has_missing_limits
on_progress_grid(f)[source]
selection

A trait which allows any value.

shape

A casting version of the int trait.

status

Use a Enum class as model for the data type description. Note that if no default-value is provided, the first enum-value is used as default-value.

# -- SINCE: Python 3.4 (or install backport: pip install enum34)
import enum
from traitlets import HasTraits, UseEnum

class Color(enum.Enum):
    red = 1         # -- IMPLICIT: default_value
    blue = 2
    green = 3

class MyEntity(HasTraits):
    color = UseEnum(Color, default_value=Color.blue)

entity = MyEntity(color=Color.red)
entity.color = Color.green    # USE: Enum-value (preferred)
entity.color = "green"        # USE: name (as string)
entity.color = "Color.green"  # USE: scoped-name (as string)
entity.color = 3              # USE: number (as int)
assert entity.color is Color.green
status_text

A trait for unicode strings.

class vaex.jupyter.model.GridCalculator(**kwargs)[source]

Bases: vaex.jupyter.model._HasState

A grid is responsible for scheduling the grid calculations and possible slicing

class Status(value)[source]

Bases: enum.Enum

An enumeration.

CALCULATING = 4
READY = 9
STAGED_CALCULATION = 3
VOID = 1
computation()[source]
df

A trait whose value must be an instance of a specified class.

The value can also be an instance of a subclass of the specified class.

Subclasses can declare default classes by overriding the klass attribute

model_add(model)[source]
models

An instance of a Python list.

on_regrid(ignore=None)[source]
progress(f)[source]
reslice(source_model=None)[source]
status

Use a Enum class as model for the data type description. Note that if no default-value is provided, the first enum-value is used as default-value.

# -- SINCE: Python 3.4 (or install backport: pip install enum34)
import enum
from traitlets import HasTraits, UseEnum

class Color(enum.Enum):
    red = 1         # -- IMPLICIT: default_value
    blue = 2
    green = 3

class MyEntity(HasTraits):
    color = UseEnum(Color, default_value=Color.blue)

entity = MyEntity(color=Color.red)
entity.color = Color.green    # USE: Enum-value (preferred)
entity.color = "green"        # USE: name (as string)
entity.color = "Color.green"  # USE: scoped-name (as string)
entity.color = 3              # USE: number (as int)
assert entity.color is Color.green
class vaex.jupyter.model.Heatmap(**kwargs)[source]

Bases: vaex.jupyter.model.DataArray

x

A trait whose value must be an instance of a specified class.

The value can also be an instance of a subclass of the specified class.

Subclasses can declare default classes by overriding the klass attribute

y

A trait whose value must be an instance of a specified class.

The value can also be an instance of a subclass of the specified class.

Subclasses can declare default classes by overriding the klass attribute

class vaex.jupyter.model.Histogram(**kwargs)[source]

Bases: vaex.jupyter.model.DataArray

x

A trait whose value must be an instance of a specified class.

The value can also be an instance of a subclass of the specified class.

Subclasses can declare default classes by overriding the klass attribute

vaex.jupyter.view

class vaex.jupyter.view.DataArray(**kwargs)[source]

Bases: vaex.jupyter.view.ViewBase

Will display a DataArray interactively, with an optional custom display_function.

By default, it will simply display(…) the DataArray, using xarray’s default display mechanism.

Public constructor

clear_output

Clear output each time the data changes

display_function

A trait which allows any value.

matplotlib_autoshow

Will call plt.show() inside output context if open figure handles exist

model

A trait whose value must be an instance of a specified class.

The value can also be an instance of a subclass of the specified class.

Subclasses can declare default classes by overriding the klass attribute

numpy_errstate

Default numpy errstate during display to avoid showing error messsages, see :py:data:`numpy.errstate`_

update_output(change=None)[source]
class vaex.jupyter.view.Heatmap(**kwargs)[source]

Bases: vaex.jupyter.view.ViewBase

Public constructor

TOOLS_SUPPORTED = ['pan-zoom', 'select-rect', 'select-x']
blend

A trait for unicode strings.

colormap

A trait for unicode strings.

dimension_alternative

A trait for unicode strings.

dimension_facets

A trait for unicode strings.

dimension_fade

A trait for unicode strings.

model

A trait whose value must be an instance of a specified class.

The value can also be an instance of a subclass of the specified class.

Subclasses can declare default classes by overriding the klass attribute

normalize

A boolean (True, False) trait.

supports_normalize = False
supports_transforms = True
tool

A trait for unicode strings.

transform

A trait for unicode strings.

update_heatmap(change=None)[source]
class vaex.jupyter.view.Histogram(**kwargs)[source]

Bases: vaex.jupyter.view.ViewBase

Public constructor

TOOLS_SUPPORTED = ['pan-zoom', 'select-x']
create_plot()[source]
dimension_facets

A trait for unicode strings.

dimension_groups

A trait for unicode strings.

dimension_overplot

A trait for unicode strings.

model

A trait whose value must be an instance of a specified class.

The value can also be an instance of a subclass of the specified class.

Subclasses can declare default classes by overriding the klass attribute

normalize

A boolean (True, False) trait.

supports_normalize = True
supports_transforms = False
transform

A trait for unicode strings.

update_data(change=None)[source]
class vaex.jupyter.view.PieChart(**kwargs)[source]

Bases: vaex.jupyter.view.Histogram

Public constructor

create_plot()[source]
radius_split_fraction = 0.8
class vaex.jupyter.view.ViewBase(**kwargs)[source]

Bases: ipyvuetify.generated.Container.Container

Public constructor

hide_progress()[source]
on_grid_progress(fraction)[source]
select_nothing()[source]
select_rectangle(x1, x2, y1, y2)[source]
select_x_range(x1, x2)[source]
selection_interact

A trait for unicode strings.

selection_mode

A trait for unicode strings.

tool

A trait for unicode strings.

vaex.jupyter.widgets

class vaex.jupyter.widgets.ColumnExpressionAdder(**kwargs)[source]

Bases: vaex.jupyter.widgets.ColumnPicker

Public constructor

component

A trait which allows any value.

target

A trait for unicode strings.

vue_menu_click(data)[source]
class vaex.jupyter.widgets.ColumnList(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate, vaex.jupyter.traitlets.ColumnsMixin

Public constructor

column_filter

A trait for unicode strings.

dialog_open

A boolean (True, False) trait.

editor

A trait which allows any value.

editor_open

A boolean (True, False) trait.

template

A trait for unicode strings.

tooltip

A trait for unicode strings.

valid_expression

A boolean (True, False) trait.

vue_add_virtual_column(data)[source]
vue_column_click(data)[source]
vue_save_column(data)[source]
class vaex.jupyter.widgets.ColumnPicker(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate, vaex.jupyter.traitlets.ColumnsMixin

Public constructor

label

A trait for unicode strings.

template

A trait for unicode strings.

value
class vaex.jupyter.widgets.ColumnSelectionAdder(**kwargs)[source]

Bases: vaex.jupyter.widgets.ColumnPicker

Public constructor

component

A trait which allows any value.

target

A trait for unicode strings.

vue_menu_click(data)[source]
class vaex.jupyter.widgets.ContainerCard(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

Public constructor

card_props

An instance of a Python dict.

One or more traits can be passed to the constructor to validate the keys and/or values of the dict. If you need more detailed validation, you may use a custom validator method.

Changed in version 5.0: Added key_trait for validating dict keys.

Changed in version 5.0: Deprecated ambiguous trait, traits args in favor of value_trait, per_key_traits.

controls

An instance of a Python list.

main

A trait which allows any value.

main_props

An instance of a Python dict.

One or more traits can be passed to the constructor to validate the keys and/or values of the dict. If you need more detailed validation, you may use a custom validator method.

Changed in version 5.0: Added key_trait for validating dict keys.

Changed in version 5.0: Deprecated ambiguous trait, traits args in favor of value_trait, per_key_traits.

show_controls

A boolean (True, False) trait.

subtitle

A trait for unicode strings.

text

A trait for unicode strings.

title

A trait for unicode strings.

class vaex.jupyter.widgets.Counter(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

Public constructor

characters

An instance of a Python list.

format

A trait for unicode strings.

postfix

A trait for unicode strings.

prefix

A trait for unicode strings.

template

A trait for unicode strings.

value

An int trait.

class vaex.jupyter.widgets.Expression(**kwargs)[source]

Bases: ipyvuetify.generated.TextField.TextField

Public constructor

check_expression()[source]
df

A trait which allows any value.

valid

A boolean (True, False) trait.

value
class vaex.jupyter.widgets.ExpressionSelectionTextArea(**kwargs)[source]

Bases: vaex.jupyter.widgets.Expression

Public constructor

selection_name

A trait which allows any value.

update_custom_selection
update_selection()[source]
vaex.jupyter.widgets.ExpressionTextArea

alias of vaex.jupyter.widgets.Expression

class vaex.jupyter.widgets.Html(**kwargs)[source]

Bases: ipyvuetify.Html.Html

Public constructor

Bases: vaex.jupyter.widgets.VuetifyTemplate

Public constructor

items

An instance of a Python list.

class vaex.jupyter.widgets.PlotTemplate(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

Public constructor

button_text

A trait for unicode strings.

clipped

A boolean (True, False) trait.

components

An instance of a Python dict.

One or more traits can be passed to the constructor to validate the keys and/or values of the dict. If you need more detailed validation, you may use a custom validator method.

Changed in version 5.0: Added key_trait for validating dict keys.

Changed in version 5.0: Deprecated ambiguous trait, traits args in favor of value_trait, per_key_traits.

dark

A boolean (True, False) trait.

drawer

A boolean (True, False) trait.

drawers

A trait which allows any value.

floating

A boolean (True, False) trait.

items

An instance of a Python list.

mini

A boolean (True, False) trait.

model

A trait which allows any value.

new_output

A boolean (True, False) trait.

show_output

A boolean (True, False) trait.

template

A trait for unicode strings.

title

A trait for unicode strings.

type

A trait for unicode strings.

class vaex.jupyter.widgets.ProgressCircularNoAnimation(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

v-progress-circular that avoids animations

Public constructor

color

A trait for unicode strings.

hidden

A boolean (True, False) trait.

parts

An instance of a Python list.

size

An int trait.

template

A trait for unicode strings.

text

A trait for unicode strings.

value

A float trait.

width

An int trait.

class vaex.jupyter.widgets.Selection(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

Public constructor

df

A trait which allows any value.

name

A trait for unicode strings.

value

A trait for unicode strings.

class vaex.jupyter.widgets.SelectionEditor(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

Public constructor

adder

A trait which allows any value.

components

An instance of a Python dict.

One or more traits can be passed to the constructor to validate the keys and/or values of the dict. If you need more detailed validation, you may use a custom validator method.

Changed in version 5.0: Added key_trait for validating dict keys.

Changed in version 5.0: Deprecated ambiguous trait, traits args in favor of value_trait, per_key_traits.

df

A trait which allows any value.

input

A trait which allows any value.

on_close

A trait which allows any value.

template

A trait for unicode strings.

class vaex.jupyter.widgets.SelectionToggleList(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

Public constructor

df

A trait which allows any value.

selection_names

An instance of a Python list.

title

A trait for unicode strings.

value

An instance of a Python list.

class vaex.jupyter.widgets.Status(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

Public constructor

template

A trait for unicode strings.

value

A trait for unicode strings.

class vaex.jupyter.widgets.ToolsSpeedDial(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

Public constructor

children

An instance of a Python list.

expand

A boolean (True, False) trait.

items

A trait which allows any value.

template

A trait for unicode strings.

value

A trait for unicode strings.

vue_action(data)[source]
class vaex.jupyter.widgets.ToolsToolbar(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

Public constructor

interact_items

A trait which allows any value.

interact_value

A trait for unicode strings.

normalize

A boolean (True, False) trait.

selection_mode

A trait for unicode strings.

selection_mode_items

A trait which allows any value.

supports_normalize

A boolean (True, False) trait.

supports_transforms

A boolean (True, False) trait.

transform_items

An instance of a Python list.

transform_value

A trait for unicode strings.

z_normalize

A boolean (True, False) trait.

class vaex.jupyter.widgets.UsesVaexComponents(**kwargs)[source]

Bases: traitlets.traitlets.HasTraits

class vaex.jupyter.widgets.VirtualColumnEditor(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

Public constructor

adder

A trait which allows any value.

column_name

A trait for unicode strings.

components

An instance of a Python dict.

One or more traits can be passed to the constructor to validate the keys and/or values of the dict. If you need more detailed validation, you may use a custom validator method.

Changed in version 5.0: Added key_trait for validating dict keys.

Changed in version 5.0: Deprecated ambiguous trait, traits args in favor of value_trait, per_key_traits.

df

A trait which allows any value.

editor

A trait which allows any value.

on_close

A trait which allows any value.

save_column()[source]
template

A trait for unicode strings.

class vaex.jupyter.widgets.VuetifyTemplate(**kwargs)[source]

Bases: ipyvuetify.VuetifyTemplate.VuetifyTemplate

Public constructor

vaex.jupyter.widgets.component(name)[source]
vaex.jupyter.widgets.load_template(filename)[source]

Machine learning with vaex.ml

See the ML tutorial an introduction, and the ML examples for more advanced usage.

Scikit-learn

vaex.ml.sklearn.IncrementalPredictor(**kwargs)

This class wraps any scikit-learn estimator (a.k.a predictions) that has a .partial_fit method, and makes it a vaex pipeline object.

vaex.ml.sklearn.Predictor(**kwargs)

This class wraps any scikit-learn estimator (a.k.a predictor) making it a vaex pipeline object.

class vaex.ml.sklearn.IncrementalPredictor(**kwargs)[source]

Bases: vaex.ml.state.HasState

This class wraps any scikit-learn estimator (a.k.a predictions) that has a .partial_fit method, and makes it a vaex pipeline object.

By wrapping “on-line” scikit-learn estimators with this class, they become a vaex pipeline object. Thus, they can take full advantage of the serialization and pipeline system of vaex. While the underlying estimator need to call the .partial_fit method, this class contains the standard .fit method, and the rest happens behind the scenes. One can also iterate over the data multiple times (epochs), and optionally shuffle each batch before it is sent to the estimator. The predict method returns a numpy array, while the transform method adds the prediction as a virtual column to a vaex DataFrame.

Note: the .fit method will use as much memory as needed to copy one batch of data, while the .predict method will require as much memory as needed to output the predictions as a numpy array. The transform method is evaluated lazily, and no memory copies are made.

Note: we are using normal sklearn without modifications here.

Example:

>>> import vaex
>>> import vaex.ml
>>> from vaex.ml.sklearn import IncrementalPredictor
>>> from sklearn.linear_model import SGDRegressor
>>>
>>> df = vaex.example()
>>>
>>> features = df.column_names[:6]
>>> target = 'FeH'
>>>
>>> standard_scaler = vaex.ml.StandardScaler(features=features)
>>> df = standard_scaler.fit_transform(df)
>>>
>>> features = df.get_column_names(regex='^standard')
>>> model = SGDRegressor(learning_rate='constant', eta0=0.01, random_state=42)
>>>
>>> incremental = IncrementalPredictor(model=model,
...                                    features=features,
...                                    target=target,
...                                    batch_size=10_000,
...                                    num_epochs=3,
...                                    shuffle=True,
...                                    prediction_name='pred_FeH')
>>> incremental.fit(df=df)
>>> df = incremental.transform(df)
>>> df.head(5)[['FeH', 'pred_FeH']]
  #        FeH    pred_FeH
  0  -2.30923     -1.66226
  1  -1.78874     -1.68218
  2  -0.761811    -1.59562
  3  -1.52088     -1.62225
  4  -2.65534     -1.61991
Parameters
  • batch_size – Number of samples to be sent to the model in each batch.

  • features – List of features to use.

  • model – A scikit-learn estimator with a .fit_predict method.

  • num_epochs – Number of times each batch is sent to the model.

  • partial_fit_kwargs – A dictionary of key word arguments to be passed on to the fit_predict method of the model.

  • prediction_name – The name of the virtual column housing the predictions.

  • prediction_type – Which method to use to get the predictions. Can be “predict”, “predict_proba” or “predict_log_proba”.

  • shuffle – If True, shuffle the samples before sending them to the model.

  • target – The name of the target column.

batch_size

An int trait.

features

An instance of a Python list.

fit(df, progress=None)[source]

Fit the IncrementalPredictor to the DataFrame.

Parameters
  • df – A vaex DataFrame containing the features and target on which to train the model.

  • progress – If True, display a progressbar which tracks the training progress.

model

A trait which allows any value.

num_epochs

An int trait.

partial_fit_kwargs

An instance of a Python dict.

One or more traits can be passed to the constructor to validate the keys and/or values of the dict. If you need more detailed validation, you may use a custom validator method.

Changed in version 5.0: Added key_trait for validating dict keys.

Changed in version 5.0: Deprecated ambiguous trait, traits args in favor of value_trait, per_key_traits.

predict(df)[source]

Get an in-memory numpy array with the predictions of the Predictor

Parameters

df – A vaex DataFrame, containing the input features.

Returns

A in-memory numpy array containing the Predictor predictions.

Return type

numpy.array

prediction_name

A trait for unicode strings.

prediction_type

An enum whose value must be in a given sequence.

shuffle

A boolean (True, False) trait.

target

A trait for unicode strings.

transform(df)[source]

Transform a DataFrame such that it contains the predictions of the IncrementalPredictor. in form of a virtual column.

Parameters

df – A vaex DataFrame.

Return copy

A shallow copy of the DataFrame that includes the IncrementalPredictor prediction as a virtual column.

Return type

DataFrame

class vaex.ml.sklearn.Predictor(**kwargs)[source]

Bases: vaex.ml.state.HasState

This class wraps any scikit-learn estimator (a.k.a predictor) making it a vaex pipeline object.

By wrapping any scikit-learn estimators with this class, it becomes a vaex pipeline object. Thus, it can take full advantage of the serialization and pipeline system of vaex. One can use the predict method to get a numpy array as an output of a fitted estimator, or the transform method do add such a prediction to a vaex DataFrame as a virtual column.

Note that a full memory copy of the data used is created when the fit and predict are called. The transform method is evaluated lazily.

The scikit-learn estimators themselves are not modified at all, they are taken from your local installation of scikit-learn.

Example:

>>> import vaex.ml
>>> from vaex.ml.sklearn import Predictor
>>> from sklearn.linear_model import LinearRegression
>>> df = vaex.ml.datasets.load_iris()
>>> features = ['sepal_width', 'petal_length', 'sepal_length']
>>> df_train, df_test = df.ml.train_test_split()
>>> model = Predictor(model=LinearRegression(), features=features, target='petal_width', prediction_name='pred')
>>> model.fit(df_train)
>>> df_train = model.transform(df_train)
>>> df_train.head(3)
 #    sepal_length    sepal_width    petal_length    petal_width    class_      pred
 0             5.4            3               4.5            1.5         1  1.64701
 1             4.8            3.4             1.6            0.2         0  0.352236
 2             6.9            3.1             4.9            1.5         1  1.59336
>>> df_test = model.transform(df_test)
>>> df_test.head(3)
 #    sepal_length    sepal_width    petal_length    petal_width    class_     pred
 0             5.9            3               4.2            1.5         1  1.39437
 1             6.1            3               4.6            1.4         1  1.56469
 2             6.6            2.9             4.6            1.3         1  1.44276
Parameters
  • features – List of features to use.

  • model – A scikit-learn estimator.

  • prediction_name – The name of the virtual column housing the predictions.

  • prediction_type – Which method to use to get the predictions. Can be “predict”, “predict_proba” or “predict_log_proba”.

  • target – The name of the target column.

features

An instance of a Python list.

fit(df, **kwargs)[source]

Fit the Predictor to the DataFrame.

Parameters

df – A vaex DataFrame containing the features and target on which to train the model.

model

A trait which allows any value.

predict(df)[source]

Get an in-memory numpy array with the predictions of the Predictor.

Parameters

df – A vaex DataFrame, containing the input features.

Returns

A in-memory numpy array containing the Predictor predictions.

Return type

numpy.array

prediction_name

A trait for unicode strings.

prediction_type

An enum whose value must be in a given sequence.

snake_name = 'sklearn_predictor'
target

A trait for unicode strings.

transform(df)[source]

Transform a DataFrame such that it contains the predictions of the Predictor. in form of a virtual column.

Parameters

df – A vaex DataFrame.

Return copy

A shallow copy of the DataFrame that includes the Predictor prediction as a virtual column.

Return type

DataFrame

Clustering

vaex.ml.cluster.KMeans(**kwargs)

The KMeans clustering algorithm.

class vaex.ml.cluster.KMeans(**kwargs)[source]

Bases: vaex.ml.state.HasState

The KMeans clustering algorithm.

Example:

>>> import vaex.ml
>>> import vaex.ml.cluster
>>> df = vaex.ml.datasets.load_iris()
>>> features = ['sepal_width', 'petal_length', 'sepal_length', 'petal_width']
>>> cls = vaex.ml.cluster.KMeans(n_clusters=3, features=features, init='random', max_iter=10)
>>> cls.fit(df)
>>> df = cls.transform(df)
>>> df.head(5)
 #    sepal_width    petal_length    sepal_length    petal_width    class_    prediction_kmeans
 0            3               4.2             5.9            1.5         1                    2
 1            3               4.6             6.1            1.4         1                    2
 2            2.9             4.6             6.6            1.3         1                    2
 3            3.3             5.7             6.7            2.1         2                    0
 4            4.2             1.4             5.5            0.2         0                    1
Parameters
  • cluster_centers – Coordinates of cluster centers.

  • features – List of features to cluster.

  • inertia – Sum of squared distances of samples to their closest cluster center.

  • init – Method for initializing the centroids.

  • max_iter – Maximum number of iterations of the KMeans algorithm for a single run.

  • n_clusters – Number of clusters to form.

  • n_init – Number of centroid initializations. The KMeans algorithm will be run for each initialization, and the final results will be the best output of the n_init consecutive runs in terms of inertia.

  • prediction_label – The name of the virtual column that houses the cluster labels for each point.

  • random_state – Random number generation for centroid initialization. If an int is specified, the randomness becomes deterministic.

  • verbose – If True, enable verbosity mode.

fit(dataframe)[source]

Fit the KMeans model to the dataframe.

Parameters

dataframe – A vaex DataFrame.

transform(dataframe)[source]

Label a DataFrame with a fitted KMeans model.

Parameters

dataframe – A vaex DataFrame.

Returns copy

A shallow copy of the DataFrame that includes the cluster labels.

Return type

DataFrame

Transformers/encoders

vaex.ml.transformations.FrequencyEncoder(…)

Encode categorical columns by the frequency of their respective samples.

vaex.ml.transformations.LabelEncoder(**kwargs)

Encode categorical columns with integer values between 0 and num_classes-1.

vaex.ml.transformations.MaxAbsScaler(**kwargs)

Scale features by their maximum absolute value.

vaex.ml.transformations.MinMaxScaler(**kwargs)

Will scale a set of features to a given range.

vaex.ml.transformations.OneHotEncoder(**kwargs)

Encode categorical columns according ot the One-Hot scheme.

vaex.ml.transformations.PCA(**kwargs)

Transform a set of features using a Principal Component Analysis.

vaex.ml.transformations.RobustScaler(**kwargs)

The RobustScaler removes the median and scales the data according to a given percentile range.

vaex.ml.transformations.StandardScaler(**kwargs)

Standardize features by removing thir mean and scaling them to unit variance.

vaex.ml.transformations.CycleTransformer(…)

A strategy for transforming cyclical features (e.g.

vaex.ml.transformations.BayesianTargetEncoder(…)

Encode categorical variables with a Bayesian Target Encoder.

vaex.ml.transformations.WeightOfEvidenceEncoder(…)

Encode categorical variables with a Weight of Evidence Encoder.

vaex.ml.transformations.KBinsDiscretizer(…)

Bin continous features into discrete bins.

vaex.ml.transformations.GroupByTransformer(…)

The GroupByTransformer creates aggregations via the groupby operation, which are joined to a DataFrame.

class vaex.ml.transformations.FrequencyEncoder(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

Encode categorical columns by the frequency of their respective samples.

Example:

>>> import vaex
>>> df = vaex.from_arrays(color=['red', 'green', 'green', 'blue', 'red', 'green'])
>>> df
 #  color
 0  red
 1  green
 2  green
 3  blue
 4  red
>>> encoder = vaex.ml.FrequencyEncoder(features=['color'])
>>> encoder.fit_transform(df)
 #  color      frequency_encoded_color
 0  red                       0.333333
 1  green                     0.5
 2  green                     0.5
 3  blue                      0.166667
 4  red                       0.333333
 5  green                     0.5
Parameters
  • features – List of features to transform.

  • prefix – Prefix for the names of the transformed features.

  • unseen – Strategy to deal with unseen values.

fit(df)[source]

Fit FrequencyEncoder to the DataFrame.

Parameters

df – A vaex DataFrame.

transform(df)[source]

Transform a DataFrame with a fitted FrequencyEncoder.

Parameters

df – A vaex DataFrame.

Returns

A shallow copy of the DataFrame that includes the encodings.

Return type

DataFrame

class vaex.ml.transformations.LabelEncoder(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

Encode categorical columns with integer values between 0 and num_classes-1.

Example:

>>> import vaex
>>> df = vaex.from_arrays(color=['red', 'green', 'green', 'blue', 'red'])
>>> df
 #  color
 0  red
 1  green
 2  green
 3  blue
 4  red
>>> encoder = vaex.ml.LabelEncoder(features=['color'])
>>> encoder.fit_transform(df)
 #  color      label_encoded_color
 0  red                          2
 1  green                        1
 2  green                        1
 3  blue                         0
 4  red                          2
Parameters
  • allow_unseen – If True, unseen values will be encoded with -1, otherwise an error is raised

  • features – List of features to transform.

  • prefix – Prefix for the names of the transformed features.

fit(df)[source]

Fit LabelEncoder to the DataFrame.

Parameters

df – A vaex DataFrame.

transform(df)[source]

Transform a DataFrame with a fitted LabelEncoder.

Parameters

df – A vaex DataFrame.

Returns: :return copy: A shallow copy of the DataFrame that includes the encodings. :rtype: DataFrame

class vaex.ml.transformations.MaxAbsScaler(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

Scale features by their maximum absolute value.

Example:

>>> import vaex
>>> df = vaex.from_arrays(x=[2,5,7,2,15], y=[-2,3,0,0,10])
>>> df
 #    x    y
 0    2   -2
 1    5    3
 2    7    0
 3    2    0
 4   15   10
>>> scaler = vaex.ml.MaxAbsScaler(features=['x', 'y'])
>>> scaler.fit_transform(df)
 #    x    y    absmax_scaled_x    absmax_scaled_y
 0    2   -2           0.133333               -0.2
 1    5    3           0.333333                0.3
 2    7    0           0.466667                0
 3    2    0           0.133333                0
 4   15   10           1                       1
Parameters
  • features – List of features to transform.

  • prefix – Prefix for the names of the transformed features.

fit(df)[source]

Fit MinMaxScaler to the DataFrame.

Parameters

df – A vaex DataFrame.

transform(df)[source]

Transform a DataFrame with a fitted MaxAbsScaler.

Parameters

df – A vaex DataFrame.

Return copy

a shallow copy of the DataFrame that includes the scaled features.

Return type

DataFrame

class vaex.ml.transformations.MinMaxScaler(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

Will scale a set of features to a given range.

Example:

>>> import vaex
>>> df = vaex.from_arrays(x=[2,5,7,2,15], y=[-2,3,0,0,10])
>>> df
 #    x    y
 0    2   -2
 1    5    3
 2    7    0
 3    2    0
 4   15   10
>>> scaler = vaex.ml.MinMaxScaler(features=['x', 'y'])
>>> scaler.fit_transform(df)
 #    x    y    minmax_scaled_x    minmax_scaled_y
 0    2   -2           0                  0
 1    5    3           0.230769           0.416667
 2    7    0           0.384615           0.166667
 3    2    0           0                  0.166667
 4   15   10           1                  1
Parameters
  • feature_range – The range the features are scaled to.

  • features – List of features to transform.

  • prefix – Prefix for the names of the transformed features.

fit(df)[source]

Fit MinMaxScaler to the DataFrame.

Parameters

df – A vaex DataFrame.

transform(df)[source]

Transform a DataFrame with a fitted MinMaxScaler.

Parameters

df – A vaex DataFrame.

Return copy

a shallow copy of the DataFrame that includes the scaled features.

Return type

DataFrame

class vaex.ml.transformations.OneHotEncoder(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

Encode categorical columns according ot the One-Hot scheme.

Example:

>>> import vaex
>>> df = vaex.from_arrays(color=['red', 'green', 'green', 'blue', 'red'])
>>> df
 #  color
 0  red
 1  green
 2  green
 3  blue
 4  red
>>> encoder = vaex.ml.OneHotEncoder(features=['color'])
>>> encoder.fit_transform(df)
 #  color      color_blue    color_green    color_red
 0  red                 0              0            1
 1  green               0              1            0
 2  green               0              1            0
 3  blue                1              0            0
 4  red                 0              0            1
Parameters
  • features – List of features to transform.

  • one – Value to encode when a category is present.

  • prefix – Prefix for the names of the transformed features.

  • zero – Value to encode when category is absent.

fit(df)[source]

Fit OneHotEncoder to the DataFrame.

Parameters

df – A vaex DataFrame.

transform(df)[source]

Transform a DataFrame with a fitted OneHotEncoder.

Parameters

df – A vaex DataFrame.

Returns

A shallow copy of the DataFrame that includes the encodings.

Return type

DataFrame

class vaex.ml.transformations.PCA(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

Transform a set of features using a Principal Component Analysis.

Example:

>>> import vaex
>>> df = vaex.from_arrays(x=[2,5,7,2,15], y=[-2,3,0,0,10])
>>> df
 #   x   y
 0   2   -2
 1   5   3
 2   7   0
 3   2   0
 4   15  10
>>> pca = vaex.ml.PCA(n_components=2, features=['x', 'y'])
>>> pca.fit_transform(df)
 #    x    y       PCA_0      PCA_1
 0    2   -2    5.92532    0.413011
 1    5    3    0.380494  -1.39112
 2    7    0    0.840049   2.18502
 3    2    0    4.61287   -1.09612
 4   15   10  -11.7587    -0.110794
Parameters
  • features – List of features to transform.

  • n_components – Number of components to retain. If None, all the components will be retained.

  • prefix – Prefix for the names of the transformed features.

  • progress – If True, display a progressbar of the PCA fitting process.

fit(df)[source]

Fit the PCA model to the DataFrame.

Parameters

df – A vaex DataFrame.

transform(df, n_components=None)[source]

Apply the PCA transformation to the DataFrame.

Parameters
  • df – A vaex DataFrame.

  • n_components – The number of PCA components to retain.

Return copy

A shallow copy of the DataFrame that includes the PCA components.

Return type

DataFrame

class vaex.ml.transformations.RobustScaler(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

The RobustScaler removes the median and scales the data according to a given percentile range. By default, the scaling is done between the 25th and the 75th percentile. Centering and scaling happens independently for each feature (column).

Example:

>>> import vaex
>>> df = vaex.from_arrays(x=[2,5,7,2,15], y=[-2,3,0,0,10])
>>> df
 #    x    y
 0    2   -2
 1    5    3
 2    7    0
 3    2    0
 4   15   10
>>> scaler = vaex.ml.MaxAbsScaler(features=['x', 'y'])
>>> scaler.fit_transform(df)
 #    x    y    robust_scaled_x    robust_scaled_y
 0    2   -2       -0.333686             -0.266302
 1    5    3       -0.000596934           0.399453
 2    7    0        0.221462              0
 3    2    0       -0.333686              0
 4   15   10        1.1097                1.33151
Parameters
  • features – List of features to transform.

  • percentile_range – The percentile range to which to scale each feature to.

  • prefix – Prefix for the names of the transformed features.

  • with_centering – If True, remove the median.

  • with_scaling – If True, scale each feature between the specified percentile range.

fit(df)[source]

Fit RobustScaler to the DataFrame.

Parameters

df – A vaex DataFrame.

transform(df)[source]

Transform a DataFrame with a fitted RobustScaler.

Parameters

df – A vaex DataFrame.

Returns copy

a shallow copy of the DataFrame that includes the scaled features.

Return type

DataFrame

class vaex.ml.transformations.StandardScaler(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

Standardize features by removing thir mean and scaling them to unit variance.

Example:

>>> import vaex
>>> df = vaex.from_arrays(x=[2,5,7,2,15], y=[-2,3,0,0,10])
>>> df
 #    x    y
 0    2   -2
 1    5    3
 2    7    0
 3    2    0
 4   15   10
>>> scaler = vaex.ml.StandardScaler(features=['x', 'y'])
>>> scaler.fit_transform(df)
 #    x    y    standard_scaled_x    standard_scaled_y
 0    2   -2            -0.876523            -0.996616
 1    5    3            -0.250435             0.189832
 2    7    0             0.166957            -0.522037
 3    2    0            -0.876523            -0.522037
 4   15   10             1.83652              1.85086
Parameters
  • features – List of features to transform.

  • prefix – Prefix for the names of the transformed features.

  • with_mean – If True, remove the mean from each feature.

  • with_std – If True, scale each feature to unit variance.

fit(df)[source]

Fit StandardScaler to the DataFrame.

Parameters

df – A vaex DataFrame.

transform(df)[source]

Transform a DataFrame with a fitted StandardScaler.

Parameters

df – A vaex DataFrame.

Returns copy

a shallow copy of the DataFrame that includes the scaled features.

Return type

DataFrame

class vaex.ml.transformations.CycleTransformer(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

A strategy for transforming cyclical features (e.g. angles, time).

Think of each feature as an angle of a unit circle in polar coordinates, and then and then obtaining the x and y coordinate projections, or the cos and sin components respectively.

Suitable for a variaty of machine learning tasks. It preserves the cyclical continuity of the feature. Inspired by: http://blog.davidkaleko.com/feature-engineering-cyclical-features.html >>> df = vaex.from_arrays(days=[0, 1, 2, 3, 4, 5, 6]) >>> cyctrans = vaex.ml.CycleTransformer(n=7, features=[‘days’]) >>> cyctrans.fit_transform(df)

# days days_x days_y 0 0 1 0 1 1 0.62349 0.781831 2 2 -0.222521 0.974928 3 3 -0.900969 0.433884 4 4 -0.900969 -0.433884 5 5 -0.222521 -0.974928 6 6 0.62349 -0.781831

Parameters
  • features – List of features to transform.

  • n – The number of elements in one cycle.

  • prefix_x – Prefix for the x-component of the transformed features.

  • prefix_y – Prefix for the y-component of the transformed features.

  • suffix_x – Suffix for the x-component of the transformed features.

  • suffix_y – Suffix for the y-component of the transformed features.

fit(df)[source]

Fit a CycleTransformer to the DataFrame.

This is a dummy method, as it is not needed for the transformation to be applied.

Parameters

df – A vaex DataFrame.

transform(df)[source]

Transform a DataFrame with a CycleTransformer.

Parameters

df – A vaex DataFrame.

class vaex.ml.transformations.BayesianTargetEncoder(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

Encode categorical variables with a Bayesian Target Encoder.

The categories are encoded by the mean of their target value, which is adjusted by the global mean value of the target variable using a Bayesian schema. For a larger weight value, the target encodings are smoothed toward the global mean, while for a weight of 0, the encodings are just the mean target value per class.

Reference: https://www.wikiwand.com/en/Bayes_estimator#/Practical_example_of_Bayes_estimators

Example:

>>> import vaex
>>> import vaex.ml
>>> df = vaex.from_arrays(x=['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'],
...                       y=[1, 1, 1, 0, 0, 0, 0, 1])
>>> target_encoder = vaex.ml.BayesianTargetEncoder(features=['x'], weight=4)
>>> target_encoder.fit_transform(df, 'y')
  #  x      y    mean_encoded_x
  0  a      1             0.625
  1  a      1             0.625
  2  a      1             0.625
  3  a      0             0.625
  4  b      0             0.375
  5  b      0             0.375
  6  b      0             0.375
  7  b      1             0.375
Parameters
  • features – List of features to transform.

  • prefix – Prefix for the names of the transformed features.

  • target – The name of the column containing the target variable.

  • unseen – Strategy to deal with unseen values.

  • weight – Weight to be applied to the mean encodings (smoothing parameter).

fit(df)[source]

Fit a BayesianTargetEncoder to the DataFrame.

Parameters

df – A vaex DataFrame

transform(df)[source]

Transform a DataFrame with a fitted BayesianTargetEncoder.

Parameters

df – A vaex DataFrame.

Returns

A shallow copy of the DataFrame that includes the encodings.

Return type

DataFrame

class vaex.ml.transformations.WeightOfEvidenceEncoder(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

Encode categorical variables with a Weight of Evidence Encoder.

Weight of Evidence measures how well a particular feature supports the given hypothesis (i.e. the target variable). With this encoder, each category in a categorical feature is encoded by its “strength” i.e. Weight of Evidence value. The target feature can be a boolean or numerical column, where True/1 is seen as ‘Good’, and False/0 is seen as ‘Bad’

Reference: https://www.listendata.com/2015/03/weight-of-evidence-woe-and-information.html

Example:

>>> import vaex
>>> import vaex.ml
>>> df = vaex.from_arrays(x=['a', 'a', 'b', 'b', 'b', 'c', 'c'],
...                       y=[1, 1, 0, 0, 1, 1, 0])
>>> woe_encoder = vaex.ml.WeightOfEvidenceEncoder(target='y', features=['x'])
>>> woe_encoder.fit_transform(df)
  #  x      y    mean_encoded_x
  0  a      1         13.8155
  1  a      1         13.8155
  2  b      0         -0.693147
  3  b      0         -0.693147
  4  b      1         -0.693147
  5  c      1          0
  6  c      0          0
Parameters
  • epsilon – Small value taken as minimum fot the negatives, to avoid a division by zero

  • features – List of features to transform.

  • prefix – Prefix for the names of the transformed features.

  • target – The name of the column containing the target variable.

  • unseen – Strategy to deal with unseen values.

fit(df)[source]

Fit a WeightOfEvidenceEncoder to the DataFrame.

Parameters

df – A vaex DataFrame

transform(df)[source]

Transform a DataFrame with a fitted WeightOfEvidenceEncoder.

Parameters

df – A vaex DataFrame.

Returns

A shallow copy of the DataFrame that includes the encodings.

Return type

DataFrame

class vaex.ml.transformations.KBinsDiscretizer(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

Bin continous features into discrete bins.

A stretegy to encode continuous features into discrete bins. The transformed columns contain the bin label each sample falls into. In a way this transformer Label/Ordinal encodes continous features.

Example:

>>> import vaex
>>> import vaex.ml
>>> df = vaex.from_arrays(x=[0, 2.5, 5, 7.5, 10, 12.5, 15])
>>> bin_trans = vaex.ml.KBinsDiscretizer(features=['x'], n_bins=3, strategy='uniform')
>>> bin_trans.fit_transform(df)
  #     x    binned_x
  0   0             0
  1   2.5           0
  2   5             1
  3   7.5           1
  4  10             2
  5  12.5           2
  6  15             2
Parameters
  • epsilon – Tiny value added to the bin edges ensuring samples close to the bin edges are binned correcly.

  • features – List of features to transform.

  • n_bins – Number of bins. Must be greater than 1.

  • prefix – Prefix for the names of the transformed features.

  • strategy – Strategy used to define the widths of the bins.

fit(df)[source]

Fit KBinsDiscretizer to the DataFrame.

Parameters

df – A vaex DataFrame.

transform(df)[source]

Transform a DataFrame with a fitted KBinsDiscretizer.

Parameters

df – A vaex DataFrame.

Returns copy

a shallow copy of the DataFrame that includes the binned features.

Return type

DataFrame

class vaex.ml.transformations.GroupByTransformer(**kwargs)[source]

Bases: vaex.ml.transformations.Transformer

The GroupByTransformer creates aggregations via the groupby operation, which are joined to a DataFrame. This is useful for creating aggregate features.

Example:

>>> import vaex
>>> import vaex.ml
>>> df_train = vaex.from_arrays(x=['dog', 'dog', 'dog', 'cat', 'cat'], y=[2, 3, 4, 10, 20])
>>> df_test = vaex.from_arrays(x=['dog', 'cat', 'dog', 'mouse'], y=[5, 5, 5, 5])
>>> group_trans = vaex.ml.GroupByTransformer(by='x', agg={'mean_y': vaex.agg.mean('y')}, rsuffix='_agg')
>>> group_trans.fit_transform(df_train)
  #  x      y  x_agg      mean_y
  0  dog    2  dog             3
  1  dog    3  dog             3
  2  dog    4  dog             3
  3  cat   10  cat            15
  4  cat   20  cat            15
>>> group_trans.transform(df_test)
  #  x        y  x_agg    mean_y
  0  dog      5  dog      3.0
  1  cat      5  cat      15.0
  2  dog      5  dog      3.0
  3  mouse    5  --       --
Parameters
  • agg – Dict where the keys are feature names and the values are vaex.agg objects.

  • by – The feature on which to do the grouping.

  • features – List of features to transform.

  • rprefix – Prefix for the names of the aggregate features in case of a collision.

  • rsuffix – Suffix for the names of the aggregate features in case of a collision.

fit(df)[source]

Fit GroupByTransformer to the DataFrame.

Parameters

df – A vaex DataFrame.

transform(df)[source]

Transform a DataFrame with a fitted GroupByTransformer.

Parameters

df – A vaex DataFrame.

Returns copy

a shallow copy of the DataFrame that includes the aggregated features.

Return type

DataFrame

Boosted trees

vaex.ml.lightgbm.LightGBMModel(**kwargs)

The LightGBM algorithm.

vaex.ml.xgboost.XGBoostModel(**kwargs)

The XGBoost algorithm.

vaex.ml.catboost.CatBoostModel(**kwargs)

The CatBoost algorithm.

class vaex.ml.lightgbm.LightGBMModel(**kwargs)[source]

Bases: vaex.ml.state.HasState

The LightGBM algorithm.

This class provides an interface to the LightGBM algorithm, with some optimizations for better memory efficiency when training large datasets. The algorithm itself is not modified at all.

LightGBM is a fast gradient boosting algorithm based on decision trees and is mainly used for classification, regression and ranking tasks. It is under the umbrella of the Distributed Machine Learning Toolkit (DMTK) project of Microsoft. For more information, please visit https://github.com/Microsoft/LightGBM/.

Example:

>>> import vaex.ml
>>> import vaex.ml.lightgbm
>>> df = vaex.ml.datasets.load_iris()
>>> features = ['sepal_width', 'petal_length', 'sepal_length', 'petal_width']
>>> df_train, df_test = df.ml.train_test_split()
>>> params = {
    'boosting': 'gbdt',
    'max_depth': 5,
    'learning_rate': 0.1,
    'application': 'multiclass',
    'num_class': 3,
    'subsample': 0.80,
    'colsample_bytree': 0.80}
>>> booster = vaex.ml.lightgbm.LightGBMModel(features=features, target='class_', num_boost_round=100, params=params)
>>> booster.fit(df_train)
>>> df_train = booster.transform(df_train)
>>> df_train.head(3)
 #    sepal_width    petal_length    sepal_length    petal_width    class_    lightgbm_prediction
 0            3               4.5             5.4            1.5         1    [0.00165619 0.98097899 0.01736482]
 1            3.4             1.6             4.8            0.2         0    [9.99803930e-01 1.17346471e-04 7.87235133e-05]
 2            3.1             4.9             6.9            1.5         1    [0.00107541 0.9848717  0.01405289]
>>> df_test = booster.transform(df_test)
>>> df_test.head(3)
 #    sepal_width    petal_length    sepal_length    petal_width    class_    lightgbm_prediction
 0            3               4.2             5.9            1.5         1    [0.00208904 0.9821348  0.01577616]
 1            3               4.6             6.1            1.4         1    [0.00182039 0.98491357 0.01326604]
 2            2.9             4.6             6.6            1.3         1    [2.50915444e-04 9.98431777e-01 1.31730785e-03]
Parameters
  • features – List of features to use when fitting the LightGBMModel.

  • num_boost_round – Number of boosting iterations.

  • params – parameters to be passed on the to the LightGBM model.

  • prediction_name – The name of the virtual column housing the predictions.

  • target – The name of the target column.

fit(df, valid_sets=None, valid_names=None, early_stopping_rounds=None, evals_result=None, verbose_eval=None, **kwargs)[source]

Fit the LightGBMModel to the DataFrame.

The model will train until the validation score stops improving. Validation score needs to improve at least every early_stopping_rounds rounds to continue training. Requires at least one validation DataFrame, metric specified. If there’s more than one, will check all of them, but the training data is ignored anyway. If early stopping occurs, the model will add best_iteration field to the booster object.

Parameters
  • df – A vaex DataFrame containing the features and target on which to train the model.

  • valid_sets (list) – A list of DataFrames to be used for validation.

  • valid_names (list) – A list of strings to label the validation sets.

  • int (early_stopping_rounds) – Activates early stopping.

  • evals_result (dict) – A dictionary storing the evaluation results of all valid_sets.

  • verbose_eval (bool) – Requires at least one item in valid_sets. If verbose_eval is True then the evaluation metric on the validation set is printed at each boosting stage.

predict(df, **kwargs)[source]

Get an in-memory numpy array with the predictions of the LightGBMModel on a vaex DataFrame. This method accepts the key word arguments of the predict method from LightGBM.

Parameters

df – A vaex DataFrame.

Returns

A in-memory numpy array containing the LightGBMModel predictions.

Return type

numpy.array

transform(df)[source]

Transform a DataFrame such that it contains the predictions of the LightGBMModel in form of a virtual column.

Parameters

df – A vaex DataFrame.

Return copy

A shallow copy of the DataFrame that includes the LightGBMModel prediction as a virtual column.

Return type

DataFrame

class vaex.ml.xgboost.XGBoostModel(**kwargs)[source]

Bases: vaex.ml.state.HasState

The XGBoost algorithm.

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solves many data science problems in a fast and accurate way. (https://github.com/dmlc/xgboost)

Example:

>>> import vaex
>>> import vaex.ml.xgboost
>>> df = vaex.ml.datasets.load_iris()
>>> features = ['sepal_width', 'petal_length', 'sepal_length', 'petal_width']
>>> df_train, df_test = df.ml.train_test_split()
>>> params = {
    'max_depth': 5,
    'learning_rate': 0.1,
    'objective': 'multi:softmax',
    'num_class': 3,
    'subsample': 0.80,
    'colsample_bytree': 0.80,
    'silent': 1}
>>> booster = vaex.ml.xgboost.XGBoostModel(features=features, target='class_', num_boost_round=100, params=params)
>>> booster.fit(df_train)
>>> df_train = booster.transform(df_train)
>>> df_train.head(3)
#    sepal_length    sepal_width    petal_length    petal_width    class_    xgboost_prediction
0             5.4            3               4.5            1.5         1                     1
1             4.8            3.4             1.6            0.2         0                     0
2             6.9            3.1             4.9            1.5         1                     1
>>> df_test = booster.transform(df_test)
>>> df_test.head(3)
#    sepal_length    sepal_width    petal_length    petal_width    class_    xgboost_prediction
0             5.9            3               4.2            1.5         1                     1
1             6.1            3               4.6            1.4         1                     1
2             6.6            2.9             4.6            1.3         1                     1
Parameters
  • features – List of features to use when fitting the XGBoostModel.

  • num_boost_round – Number of boosting iterations.

  • params – A dictionary of parameters to be passed on to the XGBoost model.

  • prediction_name – The name of the virtual column housing the predictions.

  • target – The name of the target column.

fit(df, evals=(), early_stopping_rounds=None, evals_result=None, verbose_eval=False, **kwargs)[source]

Fit the XGBoost model given a DataFrame.

This method accepts all key word arguments for the xgboost.train method.

Parameters
  • df – A vaex DataFrame containing the features and target on which to train the model.

  • evals – A list of pairs (DataFrame, string). List of items to be evaluated during training, this allows user to watch performance on the validation set.

  • early_stopping_rounds (int) – Activates early stopping. Validation error needs to decrease at least every early_stopping_rounds round(s) to continue training. Requires at least one item in evals. If there’s more than one, will use the last. Returns the model from the last iteration (not the best one).

  • evals_result (dict) – A dictionary storing the evaluation results of all the items in evals.

  • verbose_eval (bool) – Requires at least one item in evals. If verbose_eval is True then the evaluation metric on the validation set is printed at each boosting stage.

predict(df, **kwargs)[source]

Provided a vaex DataFrame, get an in-memory numpy array with the predictions from the XGBoost model. This method accepts the key word arguments of the predict method from XGBoost.

Returns

A in-memory numpy array containing the XGBoostModel predictions.

Return type

numpy.array

transform(df)[source]

Transform a DataFrame such that it contains the predictions of the XGBoostModel in form of a virtual column.

Parameters

df – A vaex DataFrame. It should have the same columns as the DataFrame used to train the model.

Return copy

A shallow copy of the DataFrame that includes the XGBoostModel prediction as a virtual column.

Return type

DataFrame

class vaex.ml.catboost.CatBoostModel(**kwargs)[source]

Bases: vaex.ml.state.HasState

The CatBoost algorithm.

This class provides an interface to the CatBoost aloritham. CatBoost is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks. For more information please visit https://github.com/catboost/catboost

Example:

>>> import vaex
>>> import vaex.ml.catboost
>>> df = vaex.ml.datasets.load_iris()
>>> features = ['sepal_width', 'petal_length', 'sepal_length', 'petal_width']
>>> df_train, df_test = df.ml.train_test_split()
>>> params = {
    'leaf_estimation_method': 'Gradient',
    'learning_rate': 0.1,
    'max_depth': 3,
    'bootstrap_type': 'Bernoulli',
    'objective': 'MultiClass',
    'eval_metric': 'MultiClass',
    'subsample': 0.8,
    'random_state': 42,
    'verbose': 0}
>>> booster = vaex.ml.catboost.CatBoostModel(features=features, target='class_', num_boost_round=100, params=params)
>>> booster.fit(df_train)
>>> df_train = booster.transform(df_train)
>>> df_train.head(3)
#    sepal_length    sepal_width    petal_length    petal_width    class_  catboost_prediction
0             5.4            3               4.5            1.5         1  [0.00615039 0.98024259 0.01360702]
1             4.8            3.4             1.6            0.2         0  [0.99034267 0.00526382 0.0043935 ]
2             6.9            3.1             4.9            1.5         1  [0.00688241 0.95190908 0.04120851]
>>> df_test = booster.transform(df_test)
>>> df_test.head(3)
#    sepal_length    sepal_width    petal_length    petal_width    class_  catboost_prediction
0             5.9            3               4.2            1.5         1  [0.00464228 0.98883351 0.00652421]
1             6.1            3               4.6            1.4         1  [0.00350424 0.9882139  0.00828186]
2             6.6            2.9             4.6            1.3         1  [0.00325705 0.98891631 0.00782664]
Parameters
  • batch_size – If provided, will train in batches of this size.

  • batch_weights – Weights to sum models at the end of training in batches.

  • ctr_merge_policy – Strategy for summing up models. Only used when training in batches. See the CatBoost documentation for more info.

  • evals_result – Evaluation results

  • features – List of features to use when fitting the CatBoostModel.

  • num_boost_round – Number of boosting iterations.

  • params – A dictionary of parameters to be passed on to the CatBoostModel model.

  • pool_params – A dictionary of parameters to be passed to the Pool data object construction

  • prediction_name – The name of the virtual column housing the predictions.

  • prediction_type – The form of the predictions. Can be “RawFormulaVal”, “Probability” or “Class”.

  • target – The name of the target column.

fit(df, evals=None, early_stopping_rounds=None, verbose_eval=None, plot=False, progress=None, **kwargs)[source]

Fit the CatBoostModel model given a DataFrame. This method accepts all key word arguments for the catboost.train method.

Parameters
  • df – A vaex DataFrame containing the features and target on which to train the model.

  • evals – A list of DataFrames to be evaluated during training. This allows user to watch performance on the validation sets.

  • early_stopping_rounds (int) – Activates early stopping.

  • verbose_eval (bool) – Requires at least one item in evals. If verbose_eval is True then the evaluation metric on the validation set is printed at each boosting stage.

  • plot (bool) – if True, display an interactive widget in the Jupyter notebook of how the train and validation sets score on each boosting iteration.

  • progress – If True display a progressbar when the training is done in batches.

predict(df, **kwargs)[source]

Provided a vaex DataFrame, get an in-memory numpy array with the predictions from the CatBoostModel model. This method accepts the key word arguments of the predict method from catboost.

Parameters

df – a vaex DataFrame

Returns

A in-memory numpy array containing the CatBoostModel predictions.

Return type

numpy.array

transform(df)[source]

Transform a DataFrame such that it contains the predictions of the CatBoostModel in form of a virtual column.

Parameters

df – A vaex DataFrame. It should have the same columns as the DataFrame used to train the model.

Return copy

A shallow copy of the DataFrame that includes the CatBoostModel prediction as a virtual column.

Return type

DataFrame

Incubator/experimental

These models are in the incubator phase and may disappear in the future

class vaex.ml.incubator.annoy.ANNOYModel(**kwargs)[source]

Bases: vaex.ml.state.HasState

Parameters
  • features – List of features to use.

  • metric – Metric to use for distance calculations

  • n_neighbours – Now many neighbours

  • n_trees – Number of trees to build.

  • predcition_name – Output column name for the neighbours when transforming a DataFrame

  • prediction_name – Output column name for the neighbours when transforming a DataFrame

  • search_k – Jovan?