Sklearn-pandas

This module provides a bridge between Scikit-Learn's machine learning methods and pandas-style Data Frames.

In particular, it provides:

1. A way to map DataFrame columns to transformations, which are later recombined into features.
2. A compatibility shim for old scikit-learn versions to cross-validate a pipeline that takes a pandas DataFrame as input. This is only needed for scikit-learn<0.16.0 (see #11 for details). It is deprecated and will likely be dropped in skearn-pandas==2.0.
3. A CategoricalImputer that replaces null-like values with the mode and works with string columns.

Installation

You can install sklearn-pandas with pip:

# pip install sklearn-pandas

Tests

The examples in this file double as basic sanity tests. To run them, use doctest, which is included with python:

# python -m doctest README.rst

Usage

Import

Import what you need from the sklearn_pandas package. The choices are:

• DataFrameMapper, a class for mapping pandas data frame columns to different sklearn transformations
• cross_val_score, similar to sklearn.cross_validation.cross_val_score but working on pandas DataFrames

For this demonstration, we will import both:

>>> from sklearn_pandas import DataFrameMapper, cross_val_score

For these examples, we'll also use pandas, numpy, and sklearn:

>>> import pandas as pd
>>> import numpy as np
>>> import sklearn.preprocessing, sklearn.decomposition, \
...     sklearn.linear_model, sklearn.pipeline, sklearn.metrics
>>> from sklearn.feature_extraction.text import CountVectorizer

Load some Data

Normally you'll read the data from a file, but for demonstration purposes I'll create a data frame from a Python dict:

>>> data = pd.DataFrame({'pet':      ['cat', 'dog', 'dog', 'fish', 'cat', 'dog', 'cat', 'fish'],
...                      'children': [4., 6, 3, 3, 2, 3, 5, 4],
...                      'salary':   [90, 24, 44, 27, 32, 59, 36, 27]})

Transformation Mapping

Map the Columns to Transformations

The mapper takes a list of pairs. The first is a column name from the pandas DataFrame, or a list containing one or multiple columns (we will see an example with multiple columns later). The second is an object which will perform the transformation which will be applied to that column:

>>> mapper = DataFrameMapper([
...     ('pet', sklearn.preprocessing.LabelBinarizer()),
...     (['children'], sklearn.preprocessing.StandardScaler())
... ])

The difference between specifying the column selector as 'column' (as a simple string) and ['column'] (as a list with one element) is the shape of the array that is passed to the transformer. In the first case, a one dimensional array will be passed, while in the second case it will be a 2-dimensional array with one column, i.e. a column vector.

This behaviour mimics the same pattern as pandas' dataframes __getitem__ indexing:

>>> data['children'].shape
(8,)
>>> data[['children']].shape
(8, 1)

Be aware that some transformers expect a 1-dimensional input (the label-oriented ones) while some others, like OneHotEncoder or Imputer, expect 2-dimensional input, with the shape [n_samples, n_features].

Test the Transformation

We can use the fit_transform shortcut to both fit the model and see what transformed data looks like. In this and the other examples, output is rounded to two digits with np.round to account for rounding errors on different hardware:

>>> np.round(mapper.fit_transform(data.copy()), 2)
array([[ 1.  ,  0.  ,  0.  ,  0.21],
       [ 0.  ,  1.  ,  0.  ,  1.88],
       [ 0.  ,  1.  ,  0.  , -0.63],
       [ 0.  ,  0.  ,  1.  , -0.63],
       [ 1.  ,  0.  ,  0.  , -1.46],
       [ 0.  ,  1.  ,  0.  , -0.63],
       [ 1.  ,  0.  ,  0.  ,  1.04],
       [ 0.  ,  0.  ,  1.  ,  0.21]])

Note that the first three columns are the output of the LabelBinarizer (corresponding to _cat_, _dog_, and _fish_ respectively) and the fourth column is the standardized value for the number of children. In general, the columns are ordered according to the order given when the DataFrameMapper is constructed.

Now that the transformation is trained, we confirm that it works on new data:

>>> sample = pd.DataFrame({'pet': ['cat'], 'children': [5.]})
>>> np.round(mapper.transform(sample), 2)
array([[ 1.  ,  0.  ,  0.  ,  1.04]])

Output features names

In certain cases, like when studying the feature importances for some model, we want to be able to associate the original features to the ones generated by the dataframe mapper. We can do so by inspecting the automatically generated

transformed_names_ attribute of the mapper after transformation:

>>> mapper.transformed_names_
['pet_cat', 'pet_dog', 'pet_fish', 'children']

Outputting a dataframe

By default the output of the dataframe mapper is a numpy array. This is so because most sklearn estimators expect a numpy array as input. If however we want the output of the mapper to be a dataframe, we can do so using the parameter df_out when creating the mapper:

>>> mapper_df = DataFrameMapper([
...     ('pet', sklearn.preprocessing.LabelBinarizer()),
...     (['children'], sklearn.preprocessing.StandardScaler())
... ], df_out=True)
>>> np.round(mapper_df.fit_transform(data.copy()), 2)
   pet_cat  pet_dog  pet_fish  children
0      1.0      0.0       0.0      0.21
1      0.0      1.0       0.0      1.88
2      0.0      1.0       0.0     -0.63
3      0.0      0.0       1.0     -0.63
4      1.0      0.0       0.0     -1.46
5      0.0      1.0       0.0     -0.63
6      1.0      0.0       0.0      1.04
7      0.0      0.0       1.0      0.21

The names for the columns are the same ones present in the transformed_names_ attribute.

Note this does not work together with the default=True or sparse=True arguments to the mapper.

Transform Multiple Columns

Transformations may require multiple input columns. In these cases, the column names can be specified in a list:

>>> mapper2 = DataFrameMapper([
...     (['children', 'salary'], sklearn.decomposition.PCA(1))
... ])

Now running fit_transform will run PCA on the children and salary columns and return the first principal component:

>>> np.round(mapper2.fit_transform(data.copy()), 1)
array([[ 47.6],
       [-18.4],
       [  1.6],
       [-15.4],
       [-10.4],
       [ 16.6],
       [ -6.4],
       [-15.4]])

Multiple transformers for the same column

Multiple transformers can be applied to the same column specifying them in a list:

>>> mapper3 = DataFrameMapper([
...     (['age'], [sklearn.preprocessing.Imputer(),
...                sklearn.preprocessing.StandardScaler()])])
>>> data_3 = pd.DataFrame({'age': [1, np.nan, 3]})
>>> mapper3.fit_transform(data_3)
array([[-1.22474487],
       [ 0.        ],
       [ 1.22474487]])

Columns that don't need any transformation

Only columns that are listed in the DataFrameMapper are kept. To keep a column but don't apply any transformation to it, use None as transformer:

>>> mapper3 = DataFrameMapper([
...     ('pet', sklearn.preprocessing.LabelBinarizer()),
...     ('children', None)
... ])
>>> np.round(mapper3.fit_transform(data.copy()))
array([[ 1.,  0.,  0.,  4.],
       [ 0.,  1.,  0.,  6.],
       [ 0.,  1.,  0.,  3.],
       [ 0.,  0.,  1.,  3.],
       [ 1.,  0.,  0.,  2.],
       [ 0.,  1.,  0.,  3.],
       [ 1.,  0.,  0.,  5.],
       [ 0.,  0.,  1.,  4.]])

Applying a default transformer

A default transformer can be applied to columns not explicitly selected passing it as the default argument to the mapper:

>>> mapper4 = DataFrameMapper([
...     ('pet', sklearn.preprocessing.LabelBinarizer()),
...     ('children', None)
... ], default=sklearn.preprocessing.StandardScaler())
>>> np.round(mapper4.fit_transform(data.copy()), 1)
array([[ 1. ,  0. ,  0. ,  4. ,  2.3],
       [ 0. ,  1. ,  0. ,  6. , -0.9],
       [ 0. ,  1. ,  0. ,  3. ,  0.1],
       [ 0. ,  0. ,  1. ,  3. , -0.7],
       [ 1. ,  0. ,  0. ,  2. , -0.5],
       [ 0. ,  1. ,  0. ,  3. ,  0.8],
       [ 1. ,  0. ,  0. ,  5. , -0.3],
       [ 0. ,  0. ,  1. ,  4. , -0.7]])

Using default=False (the default) drops unselected columns. Using default=None pass the unselected columns unchanged.

Feature selection and other supervised transformations

DataFrameMapper supports transformers that require both X and y arguments. An example of this is feature selection. Treating the 'pet' column as the target, we will select the column that best predicts it.

>>> from sklearn.feature_selection import SelectKBest, chi2
>>> mapper_fs = DataFrameMapper([(['children','salary'], SelectKBest(chi2, k=1))])
>>> mapper_fs.fit_transform(data[['children','salary']], data['pet'])
array([[ 90.],
       [ 24.],
       [ 44.],
       [ 27.],
       [ 32.],
       [ 59.],
       [ 36.],
       [ 27.]])

Working with sparse features

DataFrameMapper``s will return a dense feature array by default. Setting ``sparse=True in the mapper will return a sparse array whenever any of the extracted features is sparse. Example:

>>> mapper5 = DataFrameMapper([
...     ('pet', CountVectorizer()),
... ], sparse=True)
>>> type(mapper5.fit_transform(data))
<class 'scipy.sparse.csr.csr_matrix'>

The stacking of the sparse features is done without ever densifying them.

Cross-Validation

Now that we can combine features from pandas DataFrames, we may want to use cross-validation to see whether our model works. scikit-learn<0.16.0 provided features for cross-validation, but they expect numpy data structures and won't work with DataFrameMapper.

To get around this, sklearn-pandas provides a wrapper on sklearn's cross_val_score function which passes a pandas DataFrame to the estimator rather than a numpy array:

>>> pipe = sklearn.pipeline.Pipeline([
...     ('featurize', mapper),
...     ('lm', sklearn.linear_model.LinearRegression())])
>>> np.round(cross_val_score(pipe, X=data.copy(), y=data.salary, scoring='r2'), 2)
array([ -1.09,  -5.3 , -15.38])

Sklearn-pandas' cross_val_score function provides exactly the same interface as sklearn's function of the same name.

CategoricalImputer

Since the scikit-learn Imputer transformer currently only works with numbers, sklearn-pandas provides an equivalent helper transformer that do work with strings, substituting null values with the most frequent value in that column.

Example:

>>> from sklearn_pandas import CategoricalImputer
>>> data = np.array(['a', 'b', 'b', np.nan], dtype=object)
>>> imputer = CategoricalImputer()
>>> imputer.fit_transform(data)
array(['a', 'b', 'b', 'b'], dtype=object)

Changelog

Development

• Capture output columns generated names in transformed_names_ attribute (#78).
• Add CategoricalImputer that replaces null-like values with the mode for string-like columns.

1.3.0 (2017-01-21)

• Make the mapper return dataframes when df_out=True (#70, #74).
• Update imports to avoid deprecation warnings in sklearn 0.18 (#68).

1.2.0 (2016-10-02)

• Deprecate custom cross-validation shim classes.
• Require scikit-learn>=0.15.0. Resolves #49.
• Allow applying a default transformer to columns not selected explicitly in the mapper. Resolves #55.
• Allow specifying an optional y argument during transform for supervised transformations. Resolves #58.

1.1.0 (2015-12-06)

• Delete obsolete PassThroughTransformer. If no transformation is desired for a given column, use None as transformer.
• Factor out code in several modules, to avoid having everything in __init__.py.
• Use custom TransformerPipeline class to allow transformation steps accepting only a X argument. Fixes #46.
• Add compatibility shim for unpickling mappers with list of transformers created before 1.0.0. Fixes #45.

1.0.0 (2015-11-28)

• Change version numbering scheme to SemVer.
• Use sklearn.pipeline.Pipeline instead of copying its code. Resolves #43.
• Raise KeyError when selecting unexistent columns in the dataframe. Fixes #30.
• Return sparse feature array if any of the features is sparse and sparse argument is True. Defaults to False to avoid potential breaking of existing code. Resolves #34.
• Return model and prediction in custom CV classes. Fixes #27.

0.0.12 (2015-11-07)

• Allow specifying a list of transformers to use sequentially on the same column.

Credits

The code for DataFrameMapper is based on code originally written by Ben Hamner.

Other contributors:

• Arnau Gil Amat
• Cal Paterson
• Gustavo Sena Mafra
• Israel Saeta Pérez
• Jeremy Howard
• Olivier Grisel
• Paul Butler
• Ritesh Agrawal
• Vitaley Zaretskey
• Zac Stewart