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This module provides a bridge betweenScikit-Learn's machine learning methods andpandas-style Data Frames.In particular, it provides a way to mapDataFrame columns to transformations, which are later recombined into features.

You can installsklearn-pandas withpip:

# pip install sklearn-pandas

or conda-forge:

# conda install -c conda-forge sklearn-pandas

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

# python -m doctest README.rst

Import what you need from thesklearn_pandas package. The choices are:

• DataFrameMapper, a class for mapping pandas data frame columns to different sklearn transformations

For this demonstration, we will import both:

>>>fromsklearn_pandasimportDataFrameMapper

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

>>>importpandasaspd>>>importnumpyasnp>>>importsklearn.preprocessing,sklearn.decomposition, \...sklearn.linear_model,sklearn.pipeline,sklearn.metrics, \...sklearn.compose>>>fromsklearn.feature_extraction.textimportCountVectorizer

Normally you'll read the data from a file, but for demonstration purposes we'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]})

The mapper takes a list of tuples. Each tuple has three elements:

1. column name(s): The first element 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) or an instance of a callable function such asmake_column_selector.
2. transformer(s): The second element is an object which will perform the transformation which will be applied to that column.
3. attributes: The third one is optional and is a dictionary containing the transformation options, if applicable (see "custom column names for transformed features" below).

Let's see an example:

>>>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, likeOneHotEncoder orImputer, expect 2-dimensional input, with the shape[n_samples, n_features].

We can use thefit_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 withnp.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 theLabelBinarizer (corresponding tocat,dog, andfish 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 theDataFrameMapper 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]])

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 bythe dataframe mapper. We can do so by inspecting the automatically generatedtransformed_names_ attribute of the mapper after transformation:

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

We can provide a custom name for the transformed features, to be used insteadof the automatically generated one, by specifying it as the third argumentof the feature definition:

>>>mapper_alias=DataFrameMapper([...     (['children'],sklearn.preprocessing.StandardScaler(),...      {'alias':'children_scaled'})... ])>>> _=mapper_alias.fit_transform(data.copy())>>>mapper_alias.transformed_names_['children_scaled']

Alternatively, you can also specify prefix and/or suffix to add to the column name. For example:

>>>mapper_alias=DataFrameMapper([...     (['children'],sklearn.preprocessing.StandardScaler(), {'prefix':'standard_scaled_'}),...     (['children'],sklearn.preprocessing.StandardScaler(), {'suffix':'_raw'})... ])>>> _=mapper_alias.fit_transform(data.copy())>>>mapper_alias.transformed_names_['standard_scaled_children','children_raw']

In some situations the columns are not known before hand and we would like to dynamically select them during the fit operation. As shown below, in such situations you can provide either a custom callable or usemake_column_selector.

>>>classGetColumnsStartingWith:...def__init__(self,start_str):...self.pattern=start_str......def__call__(self,X:pd.DataFrame=None):...return [cforcinX.columnsifc.startswith(self.pattern)]...>>>df=pd.DataFrame({...'sepal length (cm)': [1.0,2.0,3.0],...'sepal width (cm)': [1.0,2.0,3.0],...'petal length (cm)': [1.0,2.0,3.0],...'petal width (cm)': [1.0,2.0,3.0]... })>>>t=DataFrameMapper([...     (...sklearn.compose.make_column_selector(dtype_include=float),...sklearn.preprocessing.StandardScaler(),...       {'alias':'x'}...     ),...     (...GetColumnsStartingWith('petal'),...None,...       {'alias':'petal'}...     )],df_out=True,default=False)>>>t.fit(df).transform(df).shape(3,6)>>>t.transformed_names_['x_0','x_1','x_2','x_3','petal_0','petal_1']

Above we use make_column_selector to select all columns that are of type float and also use a custom callable function to select columns that start with the word 'petal'.

By default the transformers are passed a numpy array of the selected columnsas input. This is becausesklearn transformers are historically designed towork with numpy arrays, not with pandas dataframes, even though their basicindexing interfaces are similar.

However we can pass a dataframe/series to the transformers to handle customcases initializing the dataframe mapper withinput_df=True:

>>>fromsklearn.baseimportTransformerMixin>>>classDateEncoder(TransformerMixin):...deffit(self,X,y=None):...returnself......deftransform(self,X):...dt=X.dt...returnpd.concat([dt.year,dt.month,dt.day],axis=1)>>>dates_df=pd.DataFrame(...     {'dates':pd.date_range('2015-10-30','2015-11-02')})>>>mapper_dates=DataFrameMapper([...     ('dates',DateEncoder())... ],input_df=True)>>>mapper_dates.fit_transform(dates_df)array([[2015,10,30],       [2015,10,31],       [2015,11,1],       [2015,11,2]])

We can also specify this option per group of columns instead of for thewhole mapper:

>>>mapper_dates=DataFrameMapper([...     ('dates',DateEncoder(), {'input_df':True})... ])>>>mapper_dates.fit_transform(dates_df)array([[2015,10,30],       [2015,10,31],       [2015,11,1],       [2015,11,2]])

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 parameterdf_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_catpet_dogpet_fishchildren01000.2110101.882010-0.633001-0.634100-1.465010-0.6361001.0470010.21

The names for the columns are the same ones present in thetransformed_names_attribute.

Note this does not work together with thedefault=True orsparse=True arguments to the mapper.

Sometimes it is required to drop a specific column/ list of columns.For this purpose,drop_cols argument forDataFrameMapper can be used.Default value isNone:

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

Now runningfit_transform will run transformations on 'pet' and 'children' and drop 'salary' column:

>>>np.round(mapper_df.fit_transform(data.copy()),1)array([[1. ,0. ,0. ,0.2],       [0. ,1. ,0. ,1.9],       [0. ,1. ,0. ,-0.6],       [0. ,0. ,1. ,-0.6],       [1. ,0. ,0. ,-1.5],       [0. ,1. ,0. ,-0.6],       [1. ,0. ,0. ,1. ],       [0. ,0. ,1. ,0.2]])

Transformations may require multiple input columns. In these

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 runningfit_transform will run PCA on thechildren andsalary 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 can be applied to the same column specifying themin a list:

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

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.]])

A default transformer can be applied to columns not explicitly selectedpassing it as thedefault 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]])

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

Sometimes it is required to apply the same transformation to several dataframe columns.To simplify this process, the package providesgen_features function which accepts a listof columns and feature transformer class (or list of classes), and generates a feature definition,acceptable byDataFrameMapper.

For example, consider a dataset with three categorical columns, 'col1', 'col2', and 'col3',To binarize each of them, one could pass column names andLabelBinarizer transformer classinto generator, and then use returned definition asfeatures argument forDataFrameMapper:

>>>fromsklearn_pandasimportgen_features>>>feature_def=gen_features(...columns=['col1','col2','col3'],...classes=[sklearn.preprocessing.LabelEncoder]... )>>>feature_def[('col1', [LabelEncoder()], {}), ('col2', [LabelEncoder()], {}), ('col3', [LabelEncoder()], {})]>>>mapper5=DataFrameMapper(feature_def)>>>data5=pd.DataFrame({...'col1': ['yes','no','yes'],...'col2': [True,False,False],...'col3': ['one','two','three']... })>>>mapper5.fit_transform(data5)array([[1,1,0],       [0,0,2],       [1,0,1]])

If it is required to override some of transformer parameters, then a dict with 'class' key andtransformer parameters should be provided. For example, consider a dataset with missing values.Then the following code could be used to override default imputing strategy:

>>>fromsklearn.imputeimportSimpleImputer>>>importnumpyasnp>>>feature_def=gen_features(...columns=[['col1'], ['col2'], ['col3']],...classes=[{'class':SimpleImputer,'strategy':'most_frequent'}]... )>>>mapper6=DataFrameMapper(feature_def)>>>data6=pd.DataFrame({...'col1': [np.nan,1,1,2,3],...'col2': [True,False,np.nan,np.nan,True],...'col3': [0,0,0,np.nan,np.nan]... })>>>mapper6.fit_transform(data6)array([[1.0,True,0.0],       [1.0,False,0.0],       [1.0,True,0.0],       [2.0,True,0.0],       [3.0,True,0.0]],dtype=object)

You can also specify global prefix or suffix for the generated transformed column names using the prefix and suffixparameters:

>>>feature_def=gen_features(...columns=['col1','col2','col3'],...classes=[sklearn.preprocessing.LabelEncoder],...prefix="lblencoder_"... )>>>mapper5=DataFrameMapper(feature_def)>>>data5=pd.DataFrame({...'col1': ['yes','no','yes'],...'col2': [True,False,False],...'col3': ['one','two','three']... })>>> _=mapper5.fit_transform(data5)>>>mapper5.transformed_names_['lblencoder_col1','lblencoder_col2','lblencoder_col3']

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.

>>>fromsklearn.feature_selectionimportSelectKBest,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.]])

ADataFrameMapper will return a dense feature array by default. Settingsparse=True in the mapper will returna 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.

While you can useFunctionTransformation to generate arbitrary transformers, it can present serialization issueswhen pickling. UseNumericalTransformer instead, which takes the function name as a string parameter and hencecan be easily serialized.

>>>fromsklearn_pandasimportNumericalTransformer>>>mapper5=DataFrameMapper([...     ('children',NumericalTransformer('log')),... ])>>>mapper5.fit_transform(data)array([[1.38629436],       [1.79175947],       [1.09861229],       [1.09861229],       [0.69314718],       [1.09861229],       [1.60943791],       [1.38629436]])

You can change log level to info to print time take to fit/transform features. Setting it to higher level will stop printing elapsed time.Below example shows how to change logging level.

>>>importlogging>>>logging.getLogger('sklearn_pandas').setLevel(logging.INFO)

• Added an ability to provide callable functions instead of static column list.

• Removed test for Python 3.6 and added Python 3.9
• Added deprecation warning for NumericalTransformer
• Fixed pickling issue causing integration issues with Baikal.
• Started publishing package to conda repo

• Explicitly handling serialization (#224)
• document fixes
• Making transform function thread safe (#194)
• Switched to nox for unit testing (#226)

• Added elapsed time information for each feature.

• Fix DataFrameMapper drop_cols attribute naming consistency with scikit-learn and initialization.

• Added an option to explicitly drop columns.

• Deprecated support for Python < 3.6.
• Deprecated support for old versions of scikit-learn, pandas and numpy. Please check setup.py for minimum requirement.
• Removed CategoricalImputer, cross_val_score and GridSearchCV. All these functionality now exists as part ofscikit-learn. Please use SimpleImputer instead of CategoricalImputer. AlsoCross validation from sklearn now supports dataframe so we don't need to use cross validation wrapper provided overhere.
• AddedNumericalTransformer for common numerical transformations. Currently it implements log and log1ptransformation.
• Added prefix and suffix options. See examples above. These are usually helpful when using gen_features.
• Addeddrop_cols argument to DataframeMapper. This can be used to explicitly drop columns

• AddFunctionTransformer class (#117).
• Fix column names derivation for dataframes with multi-index or non-stringcolumns (#166).
• Change behaviour of DataFrameMapper's fit_transform method to invoke each underlying transformers'native fit_transform if implemented (#150).

• Fix issues with unicode names inget_names (#160).
• Update to build usingnumpy==1.14 andpython==3.6 (#154).
• Addstrategy andfill_value parameters toCategoricalImputer to allow imputingwith values other than the mode (#144),(#161).
• Preserve input data types when no transform is supplied (#138).

• Add column name to exception during fit/transform (#110).
• Addgen_feature helper function to help generating the same transformation for multiple columns (#126).

• Allow inputting a dataframe/series per group of columns.
• Get feature names also fromestimator.get_feature_names() if present.
• Attempt to derive feature names from individual transformers when applying alist of transformers.
• Do not mutate features in__init__ to be compatible withsklearn>=0.20 (#76).

• Allow specifying a custom name (alias) for transformed columns (#83).
• Capture output columns generated names intransformed_names_ attribute (#78).
• AddCategoricalImputer that replaces null-like values with the modefor string-like columns.
• Addinput_df init argument to allow inputting a dataframe/series to thetransformers instead of a numpy array (#60).

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

• Deprecate custom cross-validation shim classes.
• Requirescikit-learn>=0.15.0. Resolves #49.
• Allow applying a default transformer to columns not selected explicitly inthe mapper. Resolves #55.
• Allow specifying an optionaly argument during transform forsupervised transformations. Resolves #58.

• Delete obsoletePassThroughTransformer. If no transformation is desired for a given column, useNone as transformer.
• Factor out code in several modules, to avoid having everything in__init__.py.
• Use customTransformerPipeline 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.

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

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

The code forDataFrameMapper is based on code originally written byBen Hamner.

Other contributors:

• Ariel Rossanigo (@arielrossanigo)
• Arnau Gil Amat (@arnau126)
• Assaf Ben-David (@AssafBenDavid)
• Brendan Herger (@bjherger)
• Cal Paterson (@calpaterson)
• @defvorfu
• Floris Hoogenboom (@FlorisHoogenboom)
• Gustavo Sena Mafra (@gsmafra)
• Israel Saeta Pérez (@dukebody)
• Jeremy Howard (@jph00)
• Jimmy Wan (@jimmywan)
• Kristof Van Engeland (@kristofve91)
• Olivier Grisel (@ogrisel)
• Paul Butler (@paulgb)
• Richard Miller (@rwjmiller)
• Ritesh Agrawal (@ragrawal)
• @SandroCasagrande
• Timothy Sweetser (@hacktuarial)
• Vitaley Zaretskey (@vzaretsk)
• Zac Stewart (@zacstewart)
• Parul Singh (@paro1234)
• Vincent Heusinkveld (@VHeusinkveld)