ConstantColumns

task: Prepare
subtask: data cleaning
host: chemml
function: ConstantColumns
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s Constant class

types: (“<class ‘chemml.preprocessing.purge.ConstantColumns’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s Constant class

types: (“<class ‘chemml.preprocessing.purge.ConstantColumns’>”,)

removed_columns_ : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = chemml << function = ConstantColumns

<< func_method = None

>> id df

>> id api

>> id df

>> id api

>> id removed_columns_

Note

The documentation page for function parameters:

MissingValues

task: Prepare
subtask: data cleaning
host: chemml
function: MissingValues
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s MissingValues class

types: (“<class ‘chemml.preprocessing.handle_missing.missing_values’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s MissingValues class

types: (“<class ‘chemml.preprocessing.handle_missing.missing_values’>”,)
wrapper parameters: func_method : String, (default:None)

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = chemml << function = MissingValues

<< func_method = None

<< strategy = ignore_row

<< inf_as_null = True

<< string_as_null = True

<< missing_values = False

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters:

Outliers

task: Prepare
subtask: data cleaning
host: chemml
function: Outliers
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s Constant class

types: (“<class ‘chemml.preprocessing.purge.Outliers’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of ChemML’s Constant class

types: (“<class ‘chemml.preprocessing.purge.Outliers’>”,)

removed_columns_ : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = chemml << function = Outliers

<< func_method = None

<< m = 2.0

<< strategy = median

>> id df

>> id api

>> id df

>> id api

>> id removed_columns_

Note

The documentation page for function parameters:

Split

task: Prepare
subtask: data manipulation
host: chemml
function: Split
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): df1 : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

df2 : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: ChemML, 0.4.1

pandas, 0.20.3
config file view: ##

<< host = chemml << function = Split

<< selection = 1

>> id df

>> id df1

>> id df2

Note

The documentation page for function parameters:

Binarizer

task: Prepare
subtask: feature representation
host: sklearn
function: Binarizer
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Binarizer class

types: (“<class ‘sklearn.preprocessing.data.Binarizer’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Binarizer class

types: (“<class ‘sklearn.preprocessing.data.Binarizer’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = Binarizer

<< track_header = True

<< func_method = None

<< threshold = 0.0

<< copy = True

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Binarizer.html#sklearn.preprocessing.Binarizer

Imputer

task: Prepare
subtask: data cleaning
host: sklearn
function: Imputer
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Imputer class

types: (“<class ‘sklearn.preprocessing.imputation.Imputer’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Imputer class

types: (“<class ‘sklearn.preprocessing.imputation.Imputer’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = Imputer

<< track_header = True

<< func_method = None

<< verbose = 0

<< missing_values = NaN

<< strategy = mean

<< copy = True

<< axis = 0

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html#sklearn.preprocessing.Imputer

KFold

task: Prepare
subtask: split
host: sklearn
function: KFold
input tokens (receivers): dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s KFold class

types: (“<class ‘sklearn.model_selection._split.KFold’>”,)

fold_gen : Generator of indices to split data into training and test set

types: (“<type ‘generator’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘split’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = KFold

<< func_method = None

<< random_state = None

<< shuffle = False

<< n_splits = 3

>> id dfx

>> id api

>> id fold_gen

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

KernelPCA

task: Prepare
subtask: feature transformation
host: sklearn
function: KernelPCA
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s KernelPCA class

types: (“<class ‘sklearn.decomposition.kernel_pca.KernelPCA’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s KernelPCA class

types: (“<class ‘sklearn.decomposition.kernel_pca.KernelPCA’>”,)
wrapper parameters: track_header : Boolean, (default:False)

Always False, the header of input dataframe is not equivalent with the transformed dataframe

choose one of: False

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = KernelPCA

<< track_header = False

<< func_method = None

<< fit_inverse_transform = False

<< kernel = linear

<< n_jobs = 1

<< eigen_solver = auto

<< degree = 3

<< max_iter = None

<< copy_X = True

<< kernel_params = None

<< random_state = None

<< n_components = None

<< remove_zero_eig = False

<< tol = 0

<< alpha = 1.0

<< coef0 = 1

<< gamma = None

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.KernelPCA.html#sklearn.decomposition.KernelPCA

LeaveOneOut

task: Prepare
subtask: split
host: sklearn
function: LeaveOneOut
input tokens (receivers): dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s LeaveOneOut class

types: (“<class ‘sklearn.model_selection._split.LeaveOneOut’>”,)

fold_gen : Generator of indices to split data into training and test set

types: (“<type ‘generator’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘split’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = LeaveOneOut

<< func_method = None

>> id dfx

>> id api

>> id fold_gen

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneOut.html

MaxAbsScaler

task: Prepare
subtask: scaling
host: sklearn
function: MaxAbsScaler
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s MaxAbsScaler class

types: (“<class ‘sklearn.preprocessing.data.MaxAbsScaler’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s MaxAbsScaler class

types: (“<class ‘sklearn.preprocessing.data.MaxAbsScaler’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = MaxAbsScaler

<< track_header = True

<< func_method = None

<< copy = True

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler

MinMaxScaler

task: Prepare
subtask: scaling
host: sklearn
function: MinMaxScaler
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s MinMaxScaler class

types: (“<class ‘sklearn.preprocessing.data.MinMaxScaler’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s MinMaxScaler class

types: (“<class ‘sklearn.preprocessing.data.MinMaxScaler’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = MinMaxScaler

<< track_header = True

<< func_method = None

<< copy = True

<< feature_range = (0, 1)

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler

Normalizer

task: Prepare
subtask: scaling
host: sklearn
function: Normalizer
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Normalizer class

types: (“<class ‘sklearn.preprocessing.data.Normalizer’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s Normalizer class

types: (“<class ‘sklearn.preprocessing.data.Normalizer’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = Normalizer

<< track_header = True

<< func_method = None

<< copy = True

<< norm = l2

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html#sklearn.preprocessing.Normalizer

OneHotEncoder

task: Prepare
subtask: feature representation
host: sklearn
function: OneHotEncoder
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s OneHotEncoder class

types: (“<class ‘sklearn.preprocessing.data.OneHotEncoder’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s OneHotEncoder class

types: (“<class ‘sklearn.preprocessing.data.OneHotEncoder’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = OneHotEncoder

<< track_header = True

<< func_method = None

<< dtype = <type'numpy.float64'>

<< categorical_features = all

<< n_values = auto

<< sparse = True

<< handle_unknown = error

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder

PCA

task: Prepare
subtask: feature transformation
host: sklearn
function: PCA
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s PCA class

types: (“<class ‘sklearn.decomposition.pca.PCA’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s PCA class

types: (“<class ‘sklearn.decomposition.pca.PCA’>”,)
wrapper parameters: track_header : Boolean, (default:False)

Always False, the header of input dataframe is not equivalent with the transformed dataframe

choose one of: False

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = PCA

<< track_header = False

<< func_method = None

<< svd_solver = auto

<< iterated_power = auto

<< random_state = None

<< whiten = False

<< tol = 0.0

<< copy = True

<< n_components = None

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA

PolynomialFeatures

task: Prepare
subtask: feature representation
host: sklearn
function: PolynomialFeatures
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s PolynomialFeatures class

types: (“<class ‘sklearn.preprocessing.data.PolynomialFeatures’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s PolynomialFeatures class

types: (“<class ‘sklearn.preprocessing.data.PolynomialFeatures’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = PolynomialFeatures

<< track_header = True

<< func_method = None

<< include_bias = True

<< interaction_only = False

<< degree = 2

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures

RobustScaler

task: Prepare
subtask: scaling
host: sklearn
function: RobustScaler
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s RobustScaler class

types: (“<class ‘sklearn.preprocessing.data.RobustScaler’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s RobustScaler class

types: (“<class ‘sklearn.preprocessing.data.RobustScaler’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = RobustScaler

<< track_header = True

<< func_method = None

<< copy = True

<< with_scaling = True

<< with_centering = True

<< quantile_range = (25.0, 75.0)

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html#sklearn.preprocessing.RobustScaler

ShuffleSplit

task: Prepare
subtask: split
host: sklearn
function: ShuffleSplit
input tokens (receivers): dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s ShuffleSplit class

types: (“<class ‘sklearn.model_selection._split.ShuffleSplit’>”,)

fold_gen : Generator of indices to split data into training and test set

types: (“<type ‘generator’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘split’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = ShuffleSplit

<< func_method = None

<< n_splits = 10

<< train_size = None

<< random_state = None

<< test_size = default

>> id dfx

>> id api

>> id fold_gen

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.ShuffleSplit.html#sklearn.model_selection.ShuffleSplit

StandardScaler

task: Prepare
subtask: scaling
host: sklearn
function: StandardScaler
input tokens (receivers): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s StandardScaler class

types: (“<class ‘sklearn.preprocessing.data.StandardScaler’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

api : instance of scikit-learn’s StandardScaler class

types: (“<class ‘sklearn.preprocessing.data.StandardScaler’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)

func_method : string, (default:None)

fit_transform: always make a new api; transform: must receive an api; inverse_transform: must receive an api; None: only make a new api

choose one of: (‘fit_transform’, ‘transform’, ‘inverse_transform’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = StandardScaler

<< track_header = True

<< func_method = None

<< copy = True

<< with_mean = True

<< with_std = True

>> id df

>> id api

>> id df

>> id api

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler

StratifiedShuffleSplit

task: Prepare
subtask: split
host: sklearn
function: StratifiedShuffleSplit
input tokens (receivers): dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): api : instance of scikit-learn’s StratifiedShuffleSplit class

types: (“<class ‘sklearn.model_selection._split.StratifiedShuffleSplit’>”,)

fold_gen : Generator of indices to split data into training and test set

types: (“<type ‘generator’>”,)
wrapper parameters: func_method : string, (default:None)

choose one of: (‘split’, None)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = StratifiedShuffleSplit

<< func_method = None

<< n_splits = 10

<< train_size = None

<< random_state = None

<< test_size = default

>> id dfx

>> id api

>> id fold_gen

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html#sklearn.model_selection.StratifiedShuffleSplit

train_test_split

task: Prepare
subtask: split
host: sklearn
function: train_test_split
input tokens (receivers): dfy : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): dfx_test : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfy_train : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfy_test : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

dfx_train : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
wrapper parameters: track_header : Boolean, (default:True)

if True, the input dataframe’s header will be transformed to the output dataframe

choose one of: (True, False)
required packages: scikit-learn, 0.19.0

pandas, 0.20.3
config file view: ##

<< host = sklearn << function = train_test_split

<< track_header = True

<< shuffle = True

<< train_size = None

<< random_state = None

<< test_size = 0.25

<< stratify = None

>> id dfy

>> id dfx

>> id dfx_test

>> id dfy_train

>> id dfy_test

>> id dfx_train

Note

The documentation page for function parameters: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html

concat

task: Prepare
subtask: data manipulation
host: pandas
function: concat
input tokens (receivers): df1 : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

df3 : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)

df2 : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
output tokens (senders): df : pandas dataframe

types: (“<class ‘pandas.core.frame.DataFrame’>”,)
required packages: pandas, 0.20.3
config file view: ##

<< host = pandas << function = concat

<< join = outer

<< verify_integrity = False

<< keys = None

<< levels = None

<< ignore_index = False

<< names = None

<< join_axes = None

<< copy = True

<< axis = 0

>> id df1

>> id df3

>> id df2

>> id df

Note

The documentation page for function parameters: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html