column_name#

paramcheckup.data_frames.column_name(column_name, data_frame, param_name, kind, kind_name, stacklevel=4, error=True)[source]#

This function checks whether the str column_name is a valid column name for the DataFrame dataframe.

Parameters:

column_namestr: The name of the column to be checked;
data_frameDataFrame: The DataFrame that should contain the column named column_name;
param_namestr: The name of the parameter that received the variable data_frame;
kindstr: The object where param_name is applied (function, method, class, etc.);
kind_namestr: The name of the object kind;
stacklevelint, optional: The stacking level (default is 4);
errorbool, optional: Whether to display error text (True, default) or not (False);

Returns:

outputTrue: If column_name IS a valid column name for the data_frame;
raisesValueError: If column_name is NOT a valid column name for the data_frame;

Examples

>>> import pandas as pd
>>> from paramcheckup import data_frames
>>> data = [["Anderson", 33], ["Juliana", 31], ["Marcos", 26], ["Mariana", 30]]
>>> columns = ["Name", "Age"]
>>> df = pd.DataFrame(
    data=data,
    columns=columns,
)
>>> output = data_frames.column_name(
    column_name="Name",
    data_frame=df,
    param_name="data_frame",
    kind="function",
    kind_name="database",
    stacklevel=3,
    error=True,
)
>>> print(output)
True

>>> import pandas as pd
>>> from paramcheckup import data_frames
>>> data = [["Anderson", 33], ["Juliana", 31], ["Marcos", 26], ["Mariana", 30]]
>>> columns = ["Name", "Age"]
>>> df = pd.DataFrame(
    data=data,
    columns=columns,
)
>>> output = data_frames.column_name(
    column_name="Gender",
    data_frame=df,
    param_name="data_frame",
    kind="function",
    kind_name="database",
    stacklevel=3,
    error=False,
)
UserWarning at line 10: The `data_frame` in function `database` does not contain a column with the name `Gender`.