handle_missing

handle_missing

Functions

Name	Description
handle_missing	Handles missing data in a pandas DataFrame.

handle_missing

handle_missing.handle_missing(df, strategy='drop', columns=None)

Handles missing data in a pandas DataFrame.

Function returns a pandas DataFrame where missing values are handled in a user-defined way.

Parameters

Name	Type	Description	Default
df	pandas.DataFrame	Input DataFrame	required
strategy	str	The strategy to use for handling missing values. Permissible values (numeric): mean, median, max, min, mode, drop Permissible values (else): mode, drop	'drop'
columns	list	Columns where the missing values are to be handled. Default handles all columns.	None

Returns

Name	Type	Description
	pandas.DataFrame	Dataframe where missing values have been handled.

Raises

Name	Type	Description
	TypeError	If df is not a pandas DataFrame. If strategy is not a string. If columns is not a list or None. If strategy cannot be used for dtype of column. If dtype of column is not designed to be handled.
	ValueError	If strategy is not permitted. If column is not in df.columns. If column only contains NaN.

Examples

>>> import numpy as np
>>> import pandas as pd
>>>df = pd.DataFrame({
...     "A": [1, 1, 2],
...     "B": [np.nan, 3, 4]
... })
>>> handle_missing(df)
   A  B
1  1  3
2  2  4

>>> handle_missing(df, strategy='mean')
   A  B
0  1  3.5
1  1  3.0
2  2  4.0