niimpy.exploration.eda.missingness module
This module is rewritten based on the missingno package. The original files can be found here: https://github.com/ResidentMario/missingno
- niimpy.exploration.eda.missingness.bar(df, columns=None, title='Data frequency', xaxis_title='', yaxis_title='', sampling_freq=None, sampling_method='mean')[source]
Display bar chart visualization of the nullity of the given DataFrame.
- Parameters:
- df: pandas Dataframe
Dataframe to plot
- columns: list, optional
Columns from input dataframe to investigate missingness. If none is given, uses all columns.
- title: str
Figure’s title
- xaxis_title: str, optional
x_axis’s label
- yaxis_title: str, optional
y_axis’s label
- sampling_freq: str, optional
Frequency to resample the data. Requires the dataframe to have datetime-like index. Possible values: ‘h’, ‘min’
- sampling_method: str, optional
Resampling method. Possible values: ‘sum’, ‘mean’. Default value is ‘mean’.
- Returns
- ——-
- fig: Plotly figure.
- niimpy.exploration.eda.missingness.bar_count(df, columns=None, title='Data frequency', xaxis_title='', yaxis_title='', sampling_freq='h')[source]
Display bar chart visualization of the nullity of the given DataFrame.
- Parameters:
- df: pandas Dataframe
Dataframe to plot
- columns: list, optional
Columns from input dataframe to investigate missingness. If none is given, uses all columns.
- title: str
Figure’s title
- xaxis_title: str, optional
x_axis’s label
- yaxis_title: str, optional
y_axis’s label
- sampling_freq: str, optional
Frequency to resample the data. Requires the dataframe to have datetime-like index. Possible values: ‘h’, ‘min’
- Returns:
- fig: Plotly figure.
- niimpy.exploration.eda.missingness.heatmap(df, height=800, width=800, title='', xaxis_title='', yaxis_title='')[source]
Return ‘plotly’ heatmap visualization of the nullity correlation of the Dataframe.
- Parameters:
- df: pandas Dataframe
Dataframe to plot
- width: int:
Figure’s width
- height: int:
Figure’s height
- Returns
- ——-
- fig: Plotly figure.
- niimpy.exploration.eda.missingness.matrix(df, height=500, title='Data frequency', xaxis_title='', yaxis_title='', sampling_freq=None, sampling_method='mean')[source]
Return matrix visualization of the nullity of data. For now, this function assumes that the data frame is datetime indexed.
- Parameters:
- df: pandas Dataframe
Dataframe to plot
- columns: list, optional
Columns from input dataframe to investigate missingness. If none is given, uses all columns.
- title: str
Figure’s title
- xaxis_title: str, optional
x_axis’s label
- yaxis_title: str, optional
y_axis’s label
- sampling_freq: str, optional
Frequency to resample the data. Requires the dataframe to have datetime-like index. Possible values: ‘h’, ‘min’
- sampling_method: str, optional
Resampling method. Possible values: ‘sum’, ‘mean’. Default value is ‘mean’.
- Returns
- ——-
- fig: Plotly figure.