Example usage

Here we will demonstrate how to use overviewpy in a project:

Import libraries

from overviewpy.overviewpy import overview_tab, overview_na
import pandas as pd
import numpy as np

Generate data

In the first step, we will generate some data that we will use in the next steps.

# Generate full data

data = {
        'id': ['RWA', 'RWA', 'RWA', 'GAB', 'GAB', 'FRA', 'FRA', 'BEL', 'BEL', 'ARG'],
        'year': [2022, 2023, 2021, 2023, 2020, 2019, 2015, 2014, 2013, 2002]
    }

df = pd.DataFrame(data)

df.head()

	id	year
0	RWA	2022
1	RWA	2023
2	RWA	2021
3	GAB	2023
4	GAB	2020

# Generate data with missing values

data_na = {
        'id': ['RWA', 'RWA', 'RWA', np.nan, 'GAB', 'GAB', 'FRA', 'FRA', 'BEL', 'BEL', 'ARG', np.nan,  np.nan],
        'year': [2022, 2001, 2000, 2023, 2021, 2023, 2020, 2019,  np.nan, 2015, 2014, 2013, 2002]
    }

df_na = pd.DataFrame(data_na)

Get an overview of the time distribution in your data

Generate some general overview of the data set using the time and scope conditions with overview_tab. The resulting data frame collapses the time condition for each id by taking into account potential gaps in the time frame.

df_overview = overview_tab(df=df, id='id', time='year')

print(df_overview)

    id  time_frame
9  ARG        2002
7  BEL   2013-2014
5  FRA  2015, 2019
3  GAB  2020, 2023
0  RWA   2021-2023

Get an overview of missing data in your data frame

overview_na is a simple function that provides information about the content of all variables in your data, not only the time and scope conditions. It returns a horizontal ggplot bar plot that indicates the amount of missing data (NAs) for each variable (on the y-axis). You can choose whether to display the relative amount of NAs for each variable in percentage (the default) or the total number of NAs.

overview_na(df_na)