overviewpy #

Easily Extracting Information About Your Data

Installation#

$ pip install overviewpy

Usage#

Current Functions#

The goal of overviewpy is to make it easy to get an overview of a data set by displaying relevant sample information. At the moment, there are the following functions:

  • overview_tab generates a tabular overview of the sample (and returns a data frame). The general sample plots a two-column table that provides information on an id in the left column and a the time frame on the right column.

  • overview_na plots an overview of missing values by variable (both by row and by column)

overview_tab#

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.

 from overviewpy.overviewpy import overview_tab
 import pandas as pd

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

overview_na#

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.

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

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)

overview_na(df_na)

Roadmap#

overviewpy seeks to mirror the functionality of overviewR and will extend its features with the following functionality in the future:

  • overview_crosstab generates a cross table. The conditional column allows to disaggregate the overview table by specifying two conditions, hence resulting a 2x2 table. This way, it is easy to visualize the time and scope conditions as well as theoretical assumptions with examples from the data set.

  • overview_latex converts the output of both overview_tab and overview_crosstab into LaTeX code and/or directly into a .tex file.

  • overview_plot is an alternative to visualize the sample (a way to present results from overview_tab)

  • overview_crossplot is an alternative to visualize a cross table (a way to present results from overview_crosstab)

  • overview_heat plots a heat map of your time line

  • overview_overlap plots comparison plots (bar graph and Venn diagram) to compare to data frames

Contributing#

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License#

overviewpy was created by Cosima Meyer. It is licensed under the terms of the BSD 3-Clause license.

Credits#

overviewpy was created with cookiecutter and the py-pkgs-cookiecutter template.