datastand 2.3

A python package to help Data Scientists, Machine Learning Engineers and Analysts better understand data. Gives quick insights about given data; general dataset statistics, size and shape of dataset, number of unique data types, number of numerical and non-numerical columns, small overview of dataset, missing data statistics, missing data heatmap and provides methodology to impute missing data.

datastand

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Why datastand? Data + Understand
A python package to help Data Scientists, Machine Learning Engineers and Analysts better understand data. Gives quick insights about a given dataset.

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Installation

Run the following command on the terminal to install the package:

pip install datastand

Usage :

Code:

from datastand import datastand
import pandas as pd

df = pd.read_csv("path/to/target/dataframe")

datastand(df)

Output:

General stats:
______________
Size of DataFrame: 309200
Shape of DataFrame: (3865, 80)
Number of unique data types : {dtype('int64'), dtype('O'), dtype('float64')}
Number of numerical columns: 79
Number of non-numerical columns: 1


Missing data:
=======================
DataFrame contains 185698 missing values(60.06%) as follows column-wise:
-----------------------------------------------------------------------
galactic year                                                                   0
galaxy                                                                          0
existence expectancy index                                                      1
existence expectancy at birth                                                   1
Gross income per capita                                                        28
                                                                             ... 
Adjusted net savings                                                         2953
Creature Immunodeficiency Disease prevalence, adult (% ages 15-49), total    2924
Private galaxy capital flows (% of GGP)                                      2991
Gender Inequality Index (GII)                                                3021
y                                                                               0
Length: 80, dtype: int64
-----------------------------------------------------------------------

Do you wish to long-list missing data statistics?(y/n): y
.
.
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Code:

# This function is already available in the DataStand class and also available separately
# Here we're running it separately 
from datastand import plot_missing

plot_missing(df)

Output:

Code:

from datastand import impute_missing

impute_missing(df)

Output:

Imputing missing data...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [00:02<00:00, 30.52it/s]
Imputation complete.

Author/Maintainer

Vincent N. [LinkedIn] [Twitter]