datastand 2.5.0

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

datastand

---

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.

---

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:
==================
Shape of DataFrame: (1202, 13)
Number of unique data types : {dtype('int64'), dtype('O')}
Number of numerical columns: 2
Number of non-numerical columns: 11


Missing data:
=======================
DataFrame contains 2670 missing values (17.09%) as follows column-wise:
-----------------------------------------------------------------------
Gender                 41
Car_Category          372
Subject_Car_Colour    697
Subject_Car_Make      248
LGA_Name              656
State                 656
dtype: int64
-----------------------------------------------------------------------

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

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]