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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.
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.
Run the following command on the terminal to install the package:
pip install datastand
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.
Vincent N. [LinkedIn] [Twitter]
FAQs
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.
We found that datastand demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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