pip install klibExploring klib Library in Python
Introduction
Featuring a user-friendly design, klib is a Python library tailored for efficient data exploration and preprocessing tasks. It provides a suite of functions empowering data scientists and analysts to handle various data manipulation processes seamlessly. Compatible with diverse data sources, including CSV, JSON, and Excel, klib is lightweight, easy to install, and enhances the efficiency of data manipulation workflows.
Getting started:
To begin using the klib library, start by installing it on your system:
Importing Libraries
import pandas as pd
import klib
import warnings
warnings.filterwarnings('ignore')Importing Dataset
df = pd.read_csv('C:/Users/SANKHYA/Downloads/dataset.csv')
df.head()| PlayerID | Position | Height | Weight | Shuttle (20yd) | 40yd | 60ydShuttle | |
|---|---|---|---|---|---|---|---|
| 0 | 10000 | Center Back | 69.8 | 198 | 4.60 | 4.42 | 11.91 |
| 1 | 10001 | Center | 74.8 | 266 | 4.60 | 4.97 | NaN |
| 2 | 10002 | Full Back | 71.8 | 217 | NaN | NaN | NaN |
| 3 | 10003 | Center | 75.0 | 279 | 4.33 | 5.13 | NaN |
| 4 | 10004 | Wide Receiver | 72.1 | 202 | 4.52 | 4.64 | 11.85 |
Let’s try out some functions:
klib.corr_mat () - Generates a color-coded correlation matrix.
klib.corr_mat(df)| PlayerID | Height | Weight | Shuttle (20yd) | 40yd | 60ydShuttle | |
|---|---|---|---|---|---|---|
| PlayerID | 1.00 | 0.04 | 0.02 | 0.10 | -0.00 | 0.05 |
| Height | 0.04 | 1.00 | 0.79 | 0.50 | 0.65 | 0.38 |
| Weight | 0.02 | 0.79 | 1.00 | 0.64 | 0.82 | 0.50 |
| Shuttle (20yd) | 0.10 | 0.50 | 0.64 | 1.00 | 0.68 | 0.78 |
| 40yd | -0.00 | 0.65 | 0.82 | 0.68 | 1.00 | 0.51 |
| 60ydShuttle | 0.05 | 0.38 | 0.50 | 0.78 | 0.51 | 1.00 |
klib.corr_plot() - Generates a color-coded heatmap that is well-suited for visualizing correlations.
klib.corr_plot(df, figsize=(10, 6))<Axes: title={'center': 'Feature-correlation (pearson)'}>
klib.corr_plot(df, target=” “) - Generates a color-coded heatmap of the feature column.
klib.corr_plot(df, target='Height', figsize=(10, 6))<Axes: title={'center': 'Feature-correlation (pearson)'}>
klib.dist_plot - Generates a distribution plot for each numeric feature.
klib.dist_plot(df)<Axes: xlabel='PlayerID', ylabel='Density'>
klib.missingval_plot() - Generates a visual representation providing information about missing values.
klib.missingval_plot(df)GridSpec(6, 6)
Additional Functions:
klib.cat_plot(df) - Visualize categorical feature count and frequency
klib.corr_interactive_plot(df).show() - Generate an interactive correlation plot using Plotly
klib.convert_datatypes(df) - Converts data types to more efficient ones
klib.drop_missing(df) - Removes rows with missing values, incorporated into the data cleaning process
klib.mv_col_handling(df) - Drops features with a high ratio of missing values based on informational content
klib.pool_duplicate_subsets(df) - Efficiently pools subsets of columns based on duplicates with minimal loss of information.
Conclusion:
klib stands out as a versatile Python library, offering a seamless experience from data import and cleaning to advanced analysis and preprocessing. Its comprehensive functions simplify the data preparation journey for analysts and data scientists, ensuring an efficient and insightful workflow. With a user-friendly interface and well-documented features, klib empowers users to derive meaningful insights from their data.