Introduction
Pywedge is a new technique for data visualization, data processing, and EDA
Pywedge is a pip installable Python package that intends to,
- Make multiple charts in a single line of code, to enable the user to quickly read through the charts and can make informed choices in pre-processing steps
- Quickly preprocess the data by taking the user’s preferred choice of pre-processing techniques & it returns the cleaned datasets to the user in the first step.
- Make a baseline model summary, which can return ten various baseline models, which can point the user to explore the best performing baseline model.
Pywedge intends to help the user by quickly making charts, preprocessing the data, and rightly point out the best performing baseline model for the given dataset so that the user can spend quality time tuning such a model algorithm.
Pywedge_Charts()
Makes 8 different types of interactive Charts with interactive axis selection widgets in a single line of code for the given dataset.
Different types of Charts viz,
- Scatter Plot
- Pie Chart
- Bar Plot
- Violin Plot
- Box Plot
- Distribution Plot
- Histogram
- Correlation Plot
Install pywedge
pip install pywedgeImporting the required libraries
import pandas as pd
import pywedge as pw
Loading the Dataset
We will use the breast cancer dataset for exploring pywedge
df = pd.read_csv(‘Downloads/breast_cancer.csv’)
df.head()
Visualization
mc = pw.Pywedge_Charts(df, c=None, y=”diagnosis” )
# For Visualization
chart = mc.make_charts()
In the output above we can clearly see visualize the interface which is created by pywedge. We can select different types of charts and use different variables to visualize the data.
Try this interesting and useful library on different datasets.
Thanks for reading!
If you want to get in touch with me, feel free to reach me on alam.jane61@gmail.com or my Linkedin Profile https://www.linkedin.com/in/jane-alam-90824396/. You can view my Github https://github.com/alamjane for different data science projects and packages tutorials.
