juspy is a library for making EDA and Modelling in Python quick and convinient. It's built on top of:

• pandas
• numpy
• seaborn
• and many such great libraries

Our reccomended path of getting started with juspy

• Download & install Anaconda
• Open Anaconda Prompt
• In Anaconda Prompt type

conda install -c conda-forge jupyterlab

• and then

pip install juspy
jupyterlab

Incase if you're having any trouble regarding installation or dependencies, kindly make sure you're using updated versions

conda update conda
conda update pip
conda update python

1. To test library's proper installation and working:

   import juspy
   print(juspy.__version__)
   
   from juspy import greet
   print(greet.namastey())

   
   

   Or, try greeting

   print(greet.hello())
   print(greet.ni_hao())
   print(greet.hola())
   print(greet.bonjour())
   print(greet.schatz())
   print(greet.ahlan())
   print(greet.privet())



If they're producing some output, we're good to go

2. jpplot.confusion_matrix()

   import numpy as np
   import seaborn as sns
   from sklearn.metrics import confusion_matrix
   
   y_true = [0, 1, 0, 1, 0, 1, 0]
   y_pred = [1, 1, 1, 0, 1, 0, 1]
   cf_matrix = confusion_matrix(y_true, y_pred)
   
   from juspy import plot as jpplot
   jpplot.confusion_matrix(cf_matrix)

   
   




3. jpplot.piechart()

   import pandas as pd
   df_name = pd.read_csv("https://raw.githubusercontent.com/juspreet51/juspy/main/src/juspy/datasets/default.csv")
   
   from juspy import plot as jpplot
   jpplot.piechart(df_name["student"])

   
   





Future Release:

4. from juspy.linear_models import LinearRegression
   

   from juspy.linear_models import LinearRegression as lin_reg