Function to compare count of reviews of electronic products from Ecommerce websites by date.

plot_function

#import libraries
import numpy as np
import pandas as pd
import datetime
import matplotlib.pyplot as plt
import json
from matplotlib.pyplot import figure


# import json files containing reviews 
# paste path of files
data=[open(r'.....\applewatch6-2960-reviews.json','rb'),
      open(r'.....\applewatchSE-1709-reviews.json','rb'),
      open(r'.....\fitbitversa2-5000-reviews.json','rb'),
      open(r'.....\fitbitversa3-2494-reviews.json','rb'),
      open(r'.....\samsunggalaxy2-2711-reviews.json','rb')]


# This function returns a list of dataframes with date and count of reviews sorted based on their dates 
def plot_data(data):
    data_df=[]
    for i in data:
        x=json.load(i)
        x=pd.DataFrame(pd.DataFrame(x.items())[1][14])
        x['date'] = pd.to_datetime(x['date'])
        month_year=[]
        for j in x["date"]:
            month_year.append(j.strftime("%m-%Y"))
        x['month_year'] = month_year
        x['month_year'] = pd.to_datetime(x['month_year'])
        data_df.append(pd.DataFrame(x['month_year'].value_counts()).reset_index().rename(columns={"index" : "mon_year",
                                            'month_year':'counts'}).sort_values(by='mon_year'))
    return data_df
    

#Run the function
data_df=plot_data(data) 

    
#create new list excluding the reviews from unwanted month.Here Unwanted month is 7th month as there are very less reviews in 7th month
new_data_df=[]
for i in range(0,len(data_df)):
    new_data_df.append(data_df[i][data_df[i]['mon_year'].dt.month != 7])
    
    
# Plots
figure(figsize=(18, 10), dpi=80)
for i in new_data_df:
    plt.plot(i['mon_year'], i['counts'],marker='o')
    plt.legend(['Apple','Bose','Jabra','Samsung','Powerbeats'])
    
plt.title('count Vs Year')
plt.xlabel('Year')
plt.ylabel('count Rate')
plt.show()