# Function to count number of links in dataset, it will add extra column and store information in it.ĭf = df.Message.apply(lambda x: re.findall(URLPATTERN, x)).str.len() # Counting number of word's in each messageĭf = df.apply(lambda s : len(s.split(' '))) # Counting number of letters in each messageĭf = df.apply(lambda s : len(s)) # Rearranging the columns for better understandingĭf = df] of null values in datasetĭf.unique() Now, let’s preprocess our dataset and try to extract useful information from it: # Adding one more column of 'Day' for better analysis, here we use datetime library which help us to do this task easily.ĭf = df.dt.weekday.map(weeks) Now, let’s check the basic information of our dataset and clean the dataset : # Checking shape of dataset. Here I choose my college official WhatsApp group to analyze the pattern students were following therefore in some of the snapshots I blur the contact information of my college faculty and my classmates, sorry for that.

Here I used different python libraries which help me to extract useful information from raw data. The aim of this article is to provide step by step guide to build our own WhatsApp analyzer using python. It tracks our conversation and analyses how much time we are spending or saying it as “wasting” on WhatsApp. WhatsApp Analyzer means we are analyzing our WhatsApp group activities. Leave all these things and let’s understand what actually WhatsApp analyzer means? It has more than 2B users worldwide and “According to one survey an average user spends more than 195 minutes per week on WhatsApp”.

It is one of the favorite social media platforms among all of us because of its attractive features. Today one of the trendy social media platforms is…. CODE language-jsx keep-markup - var app = require('express')() var http = require('http').createServer(app) const PORT = 8080 http.This article was published as a part of the Data Science Blogathon. Inside server.js place the following code:

Multi chat client py