Write some introduction about your project here: describe the dataset, where you got it from, what you're trying to do with it, and which tools & techniques you're using. You can also mention about the course, and what you've learned from it.
As a first step, let's upload our Jupyter notebook to Jovian.ml.
!pip install jovian --upgrade --quiet !pip install numpy --upgrade --quiet !pip install pandas --upgrade --quiet !pip install matplotlib --upgrade --quiet !pip install seaborn --upgrade --quiet
project_name = "whatsapp-chat-analysis-course-project-try"
[jovian] Attempting to save notebook.. [jovian] Please enter your API key ( from https://jovian.ml/ ): API KEY: ········ [jovian] Updating notebook "edsenmichaelcy/whatsapp-chat-analysis-course-project-try" on https://jovian.ml/ [jovian] Uploading notebook.. [jovian] Capturing environment.. [jovian] Committed successfully! https://jovian.ml/edsenmichaelcy/whatsapp-chat-analysis-course-project-try
import os import pandas as pd import re import datetime as time import jovian import numpy as np import matplotlib.pyplot as plt import seaborn as sns
from urllib.request import urlretrieve urlretrieve('https://mybinder.org/v2/git/https%3A%2F%2Fjovian.ml%2Fapi%2Fgit%2F259529b84da942178de5c636545935a7_1.git/c81c26e3a5f55908f2a7136c1f6f381d505a783f');
whatsapp_df = pd.read_fwf('WhatsApp_Chat_group.txt', header = None) whatsapp_df
<class 'pandas.core.frame.DataFrame'> RangeIndex: 23330 entries, 0 to 23329 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 0 23177 non-null object 1 1 23087 non-null object 2 2 788 non-null object dtypes: object(3) memory usage: 546.9+ KB
After that we will use the info() that provided by the pandas to understand the datatype in the dataframe. As you can see we need to do some clearning such as the date and the Media omitted.
def txtTodf(txt_file): '''Convert WhatsApp chat log text file to a Pandas dataframe.''' # some regex to account for messages taking up multiple lines pat = re.compile(r'^(\d\d\/\d\d\/\d\d\d\d.*?)(?=^^\d\d\/\d\d\/\d\d\d\d|\Z)', re.S | re.M) with open(txt_file) as file: data = [m.group(1).strip().replace('\n', ' ') for m in pat.finditer(file.read())] user = ; message = ; datetime =  for row in data: # timestamp is before the first dash datetime.append(row.split(' - ')) # sender is between am/pm, dash and colon try: s = re.search('m - (.*?):', row).group(1) user.append(s) except: user.append('') # message content is after the first colon try: message.append(row.split(': ', 1)) except: message.append('') df = pd.DataFrame(zip(datetime, user, message), columns=['datetime', 'user', 'message']) df['datetime'] = pd.to_datetime(df.datetime, format='%d/%m/%Y, %I:%M %p') # remove events not associated with a sender df = df[df.user != ''].reset_index(drop=True) return df whatsapp_df = txtTodf('WhatsApp_Chat_group.txt')