Reading data from external sources into a DataFrame

• Reading from text file
• Reading from Database
• Scraping data using bs4
• Reading data from PDF files (pyPDF2)

Reading from text file

# Using encoding = "ISO-8859-1" companies = pd.read_csv("companies.txt", sep="\t", encoding = "ISO-8859-1") companies.head()

Reading from Database

import pymysql # create a connection object 'conn' conn = pymysql.connect(host="localhost", # your host, localhost for your local machine user="root", # your username, usually "root" for localhost passwd="yourpassword", # your password db="world") # name of the data base; world comes inbuilt with mysql # create a cursor object c c = conn.cursor() # execute a query using c.execute c.execute("select * from city;") # getting the first row of data as a tuple all_rows = c.fetchall() # to get only the first row, use c.fetchone() instead # notice that it returns a tuple of tuples: each row is a tuple print(type(all_rows)) # printing the first few rows print(all_rows[:5]) df = pd.DataFrame(list(all_rows), columns=["ID", "Name", "Country", "District", "Population"]) df.head()

Scraping data using bs4

• Refer Lyrics Extraction and Auto File Renamer to see how web-scraping works
• Convert scraped data to desirable format so as to import it as a dataframe in Pandas

Reading data from PDF files (pyPDF2)

See PyPDF2 Github and Docs