Reading & Write Files

Learning Objectives

• Reading a data file

• Writing a data file

Reading Data Files

• It is always good to be able to create a dataframe by hand. But, generally, we don’t create our own data by hand. We work on the data that already exists.

• Data exists in number of formats. The most basic of these is the csv file. csv stands for comma-separated-value

What is a CSV file?

• CSV files are normally created by programs that handle large amounts of data. They are a convenient way to export data from spreadsheets and databases as well as import or use it in other programs.

• CSV (Comma Separated Values) is a simple file format used to store tabular data, such as a spreadsheet or database.

• A CSV file stores tabular data (numbers and text) in plain text.

• Each line of the file is a data record.

• Each record consists of one or more fields, separated by commas.

• The use of the comma as a field separator is the source of the name for this file format.

How does CSV look like?

Working with CSV files in Python

• For working CSV files in python, there is an inbuilt function named read_csv.

• However, a common method for working with CSV files is using Pandas. It makes importing and analyzing data much easier.

• One crucial feature of Pandas is its ability to write and read Excel, CSV, and many other types of files.

Pandas read_csv

• Functions like the Pandas read_csv() method enable you to work with files effectively.

• The read_csv() function reads the CSV file into a DataFrame object.

• A CSV file is similar to a two-dimensional table and the DataFrame object represents two dimensional tabular view.

• The most basic way to read a csv file in Pandas:

• Now, let's understand how to provide filename

• There are many other things one can do through this function only to change the returned object completely.

• For instance, one can read a csv file not only locally, but from a URL through read_csv or one can choose what columns needed to export so that we don’t have to edit the array later.

• These modifications can be done by the various arguments it takes.

• We don’t need to memorise all the arguments though, let’s have a look at few important ones in the next two slides.

Pandas to_csv with example

• The easiest way to write DataFrames to CSV files is using the Pandas to_csv function.

• Syntax:

Where df is the name of your DataFrame

• If you want to export without the index, simply add index=False

• Example: