Pandas Read CSV

Read CSV Files

A common method for storing large datasets is using CSV (comma-separated values) files. These files contain plain text and are widely recognized, making them easily readable by tools like Pandas. In our examples, we will use a CSV file named ‘data.csv’.

Example

Load the CSV file into a DataFrame:

import pandas as pd

df = pd.read_csv(‘data.csv’)

print(df.to_string())

Tip: Use to_string() to display the entire DataFrame.

If you have a large DataFrame with numerous rows, Pandas will display only the first 5 and the last 5 rows.

Example

Print the DataFrame without using the to_string() method:

import pandas as pd

df = pd.read_csv(‘data.csv’)

print(df)

max_rows

The number of rows displayed is controlled by Pandas’ option settings. You can check your system’s maximum row limit using the pd.options.display.max_rows statement.

Example

Check the maximum number of rows that can be returned:

import pandas as pd

print(pd.options.display.max_rows)

On my system, the maximum row limit is 60, meaning that if the DataFrame contains more than 60 rows, the print(df) statement will only display the headers along with the first and last 5 rows.

You can adjust this maximum row limit using the same statement.

Example

Increase the maximum number of rows to display the full DataFrame:

import pandas as pd

pd.options.display.max_rows = 9999

df = pd.read_csv(‘data.csv’)

print(df)