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Pandas Read CSV


Read CSV Files

A simple way to store big data sets is to use CSV files (comma separated files).

CSV files contains plain text and is a well know format that can be read by everyone including Pandas.

In our examples we will be using a CSV file called 'data.csv'.

Download data.csv. or Open data.csv

Example

Load the CSV into a DataFrame:

import pandas as pd

df = pd.read_csv('data.csv')

print(df.to_string()) 
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Tip: use to_string() to print the entire DataFrame.

If you have a large DataFrame with many rows, Pandas will only return the first 5 rows, and the last 5 rows:

Example

Print the DataFrame without the to_string() method:

import pandas as pd

df = pd.read_csv('data.csv')

print(df) 
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max_rows

The number of rows returned is defined in Pandas option settings.

You can check your system's maximum rows with the pd.options.display.max_rows statement.

Example

Check the number of maximum returned rows:

import pandas as pd

print(pd.options.display.max_rows) 
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In my system the number is 60, which means that if the DataFrame contains more than 60 rows, the print(df) statement will return only the headers and the first and last 5 rows.

You can change the maximum rows number with the same statement.

Example

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

import pandas as pd

pd.options.display.max_rows = 9999

df = pd.read_csv('data.csv')

print(df) 
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