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:
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import pandas as pd df = pd.read_csv('data.csv') print(df.to_string())
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:
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import pandas as pd df = pd.read_csv('data.csv') print(df)
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:
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import pandas as pd print(pd.options.display.max_rows)
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:
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import pandas as pd pd.options.display.max_rows = 9999 df = pd.read_csv('data.csv') print(df)
