Pandas DataFrame head() and tail() Method

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Before any data manipulation can occur, one (1) new library will require installation:

  • The Pandas library enables access to/from a DataFrame.

To install this library, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

If the installations were successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required library.

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd

πŸ’‘ Note: To follow along with the examples below, click here to download the finxters.csv file of auto-generated dummy user data. Move this file to the current working directory.

DataFrame head() and tail()

These methods display n numbers of records (top or bottom). These methods are useful when you are accessing large amounts of data.

If no parameter is entered, by default, five (5) rows display.

  • The head() method returns the top five (5) rows of the DataFrame.
  • The tail() method returns the bottom five (5) rows of the DataFrame

If a parameter is entered in one of the above methods, n number of rows (top/bottom) will display.

Pandas Head | pd.DataFrame.head()

As you will note, the head() method was accessed many times during this article. Both methods are must-haves in your knowledge base.

The syntax for these methods is as follows:

nAn integer value indicating the number of rows to display. By default, five (5) rows display.

For this example, the first five (5) records from the DataFrame and the last five (5) records (tail) from the same DataFrame display.

df_fxs = pd.read_csv('finxters.csv')


More Pandas DataFrame Methods

Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:

Also, check out the full cheat sheet overview of all Pandas DataFrame methods.