Pandas DataFrame Methods reset_index(), sample(), set_axis(), set_index(), take(), and truncate() – Finxter

The Pandas DataFrame has several Re-indexing/Selection/Label Manipulations methods. When applied to a DataFrame, these methods evaluate, modify the elements and return the results.

This is Part 10 of the DataFrame methods series:

• Part 1 focuses on the DataFrame methods abs(), all(), any(), clip(), corr(), and corrwith().
• Part 2 focuses on the DataFrame methods count(), cov(), cummax(), cummin(), cumprod(), cumsum().
• Part 3 focuses on the DataFrame methods describe(), diff(), eval(), kurtosis().
• Part 4 focuses on the DataFrame methods mad(), min(), max(), mean(), median(), and mode().
• Part 5 focuses on the DataFrame methods pct_change(), quantile(), rank(), round(), prod(), and product().
• Part 6 focuses on the DataFrame methods add_prefix(), add_suffix(), and align().
• Part 7 focuses on the DataFrame methods at_time(), between_time(), drop(), drop_duplicates() and duplicated().
• Part 8 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 9 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 10 focuses on the DataFrame methods reset_index(), sample(), set_axis(), set_index(), take(), and truncate()

Getting Started

Remember to add the Required Starter 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
import numpy as np 

Before any data manipulation can occur, two new libraries will require installation.

• The pandas library enables access to/from a DataFrame.
• The numpy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, 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.

$ pip install numpy

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

Feel free to check out the correct ways of installing those libraries here:

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

DataFrame reset_index()

The reset_index() method resets the DataFrames index and reverts the DataFrame to the default (original) index.

The syntax for this method is as follows:

DataFrame.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill="")

For this example, we have three (3) Classical Composers with some details about their life. They will be assigned levels based on the difficulty of their compositions.

Code – reset_index():

data = {'Composer':  ['Chopin', 'Listz', 'Haydn'],
        'Born':            [1810, 1811, 1732],
         'Country':      ['France', 'Austria', 'Austria']}

index = {'Level-1', 'Level-2', 'Level-3'}
df = pd.DataFrame(data, index)
print(df)

df.reset_index(inplace=True, drop=True)
print(df)

• Line [1] creates a dictionary of lists and saves it to data.
• Line [2] sets index labels for the Composers and saves them to the variable index.
• Line [3] creates a DataFrame and assigns it to df.
• Line [4] outputs the result to the terminal.
• Line [5] resets the DataFrame index (reset_index()) back to the original integer index.
• Line [6] outputs the result to the terminal.

Output:

df

result

Another way to accomplish the above task is to use the concat() method.

Code – concat():

data = {'Composer':  ['Chopin', 'Listz', 'Haydn'],
        'Born':            [1810, 1811, 1732],
        'Country':      ['France', 'Austria', 'Austria']}

index = {'Level-1', 'Level-2', 'Level-3'}
df = pd.DataFrame(data, index)
print(df)

df1 = pd.concat([df], ignore_index=True)
print(df)

• Line [1] creates a dictionary of lists and saves it to data.
• Line [2] sets index labels for the Composers and saves them to the variable index.
• Line [3] creates a DataFrame and assigns it to df.
• Line [4] outputs the result to the terminal.
• Line [5] resets the DataFrame index (concat()) back to the original integer index.
• Line [6] outputs the result to the terminal.

Output:

df

result

DataFrame sample()

The sample() method retrieves and returns a random sample of columns or rows (depending on the selected axis) from a DataFrame/Series.

The syntax for this method is as follows:

DataFrame.sample(n=None, frac=None, replace=False, weights=None, 
                 random_state=None, axis=None, ignore_index=False)

For these examples, the finxters.csv data saves to a DataFrame to manipulate the data.

df = pd.read_csv('finxters.csv')
result = df['First_Name'].sample(n=3, random_state=1)
print(result)

• Line [1] reads in the comma-separated CSV file and saves it to df.
• Line [2] retrieves three (3) random ‘First_Names‘ values and saves them to the result variable.
• Line [3] outputs the result to the terminal.

Output:

In this example, the np.random.randint() method calls and generates random integers on a selected column.

Code – Example 2:

df   = pd.read_csv('finxters.csv')
nums = np.random.randint(df['FID'], size=50)
result = df['FID'].sample(n=3, random_state=nums)
print(result)

• Line [1] reads in the comma-separated CSV file and saves it to df.
• Line [2] generates random integers (np.random.randint()) from the CSV file based on the ‘FID‘ column.
• Line [3] retrieves three (3) integers from the random numbers generated on Line [2]. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output:

DataFrame set_axis()

The set_axis() method assigns index(es) to the selected axis.

The syntax for this method is as follows:

DataFrame.set_axis(labels, axis=0, inplace=False)

For these examples, the index saves to the selected axis.

In this example, we set the axis to the row index.

Code – Example 1:

df = pd.DataFrame({'Micah': [123, 120, 144], 
                   'Paula': [129, 125, 90],
                   'Chloe': [101, 95,  124]})
print(df)
				   
result = df.set_axis(['Day-1', 'Day-2', 'Day-3'], axis="index")
print(result)

• Line [1] creates a dictionary of lists and saves it to df.
• Line [2] outputs the DataFrame (df) to the terminal.
• Line [3] sets the new axis for the DataFrame and saves it to the result variable.
• Line [4] outputs the result to the terminal.

Output:

df

result

In this example, we set the axis to the column index.

Code – Example 2:

df = pd.DataFrame({'Micah': [123, 120, 144], 
                   'Paula': [129, 125, 90],
                   'Chloe': [101, 95,  124]})
print(df)
				   
result = df.set_axis(['Micah M', 'Paula D', 'Chloe J'], axis="columns")
print(result)

• Line [1] creates a dictionary of lists and saves it to df.
• Line [2] outputs the DataFrame (df) to the terminal.
• Line [3] sets the new axis for the DataFrame and saves it to the result variable.
• Line [4] outputs the result to the terminal.

Output:

df

result

DataFrame set_index()

The set_index() method sets the DataFrame index using existing columns/rows.

The syntax for this method is as follows:

DataFrame.set_index(keys, drop=True, append=False, 
                    inplace=False, verify_integrity=False)

For this example, the Salesperson(s) who sold the highest number of cars over four (4) months display.

df = pd.DataFrame({'Salesman':    ['Greg', 'Fred', 'Helen', 'Tim'],
                   'Month':         ['Jan', 'Feb', 'Mar', 'Apr'],
                   'Sold':             [165, 156, 196, 124]})

result = df.set_index('Salesperson')
print(result)

• Line [1] creates a Dictionary of Lists and saves it to df.
• Line [2] sets the index to ‘Salesperson’ and saves it to the result variable.
• Line [3] outputs the result to the terminal.

Output:

DataFrame take()

The take() method returns the elements (data) across the selected axis. The indexing performs on the actual position of the DataFrame element.

🛑 Note: This method has been deprecated (since version 1.0.0).

The syntax for this method is as follows:

DataFrame.take(indices, axis=0, is_copy=None, **kwargs)

For this example, the finxters.csv data saves to a DataFrame to manipulate the data.

df   = pd.read_csv('finxters.csv')
result = df.take([30, 31], axis=0)
print(result)

• Line [1] reads in the comma-separated CSV file and saves it to df.
• Line [2] takes the 30^th and 31^st row of the CSV file and saves it to the result variable.
• Line [3] outputs the result to the terminal.

Output:

DataFrame truncate()

The truncate() method truncates a DataFrame/Series before and after a selected index value.

The syntax for this method is as follows:

DataFrame.truncate(before=None, after=None, axis=None, copy=True)

Parameters:

For this example, we have a DataFrame containing a message.

df = pd.DataFrame({'C': ['f', 'i', 'n', 'x', 't', 'e', 'r'],
                   'O': ['p', 'u', 'z', 'z', 'l', 'e', 's'],
                   'D': ['a', 'w', 'e', 's', 'o', 'm', 'e'],
                   'E': ['w', 'a', 'y', '-', 't', 'o', '-'],
                   'R': ['l', 'e', 'r', 'n', '!', '!', '!']},
                   index=[1, 2, 3, 4, 5, 6, 7])
print(df)

result = df.truncate(before=2, after=4)                  
print(result)

• Line [1] creates a DataFrame from a dictionary of lists and saves it to df.
• Line [2] outputs the result to the terminal.
• Line [3] truncates and saves the output to the result variable.
• Line [4] outputs the result to the terminal.

Output:

df

result

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