Pandas DataFrame Computations & Descriptive Stats – Part 4

The Pandas DataFrame has several methods concerning Computations and Descriptive Stats. When applied to a DataFrame, these methods evaluate the elements and return the results.

  • 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()

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.

Required Starter Code

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 mad()

The mad() method (Mean Absolute Deviation) is the average distance of all DataFrame elements from the mean.

To fully understand MAD from a mathematical point of view, feel free to watch this short tutorial:

The syntax for this method is as follows:

DataFrame.mad(axis=None, skipna=None, level=None)
ParameterDescription
axisIf zero (0) or index is selected, apply the function to each column. Default is None. If one (1) is selected, apply the function to each row.
skipnaIf this parameter is True, any NaN/NULL value(s) ignored. If False, all value(s) included: valid or empty. If no value, then None is assumed.
levelSet the appropriate parameter if the DataFrame/Series is multi-level. If no value, then None is assumed.

This example retrieves the MAD of four (4) Hockey Teams.

df_teams = pd.DataFrame({'Bruins':   [4, 5, 9],
                         'Oilers':   [3, 6, 10],
                         'Leafs':    [2, 7, 11],
                         'Flames': [1, 8, 12]})

result = df_teams.mad(axis=0).apply(lambda x:round(x,3))
print(result)
  • Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
  • Line [2] uses the mad() method with the axis parameter set to columns to calculate MAD from the DataFrame. The lambda function formats the output to three (3) decimal places. This output saves to the result variable.
  • Line [3] outputs the result to the terminal.

Output:

Bruins2.000
Oilers2.444
Leafs3.111
Flames4.000
dtype:float64

DataFrame min()

The min() method returns the smallest value(s) from a DataFrame/Series. The following methods can accomplish this task:

The syntax for this method is as follows:

DataFrame.min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
ParameterDescription
axisIf zero (0) or index is selected, apply the function to each column. Default is None. If one (1) is selected, apply the function to each row.
skipnaIf this parameter is True, any NaN/NULL value(s) ignored. If False, all value(s) included: valid or empty. If no value, then None is assumed.
levelSet the appropriate parameter if the DataFrame/Series is multi-level. If no value, then None is assumed.
numeric_onlyOnly include columns that contain integers, floats, or boolean values.
**kwargsThis is where you can add additional keywords.

For this example, we will determine which Team(s) have the smallest amounts of wins, losses, or ties.

Code Example 1:

df_teams = pd.DataFrame({'Bruins':   [4, 5,  9],
                         'Oilers':    [3, 6, 14],
                         'Leafs':     [2, 7, 11],
                         'Flames':  [21, 8, 7]})

result = df_teams.min(axis=0)
print(result)
  • Line [1] creates a DataFrame from a dictionary of lists and saves it to df_teams.
  • Line [2] uses the min() method with the axis parameter set to columns to retrieve the minimum value(s) from the DataFrame. This output saves to the result variable.
  • Line [3] outputs the result to the terminal.

Output:

Bruins4
Oilers3
Leafs2
Flames8
dtype:int64

This example uses two (2) arrays and retrieves the minimum value(s) of the Series.

Code Example 2:

c11_grades = [63, 78, 83, 93]
c12_grades = [73, 84, 79, 83]

result = np.minimum(c11_grades, c12_grades)
print(result)
  • Line [1-2] create lists of random grades and assigns them to the appropriate variable.
  • Line [3] uses NumPy minimum to compare the two (2) arrays. This output saves to the result variable.
  • Line [4] outputs the result to the terminal.

Output:

[63 78 79 83]

DataFrame max()

The max() method returns the largest value(s) from a DataFrame/Series. The following methods can accomplish this task:

The syntax for this method is as follows:

DataFrame.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
ParameterDescription
axisIf zero (0) or index is selected, apply the function to each column. Default is None. If one (1) is selected, apply the function to each row.
skipnaIf this parameter is True, any NaN/NULL value(s) ignored. If False, all value(s) included: valid or empty. If no value, then None is assumed.
levelSet the appropriate parameter if the DataFrame/Series is multi-level. If no value, then None is assumed.
numeric_onlyOnly include columns that contain integers, floats, or boolean values.
**kwargsThis is where you can add additional keywords.

For this example, we will determine which Team(s) have the largest amounts of wins, losses, or ties.

Code Example 1:

df_teams = pd.DataFrame({'Bruins':   [4, 5,  9],
                         'Oilers':    [3, 6, 14],
                         'Leafs':     [2, 7, 11],
                         'Flames':  [21, 8, 7]})

result = df_teams.max(axis=0)
print(result)
  • Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
  • Line [2] uses max() with the axis parameter set to columns to retrieve the maximum value(s) from the DataFrame. This output saves to the result variable.
  • Line [3] outputs the result to the terminal.

Output:

Bruins9
Oilers14
Leafs11
Flames21
dtype:int64

This example uses two (2) arrays and retrieves the maximum value(s) of the Series.

Code Example 2:

c11_grades = [63, 78, 83, 93]
c12_grades = [73, 84, 79, 83]

result = np.maximum(c11_grades, c12_grades)
print(result)
  • Line [1-2] create lists of random grades and assigns them to the appropriate variable.
  • Line [3] uses the NumPy library maximum function to compare the two (2) arrays. This output saves to the result variable.
  • Line [4] outputs the result to the terminal.

Output:

[73 84 83 93]

DataFrame mean()

The mean() method returns the average of the DataFrame/Series across a requested axis. If a DataFrame is used, the results will return a Series. If a Series is used, the result will return a single number (float).

The following methods can accomplish this task:

  • The DataFrame.mean() method, or
  •  The Series.mean() method

The syntax for this method is as follows:

DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
ParameterDescription
axisIf zero (0) or index is selected, apply the function to each column. Default is None. If one (1) is selected, apply the function to each row.
skipnaIf this parameter is True, any NaN/NULL value(s) ignored. If False, all value(s) included: valid or empty. If no value, then None is assumed.
levelSet the appropriate parameter if the DataFrame/Series is multi-level. If no value, then None is assumed.
numeric_onlyOnly include columns that contain integers, floats, or boolean values.
**kwargsThis is where you can add additional keywords.

For this example, we will determine average wins, losses and ties for our Hockey Teams.

Code Example 1:

df_teams = pd.DataFrame({'Bruins':   [4, 5,  9],
                         'Oilers':    [3, 6, 14],
                         'Leafs':     [2, 7, 11],
                         'Flames':  [21, 8, 7]})

result = df_teams.mean(axis=0).apply(lambda x:round(x,2))
print(result)
  • Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
  • Line [2] uses the mean() method with the axis parameter set to columns to calculate means (averages) from the DataFrame. The lambda function formats the output to two (2) decimal places. This output saves to the result variable.
  • Line [3] outputs the result to the terminal.

Output:

Bruins6.00
Oilers7.67
Leafs6.67
Flames12.00
dtype:float64

For this example, Alice Accord, an employee of Rivers Clothing has logged her hours for the week. Let’s calculate the mean (average) hours worked per day.

Code Example 2:

hours  = pd.Series([40.5, 37.5, 40, 55])
result = hours.mean()
print(result)
  • Line [1] creates a Series of hours worked for the week and saves to hours.
  • Line [2] uses the mean() method to calculate the mean (average). This output saves to the result variable.
  • Line [3] outputs the result to the terminal.

Output:

42.25

DataFrame median()

The median() method calculates and returns the median of DataFrame/Series elements across a requested axis. In other words, the median determines the middle number(s) of the dataset.

To fully understand median from a mathematical point of view, watch this short tutorial:

The syntax for this method is as follows:

DataFrame.median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
ParameterDescription
axisIf zero (0) or index is selected, apply the function to each column. Default is None. If one (1) is selected, apply the function to each row.
skipnaIf this parameter is True, any NaN/NULL value(s) ignored. If False, all value(s) included: valid or empty. If no value, then None is assumed.
levelSet the appropriate parameter if the DataFrame/Series is multi-level. If no value, then None is assumed.
numeric_onlyOnly include columns that contain integers, floats, or boolean values.
**kwargsThis is where you can add additional keywords.

For this example, we will determine the median value(2) for our Hockey Teams.

df_teams = pd.DataFrame({'Bruins':   [4, 5,  9],
                         'Oilers':    [3, 6, 14],
                         'Leafs':     [2, 7, 11],
                         'Flames':  [21, 8, 7]})

result = df_teams.median(axis=0)
print(result)
  • Line [1] creates a DataFrame from a dictionary of lists and saves it to df_teams.
  • Line [2] uses the median() method to calculate the median of the Teams. This output saves to the result variable.
  • Line [3] outputs the result to the terminal.

Output:

Bruins5.0
Oilers6.0
Leafs7.0
Flames8.0
dtype:float64

DataFrame mode()

The mode() method determines the most commonly used numbers in a DataFrame/Series.

The syntax for this method is as follows:

DataFrame.mode(axis=0, numeric_only=False, dropna=True)
ParameterDescription
axisIf zero (0) or index is selected, apply the function to each column. Default is None. If one (1) is selected, apply the function to each row.
numeric_onlyOnly include columns that contain integers, floats, or boolean values.
dropnaIf set to True, this parameter ignores all NaN and NaT values. By default, this value is True.

For this example, we determine the numbers that appear more than once.

df_teams = pd.DataFrame({'Bruins':   [4, 5,  9],
                         'Oilers':    [3, 9, 13],
                         'Leafs':     [2, 7, 4],
                         'Flames':  [13, 9, 7]})

result = df_teams.mode(axis=0)
print(result)
  • Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
  • Line [2] uses the mode() method across the column axis. This output saves to the result variable.
  • Line [3] outputs the result to the terminal.

Output:

 BruinsOilersLeafsFlames
04327
15949
2913713

You can see where the numbers come from in this visualization: