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.
Preparation
Before any data manipulation can occur, two (2) 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.
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 libraries.
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 import numpy as np
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)
Parameter | Description |
---|---|
axis | If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row. |
skipna | If 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. |
level | Set 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 theaxis
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 theresult
variable. - Line [3] outputs the result to the terminal.
Output
Bruins | 2.000 |
Oilers | 2.444 |
Leafs | 3.111 |
Flames | 4.000 |
dtype: | float64 |
DataFrame min()
The min()
method returns the smallest value(s) from a DataFrame/Series. The following methods can accomplish this task:
- The
DataFrame.min()
method, or - The
numpy.minimum()
method
The syntax for this method is as follows:
DataFrame.min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameter | Description |
---|---|
axis | If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row. |
skipna | If 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. |
level | Set the appropriate parameter if the DataFrame/Series is multi-level. If no value, then None is assumed. |
numeric_only | Only include columns that contain integers, floats, or boolean values. |
**kwargs | This 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 theresult
variable. - Line [3] outputs the result to the terminal.
Output
Bruins | 4 |
Oilers | 3 |
Leafs | 2 |
Flames | 8 |
dtype: | int64 |
This example uses two (2) arrays and retrieves the Series’s minimum value(s).
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] creates 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
DataFrame.max()
method, or - The
n
p
.maximum()
method
The syntax for this method is as follows:
DataFrame.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameter | Description |
---|---|
axis | If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row. |
skipna | If 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. |
level | Set the appropriate parameter if the DataFrame/Series is multi-level. If no value, then None is assumed. |
numeric_only | Only include columns that contain integers, floats, or boolean values. |
**kwargs | This is where you can add additional keywords. |
For this example, we will determine which Team(s) have the most significant 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 theaxis
parameter set to columns to retrieve the maximum value(s) from the DataFrame. This output saves to theresult
variable. - Line [3] outputs the result to the terminal.
Output
Bruins | 9 |
Oilers | 14 |
Leafs | 11 |
Flames | 21 |
dtype: | int64 |
This example uses two (2) arrays and retrieves the Series’s maximum value(s).
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] creates 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)
Parameter | Description |
---|---|
axis | If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row. |
skipna | If 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. |
level | Set the appropriate parameter if the DataFrame/Series is multi-level. If no value, then None is assumed. |
numeric_only | Only include columns that contain integers, floats, or boolean values. |
**kwargs | This is where you can add additional keywords. |
For this example, we will determine the 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 theaxis
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 theresult
variable. - Line [3] outputs the result to the terminal.
Output
Bruins | 6.00 |
Oilers | 7.67 |
Leafs | 6.67 |
Flames | 12.00 |
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 hours.
- Line [2] uses the
mean()
method to calculate the mean (average). This output saves to theresult
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)
Parameter | Description |
---|---|
axis | If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row. |
skipna | If 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. |
level | Set the appropriate parameter if the DataFrame/Series is multi-level. If no value, then None is assumed. |
numeric_only | Only include columns that contain integers, floats, or boolean values. |
**kwargs | This is where you can add additional keywords. |
We will determine the median value(2) for our Hockey Teams for this example.
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 theresult
variable. - Line [3] outputs the result to the terminal.
Output
Bruins | 5.0 |
Oilers | 6.0 |
Leafs | 7.0 |
Flames | 8.0 |
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)
Parameter | Description |
---|---|
axis | If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row. |
numeric_only | Only include columns that contain integers, floats, or boolean values. |
dropna | If 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 columnaxis
. This output saves to theresult
variable. - Line [3] outputs the result to the terminal.
Output
Bruins | Oilers | Leafs | Flames | |
0 | 4 | 3 | 2 | 7 |
1 | 5 | 9 | 4 | 9 |
2 | 9 | 13 | 7 | 13 |
You can see where the numbers come from in this visualization:


Further Learning Resources
This is Part 4 of the DataFrame method series.
Also, have a look at the Pandas DataFrame methods cheat sheet!