Box plots in Pandas

Introduction

A box plot helps you see how data is spread out and if there are any unusual values.

To compare test scores of students in different classes.

To check the spread of daily temperatures over a month.

To find outliers in sales data for different products.

To summarize the distribution of heights in a group of people.

Syntax

Pandas

DataFrame.boxplot(column=None, by=None, grid=True, figsize=None, fontsize=None, rot=0, return_type=None, ax=None, **kwargs)

column selects which data column to plot.

by groups data by a column to make multiple box plots.

Examples

Draws a box plot for the 'Age' column.

Pandas

df.boxplot(column='Age')

Draws box plots of 'Salary' grouped by 'Department'.

Pandas

df.boxplot(column='Salary', by='Department')

Draws box plots for all numeric columns with a custom size and no grid.

Pandas

df.boxplot(figsize=(8,6), grid=False)

Sample Program

This code creates a small table with departments, salaries, and ages. It then draws box plots to compare salaries in each department. The plot shows the spread and any unusual salary values.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'Department': ['HR', 'HR', 'IT', 'IT', 'Sales', 'Sales'],
        'Salary': [50000, 52000, 60000, 61000, 45000, 47000],
        'Age': [25, 28, 30, 35, 22, 24]}
df = pd.DataFrame(data)

# Draw box plots of Salary grouped by Department
df.boxplot(column='Salary', by='Department', grid=False, figsize=(6,4))
plt.suptitle('')  # Remove automatic title
plt.title('Salary Distribution by Department')
plt.ylabel('Salary')
plt.show()

Important Notes

Box plots show median, quartiles, and possible outliers.

Use plt.show() to display the plot when using pandas with matplotlib.

Grouping by a column helps compare different categories side by side.

Summary

Box plots help visualize data spread and outliers.

Use DataFrame.boxplot() to create box plots in pandas.

Grouping data by a column shows comparisons between groups.

Practice

1. What does a box plot primarily show about a dataset?

easy

A. Only the average value of the data

B. The spread, median, and outliers of the data

C. The exact values of all data points

D. The correlation between two variables

Solution

1. Step 1: Understand box plot components

   A box plot shows the median line, the box representing the middle 50% of data, and whiskers for spread.

2. Step 2: Identify what it reveals

   It highlights the data spread, median, and any outliers beyond whiskers.

3. Final Answer:

   The spread, median, and outliers of the data -> Option B

4. Quick Check:

   Box plot = spread + median + outliers [OK]

Hint: Box plots show spread and outliers, not just averages [OK]

Common Mistakes:

• Thinking box plots show all data points exactly
• Confusing box plots with scatter plots
• Assuming box plots show correlation

2. Which of the following is the correct way to create a box plot for a pandas DataFrame named df?

easy

A. boxplot(df)

B. df.plot.boxplot()

C. df.boxplot()

D. df.box()

Solution

1. Step 1: Recall pandas box plot syntax

   The pandas DataFrame method to create a box plot is boxplot().

2. Step 2: Check each option

   df.boxplot() uses df.boxplot(), which is correct. df.plot.boxplot() is invalid syntax. boxplot(df) is not a pandas function. df.box() is invalid.

3. Final Answer:

   df.boxplot() -> Option C

4. Quick Check:

   Use df.boxplot() to plot box plots [OK]

Hint: Use df.boxplot() to create box plots in pandas [OK]

Common Mistakes:

• Using df.plot.boxplot() which is invalid
• Trying to call boxplot as a standalone function
• Using df.box() which does not exist

3. Given the following code, what will be the output type of ax?

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'A': [1, 2, 5, 7, 9],
    'B': [2, 3, 5, 8, 10]
})
ax = df.boxplot()

medium

A. A matplotlib AxesSubplot object

B. A list of numbers

C. A pandas DataFrame

D. A matplotlib Figure object

Solution

1. Step 1: Understand df.boxplot() return type

   The boxplot() method returns a matplotlib AxesSubplot object representing the plot area.

2. Step 2: Check options against expected type

   The return type is a matplotlib AxesSubplot object. This is not a pandas DataFrame, not a list of numbers, and not a matplotlib Figure object.

3. Final Answer:

   A matplotlib AxesSubplot object -> Option A

4. Quick Check:

   df.boxplot() returns AxesSubplot [OK]

Hint: df.boxplot() returns a matplotlib Axes object [OK]

Common Mistakes:

• Expecting a DataFrame or list as output
• Confusing Figure and Axes objects
• Assuming no return value

4. What is wrong with this code snippet that tries to create a box plot grouped by a column?

import pandas as pd
df = pd.DataFrame({
    'Category': ['X', 'X', 'Y', 'Y'],
    'Value': [10, 15, 10, 20]
})
df.boxplot(column='Value', by='Category', groupby='Category')

medium

A. The argument 'groupby' is invalid for boxplot()

B. The 'by' argument should be 'groupby'

C. The 'column' argument should be a list, not a string

D. The DataFrame is missing numeric columns

Solution

1. Step 1: Check boxplot() arguments

   The pandas boxplot method accepts 'column' and 'by' arguments, but not 'groupby'.

2. Step 2: Identify invalid argument

   The code incorrectly uses 'groupby' which causes an error. 'by' is correct for grouping.

3. Final Answer:

   The argument 'groupby' is invalid for boxplot() -> Option A

4. Quick Check:

   Use 'by' to group, no 'groupby' argument [OK]

Hint: Use 'by' to group in boxplot(), not 'groupby' [OK]

Common Mistakes:

• Using 'groupby' instead of 'by' argument
• Passing column as list when string is allowed
• Thinking numeric columns are missing

5. You have a DataFrame df with columns "Group" and "Score". You want to compare the score distributions for each group side by side using box plots. Which code correctly achieves this?

hard

A. df.boxplot(by='Score', column='Group')

B. df.groupby('Group').boxplot(column='Score')

C. df.plot.box(by='Group', column='Score')

D. df.boxplot(column='Score', by='Group')

Solution

1. Step 1: Understand grouping in boxplot()

   To compare groups side by side, use boxplot() with column as the numeric data and by as the grouping column.

2. Step 2: Evaluate each option

   df.boxplot(column='Score', by='Group') correctly uses column='Score' and by='Group'. df.groupby('Group').boxplot(column='Score') tries to group then boxplot, which returns multiple plots, not side by side. df.plot.box(by='Group', column='Score') uses invalid syntax. df.boxplot(by='Score', column='Group') swaps column and by incorrectly.

3. Final Answer:

   df.boxplot(column='Score', by='Group') -> Option D

4. Quick Check:

   Use column=score and by=group for grouped boxplots [OK]

Hint: Use df.boxplot(column='Score', by='Group') for grouped box plots [OK]

Common Mistakes:

• Using groupby().boxplot() which plots separately
• Swapping column and by arguments
• Using invalid plot syntax