Distribution plots (histplot, kdeplot) in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a histogram in data analysis?

A histogram is a chart that shows how often different values appear in a dataset. It groups data into bins and counts how many values fall into each bin.

beginner

What does a KDE plot represent?

A KDE (Kernel Density Estimate) plot shows a smooth curve that estimates the probability distribution of a dataset. It helps to see the shape of the data without using bins.

intermediate

How does histplot differ from kdeplot in seaborn?

Histplot shows bars representing counts in bins, while kdeplot shows a smooth curve estimating data density. Histplot is good for seeing exact counts; kdeplot is good for understanding data shape.

intermediate

Why might you use both histplot and kdeplot together?

Using both together helps you see the exact counts (histogram) and the smooth distribution shape (KDE). This gives a fuller picture of the data's distribution.

beginner

What parameter in seaborn's histplot controls the number of bins?

The 'bins' parameter controls how many bins the data is divided into. More bins show more detail; fewer bins show a simpler view.

What does a histogram display?

A A scatter plot of two variables

B A smooth curve of data density

C Counts of data points in intervals

D A line chart of time series

A histogram groups data into bins and shows how many data points fall into each bin.

Which plot type shows a smooth estimate of data distribution?

A KDE plot

B Histogram

C Bar chart

D Pie chart

KDE plot shows a smooth curve estimating the probability density of the data.

In seaborn, which function would you use to create a histogram?

A sns.kdeplot()

B sns.histplot()

C sns.scatterplot()

D sns.lineplot()

sns.histplot() creates histograms showing counts of data in bins.

What does increasing the number of bins in a histogram do?

A Shows more detailed distribution

B Makes the histogram smoother

C Removes outliers

D Changes data values

More bins divide data into smaller intervals, showing more detail.

Why combine histplot and kdeplot in one graph?

A To compare two datasets

B To remove noise from data

C To create a 3D plot

D To see counts and smooth distribution shape

Combining both shows exact counts and the smooth shape of data distribution.

Explain the difference between a histogram and a KDE plot in simple terms.

Think about bars versus smooth curves.

Describe a situation where using both histplot and kdeplot together would help understand data better.

Imagine you want to know both how many and how data is spread.

Practice

1. What does the histplot function primarily display in a distribution plot?

easy

A. Bars showing the frequency of data values in bins

B. A smooth curve estimating the data density

C. Scatter points representing individual data values

D. A line chart showing data over time

Solution

1. Step 1: Understand the purpose of histplot

   The histplot function creates a histogram, which groups data into bins and shows how many values fall into each bin using bars.

2. Step 2: Differentiate from kdeplot

   Unlike kdeplot which draws a smooth curve, histplot uses bars to represent frequency counts.

3. Final Answer:

   Bars showing the frequency of data values in bins -> Option A

4. Quick Check:

   histplot = bars frequency [OK]

Hint: Histplot uses bars for counts, kdeplot uses smooth curves [OK]

Common Mistakes:

• Confusing histplot with kdeplot
• Thinking histplot shows individual points
• Assuming histplot draws lines instead of bars

2. Which of the following is the correct way to import seaborn and plot a histogram of a list data using histplot?

easy

A. import seaborn as sb
sb.kdeplot(data)

B. import seaborn
seaborn.plot_hist(data)

C. from seaborn import histplot
kdeplot(data)

D. import seaborn as sns
sns.histplot(data)

Solution

1. Step 1: Check the standard import for seaborn

   The common alias for seaborn is sns, so import seaborn as sns is correct.

2. Step 2: Verify the function call for histogram

   The function to plot a histogram is histplot, so sns.histplot(data) is the correct syntax.

3. Final Answer:

   import seaborn as sns sns.histplot(data) -> Option D

4. Quick Check:

   Standard import + histplot call = import seaborn as sns sns.histplot(data) [OK]

Hint: Use 'import seaborn as sns' and call sns.histplot(data) [OK]

Common Mistakes:

• Using wrong alias like sb
• Calling non-existent seaborn.plot_hist
• Importing histplot directly from seaborn

3. What will the following code output?

import seaborn as sns
import matplotlib.pyplot as plt
data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
sns.kdeplot(data)
plt.show()

medium

A. A smooth curve estimating the data density

B. A bar chart showing counts of each number

C. A scatter plot of the data points

D. An error because kdeplot needs a DataFrame

Solution

1. Step 1: Understand what sns.kdeplot does

   The kdeplot function draws a smooth curve estimating the probability density of the data.

2. Step 2: Check the input data type

   The input is a list of numbers, which is valid for kdeplot, so no error occurs.

3. Final Answer:

   A smooth curve estimating the data density -> Option A

4. Quick Check:

   kdeplot = smooth density curve [OK]

Hint: kdeplot draws smooth curves, not bars or points [OK]

Common Mistakes:

• Expecting bars instead of curve
• Thinking kdeplot requires DataFrame input
• Confusing kdeplot with scatter plot

4. The following code does not display a plot. What is the most likely cause?

import seaborn as sns
data = [1, 2, 3, 4, 5]
sns.histplot(data, kde=True)

medium

A. Data must be a DataFrame, not a list

B. The parameter 'kde' is not valid for histplot

C. Missing import of matplotlib.pyplot to show the plot

D. histplot cannot plot numeric data

Solution

1. Step 1: Check if plot display is handled

   The code calls sns.histplot but does not call plt.show() to display the plot.

2. Step 2: Identify missing import and display call

   Without importing matplotlib.pyplot and calling plt.show(), the plot won't appear, which may be mistaken as an error.

3. Final Answer:

   Missing import of matplotlib.pyplot to show the plot -> Option C

4. Quick Check:

   Need plt.show() to display plot [OK]

Hint: Always import matplotlib.pyplot and call plt.show() to display plots [OK]

Common Mistakes:

• Assuming 'kde' param is invalid for histplot
• Thinking list data causes error
• Believing histplot can't plot numeric data

5. You have two groups of data: group1 = [1,2,2,3,4] and group2 = [3,4,4,5,6]. You want to compare their distributions on the same plot using seaborn. Which code correctly overlays their KDE plots with different colors?

hard

A. import seaborn as sns
import matplotlib.pyplot as plt
data = group1 + group2
labels = ['group1', 'group2']
sns.kdeplot(data, hue=labels)
plt.show()

B. import seaborn as sns
import matplotlib.pyplot as plt
sns.kdeplot(group1, color='blue')
sns.kdeplot(group2, color='red')
plt.show()

C. import seaborn as sns
sns.histplot(group1, color='blue')
sns.histplot(group2, color='red')

D. import seaborn as sns
sns.kdeplot(group1)
sns.kdeplot(group2)
plt.show()

Solution

1. Step 1: Understand how to overlay KDE plots

   Calling sns.kdeplot twice with different data and colors overlays the KDE curves on the same plot.

2. Step 2: Check for plot display

   Importing matplotlib.pyplot and calling plt.show() ensures the plot appears.

3. Step 3: Evaluate other options

   import seaborn as sns
   import matplotlib.pyplot as plt
   data = group1 + group2
   labels = ['group1']*len(group1) + ['group2']*len(group2)
   sns.kdeplot(data, hue=labels)
   plt.show()

   uses hue incorrectly on a list without DataFrame;

   import seaborn as sns
   sns.histplot(group1, color='blue')
   sns.histplot(group2, color='red')

   uses histplot, not KDE;

   import seaborn as sns
   sns.kdeplot(group1)
   sns.kdeplot(group2)
   plt.show()

   misses import matplotlib.pyplot as plt.

4. Final Answer:

   import seaborn as sns import matplotlib.pyplot as plt sns.kdeplot(group1, color='blue') sns.kdeplot(group2, color='red') plt.show() -> Option B

5. Quick Check:

   Overlay KDE with sns.kdeplot twice + plt.show() [OK]

Hint: Call sns.kdeplot twice with colors, then plt.show() [OK]

Common Mistakes:

• Using hue on plain lists without DataFrame
• Forgetting plt.show() to display plot
• Mixing histplot with kdeplot for smooth curves