Distribution plots (histplot, kdeplot) in Data Analysis Python - Step-by-Step Execution

Concept Flow - Distribution plots (histplot, kdeplot)

Start with data array

↓

Choose plot type: histplot or kdeplot

↓

Calculate frequencies or density

↓

Draw bars (histogram) or smooth curve (KDE)

↓

Show distribution shape visually

We start with data, pick histogram or KDE, calculate frequencies or density, then draw bars or smooth curve to show data distribution.

Execution Sample

Data Analysis Python

import seaborn as sns
import matplotlib.pyplot as plt

data = [1,2,2,3,3,3,4,4,5]
sns.histplot(data)
plt.show()

This code draws a histogram showing how often each number appears in the data.

Execution Table

Step	Action	Data Processed	Result/Output
1	Start with data array	[1,2,2,3,3,3,4,4,5]	Data ready for plotting
2	Choose histplot	Data array	Calculate frequency counts per bin
3	Calculate frequencies	Bins: 1,2,3,4,5	Counts: 1,2,3,2,1
4	Draw bars	Frequencies	Bars with heights matching counts
5	Show plot	Bars drawn	Histogram displayed on screen
6	Choose kdeplot	Data array	Estimate smooth density curve
7	Calculate KDE	Data points	Smooth curve values computed
8	Draw KDE curve	Density values	Smooth curve displayed
9	Show plot	KDE curve drawn	KDE plot displayed on screen
10	End	Plots shown	Execution complete

💡 All steps complete, plots displayed, execution ends.

Variable Tracker

Variable	Start	After histplot freq calc	After kdeplot density calc	Final
data	[1,2,2,3,3,3,4,4,5]	[1,2,2,3,3,3,4,4,5]	[1,2,2,3,3,3,4,4,5]	[1,2,2,3,3,3,4,4,5]
hist_freq	N/A	[1,2,3,2,1]	[1,2,3,2,1]	[1,2,3,2,1]
kde_density	N/A	N/A	Smooth curve values (array)	Smooth curve values (array)

Key Moments - 3 Insights

Why does histplot show bars but kdeplot shows a smooth curve?

Histplot counts data in bins and draws bars for each count (see execution_table step 4). KDE estimates a smooth density curve over data points (step 7).

What does the frequency count in histplot represent?

It shows how many data points fall into each bin range (execution_table step 3). For example, the count 3 means three data points are in that bin.

Can KDE values be negative or zero?

No, KDE values represent density estimates and are always positive or zero (execution_table step 7). They form a smooth curve showing data distribution.

Visual Quiz - 3 Questions

Look at the execution_table, what is the frequency count for the bin with value 3 in histplot?

A 1

B 3

C 2

D 4

💡 Hint

Check execution_table row 3 under Result/Output for counts per bin.

At which step does the code draw the smooth KDE curve?

A Step 4

B Step 7

C Step 8

D Step 9

💡 Hint

Look at execution_table rows describing KDE plot drawing actions.

If the data had more repeated values, how would histplot bars change?

A Bars would get taller for those values

B Bars would get shorter

C Bars would disappear

D Bars would become smooth curves

💡 Hint

Refer to variable_tracker hist_freq changes and execution_table frequency calculation.

Concept Snapshot

Distribution plots show how data values spread.
Histplot draws bars for counts in bins.
Kdeplot draws smooth curves estimating density.
Use histplot for counts, kdeplot for smooth shape.
Both help understand data distribution visually.

Full Transcript

We start with a list of numbers. We pick either histplot or kdeplot to see how data spreads. Histplot counts how many numbers fall into each group and draws bars. Kdeplot estimates a smooth curve showing data density. The code calculates counts or density, then draws the plot. Histplot bars show exact counts, kdeplot curves show smooth estimates. This helps us see patterns in data easily.

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