Histogram plots in Pandas - Step-by-Step Execution

Concept Flow - Histogram plots

Start with data

↓

Select numeric column

↓

Divide data range into bins

↓

Count data points in each bin

↓

Draw bars with heights = counts

↓

Display histogram plot

↓

End

The flow shows how histogram plots are created by dividing data into bins, counting points in each bin, and drawing bars to represent counts.

Execution Sample

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = pd.Series([1,2,2,3,3,3,4,4,4,4])
data.plot.hist(bins=4)
plt.show()

This code creates a histogram plot of the data with 4 bins, showing how many values fall into each bin.

Execution Table

Step	Action	Data Range	Bins	Bin Counts	Plot Bars Height
1	Start with data	[1,4]
2	Decide number of bins		4
3	Calculate bin edges		Bins: [1.0,1.75,2.5,3.25,4.0]
4	Count data in bin 1 (1.0 to 1.75)			1	Bar height = 1
5	Count data in bin 2 (1.75 to 2.5)			2	Bar height = 2
6	Count data in bin 3 (2.5 to 3.25)			3	Bar height = 3
7	Count data in bin 4 (3.25 to 4.0]			4	Bar height = 4
8	Draw bars with heights				Bars drawn with heights 1,2,3,4
9	Show plot				Histogram displayed
10	End				Plot complete

💡 All bins counted and bars drawn, histogram plot is complete

Variable Tracker

Variable	Start	After Step 4	After Step 5	After Step 6	After Step 7	Final
data	[1,2,2,3,3,3,4,4,4,4]	[1,2,2,3,3,3,4,4,4,4]	[1,2,2,3,3,3,4,4,4,4]	[1,2,2,3,3,3,4,4,4,4]	[1,2,2,3,3,3,4,4,4,4]	[1,2,2,3,3,3,4,4,4,4]
bins	Not set	4 bins set	4 bins set	4 bins set	4 bins set	4 bins set
bin_edges	Not set	[1.0,1.75,2.5,3.25,4.0]	[1.0,1.75,2.5,3.25,4.0]	[1.0,1.75,2.5,3.25,4.0]	[1.0,1.75,2.5,3.25,4.0]	[1.0,1.75,2.5,3.25,4.0]
bin_counts	Empty	[1]	[1,2]	[1,2,3]	[1,2,3,4]	[1,2,3,4]
plot_bars_height	None	None	None	None	None	[1,2,3,4]

Key Moments - 3 Insights

Why do the bin edges include decimal values like 1.75 and 2.5 instead of just integers?

The bin edges divide the data range evenly into 4 parts, so they are calculated as decimal values to split the range [1,4] into equal intervals, as shown in execution_table rows 3 and 4.

Why does the last bin count include values equal to 4 but the first bin count only includes values equal to 1?

By default, bins include the left edge but exclude the right edge, except the last bin which includes both edges. This is why the last bin counts all 4s, as seen in execution_table row 7.

What does the height of each bar represent in the histogram?

Each bar's height shows how many data points fall into that bin's range, as counted in execution_table rows 4 to 7.

Visual Quiz - 3 Questions

Look at the execution_table at step 6, what is the count of data points in the third bin?

A 2

B 3

C 4

D 1

💡 Hint

Check the 'Bin Counts' column at step 6 in the execution_table.

At which step does the histogram plot get displayed?

A Step 9

B Step 8

C Step 7

D Step 10

💡 Hint

Look for the action 'Show plot' in the execution_table.

If we change bins from 4 to 2, how would the bin counts change?

A Counts would be [5,5]

B Counts would be [4,6]

C Counts would be [3,7]

D Counts would be [2,8]

💡 Hint

Think about dividing the data range [1,4] into 2 bins and counting data points in each.

Concept Snapshot

Histogram plots show how data values are distributed across ranges called bins.
Use pandas Series or DataFrame column with .plot.hist(bins=number).
Bins split data range evenly; bar heights show counts per bin.
Useful to see data shape and frequency visually.
Call plt.show() to display the plot.

Full Transcript

This visual execution traces how a histogram plot is created using pandas. Starting with numeric data, the code decides the number of bins, calculates bin edges dividing the data range evenly, counts how many data points fall into each bin, and draws bars with heights equal to these counts. The plot is then displayed. Key points include understanding bin edges as decimal intervals, how the last bin includes its right edge, and that bar heights represent counts of data points in each bin. The execution table shows each step clearly, and the variable tracker follows changes in data, bins, bin edges, counts, and bar heights. This helps beginners see exactly how histogram plots work step-by-step.

Practice

1. What does a histogram plot show when created from a pandas DataFrame column?

easy

A. The exact values of each data point

B. The frequency distribution of values grouped into bins

C. The relationship between two variables

D. The sum of all values in the column

Solution

1. Step 1: Understand histogram purpose

   A histogram groups data into bins and counts how many values fall into each bin.

2. Step 2: Identify what histogram shows

   It shows frequency distribution, not individual values or sums.

3. Final Answer:

   The frequency distribution of values grouped into bins -> Option B

4. Quick Check:

   Histogram = frequency distribution [OK]

Hint: Histograms show counts per group, not individual points [OK]

Common Mistakes:

• Thinking histogram shows exact data points
• Confusing histogram with scatter plot
• Assuming histogram shows sums instead of counts

2. Which of the following is the correct way to plot a histogram of the 'age' column in a pandas DataFrame named df?

easy

A. df['age'].hist()

B. df.plot.hist('age')

C. df['age'].histogram()

D. df.histogram('age')

Solution

1. Step 1: Recall pandas histogram syntax

   To plot a histogram of a single column, use df['column'].hist().

2. Step 2: Check each option

   df['age'].histogram() is incorrect syntax; df.plot.hist('age') is invalid method; df.histogram('age') does not exist; df['age'].hist() is correct.

3. Final Answer:

   df['age'].hist() -> Option A

4. Quick Check:

   Single column histogram = df['col'].hist() [OK]

Hint: Use df['col'].hist() for single column histogram [OK]

Common Mistakes:

• Using df['age'].histogram() which is invalid
• Trying df.plot.hist('col') which is incorrect
• Calling non-existent df.histogram() method

3. Given the code:

import pandas as pd
import matplotlib.pyplot as plt
data = {'scores': [55, 60, 65, 70, 75, 80, 85, 90, 95, 100]}
df = pd.DataFrame(data)
df['scores'].hist(bins=5)
plt.show()

How many bars will the histogram display?

medium

A. Cannot determine without data

B. 10

C. 5

D. 1

Solution

1. Step 1: Understand bins parameter

   The bins=5 argument divides the data range into 5 equal groups (bars).

2. Step 2: Count bars from bins

   Histogram will show 5 bars representing frequency counts in each bin.

3. Final Answer:

   5 -> Option C

4. Quick Check:

   bins=5 means 5 bars [OK]

Hint: Number of bars equals bins parameter [OK]

Common Mistakes:

• Confusing bins with number of data points
• Assuming one bar per data point
• Ignoring bins argument effect

4. What is wrong with this code snippet to plot a histogram of the 'height' column?

import pandas as pd
df = pd.DataFrame({'height': [150, 160, 170, 180, 190]})
df.hist('height', bins=3, xlabel='Height')

medium

A. The parameter 'xlabel' is not valid in df.hist()

B. The 'bins' parameter must be a list, not an integer

C. The column name must be passed as a list ['height']

D. The DataFrame is missing a required index

Solution

1. Step 1: Check df.hist() parameters

   df.hist() does not accept 'xlabel' parameter directly.

2. Step 2: Identify correct way to label axes

   Labels should be set using matplotlib functions after plotting, not as df.hist() arguments.

3. Final Answer:

   The parameter 'xlabel' is not valid in df.hist() -> Option A

4. Quick Check:

   Use plt.xlabel() to label axes, not df.hist() args [OK]

Hint: Use plt.xlabel() for labels, not df.hist() params [OK]

Common Mistakes:

• Passing axis labels as df.hist() arguments
• Thinking bins must be a list
• Assuming column name must be a list

5. You have a DataFrame df with a 'salary' column containing values from 2000 to 10000. You want to create a histogram with 4 bins and label the x-axis as 'Salary Range'. Which code correctly achieves this?

hard

A. ax = df['salary'].hist(bins=4)
ax.xlabel('Salary Range')

B. df.hist('salary', bins=4, xlabel='Salary Range')

C. df['salary'].plot.hist(bins=4, xlabel='Salary Range')

D. df['salary'].hist(bins=4)
plt.xlabel('Salary Range')

Solution

1. Step 1: Plot histogram with bins=4

   Use df['salary'].hist(bins=4) to create histogram with 4 bins.

2. Step 2: Label x-axis correctly

   Use matplotlib's plt.xlabel('Salary Range') after plotting to set label.

3. Final Answer:

   df['salary'].hist(bins=4) plt.xlabel('Salary Range') -> Option D

4. Quick Check:

   Use plt.xlabel() after hist() for axis label [OK]

Hint: Set labels with plt.xlabel() after plotting histogram [OK]

Common Mistakes:

• Passing xlabel directly to hist()
• Using plot.hist() with xlabel param
• Not labeling axes at all