Histogram plots in Pandas - Mini Project: Build & Apply

Histogram plots

📖 Scenario: You work in a small bakery that tracks daily sales of different types of bread. You want to understand how many breads are sold each day to plan better for the next week.

🎯 Goal: Create a histogram plot to visualize the distribution of daily bread sales using pandas.

📋 What You'll Learn

Create a pandas DataFrame with daily sales data

Set a variable for the number of bins in the histogram

Use pandas to plot a histogram of the sales data

Display the histogram plot

💡 Why This Matters

🌍 Real World

Histograms help businesses understand how data like sales or customer visits are spread out over time.

💼 Career

Data scientists and analysts use histograms to visualize data distributions and find patterns or outliers.

1

Create the sales data

Create a pandas DataFrame called sales_data with one column named daily_sales containing these exact values: 12, 15, 7, 10, 20, 18, 5, 8, 15, 10.

Pandas

import pandas as pd
# Create the DataFrame sales_data with daily_sales column
# Your code here

Hint

Use pd.DataFrame and pass a dictionary with key "daily_sales" and the list of values.

2

Set the number of bins

Create a variable called num_bins and set it to 5 to decide how many bars the histogram will have.

Pandas

import pandas as pd
sales_data = pd.DataFrame({"daily_sales": [12, 15, 7, 10, 20, 18, 5, 8, 15, 10]})
# Set the number of bins for the histogram
# Your code here

Hint

Just create a variable num_bins and assign it the value 5.

3

Plot the histogram

Use the plot method on sales_data["daily_sales"] with kind="hist" and bins=num_bins to create the histogram plot.

Pandas

import pandas as pd
sales_data = pd.DataFrame({"daily_sales": [12, 15, 7, 10, 20, 18, 5, 8, 15, 10]})
num_bins = 5
# Plot the histogram of daily_sales using num_bins
# Your code here

Hint

Use sales_data["daily_sales"].plot(kind="hist", bins=num_bins) to create the histogram.

4

Show the histogram plot

Import matplotlib.pyplot as plt and call plt.show() to display the histogram plot.

Pandas

import pandas as pd
sales_data = pd.DataFrame({"daily_sales": [12, 15, 7, 10, 20, 18, 5, 8, 15, 10]})
num_bins = 5
sales_data["daily_sales"].plot(kind="hist", bins=num_bins)
# Import matplotlib.pyplot as plt and show the plot
# Your code here

Hint

Use import matplotlib.pyplot as plt and then plt.show() to display the plot window.

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