Histogram plots in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of histogram bin counts

What is the output of the following code that plots a histogram and returns the counts of values in each bin?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = pd.Series([1, 2, 2, 3, 3, 3, 4, 4, 4, 4])
counts, bins, patches = plt.hist(data, bins=4, edgecolor='black')
plt.close()
print(counts)

A [1. 2. 3. 4.]

B [2. 2. 3. 3.]

C [1. 3. 3. 3.]

D [0. 3. 4. 3.]

💡 Hint

Count how many values fall into each bin range when bins=4.

The data has 1 value in the first bin (1), 2 values in the second bin (2,2), 3 values in the third bin (3,3,3), and 4 values in the last bin (4,4,4,4).

❓ data_output

intermediate

Number of bins in histogram

How many bins will the histogram have in this code snippet?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = pd.Series([5, 7, 8, 5, 6, 7, 8, 9, 10, 11])
plt.hist(data, bins=5)
plt.close()

A 10

B 4

C 5

D 6

💡 Hint

The bins parameter sets the number of bins directly.

The bins parameter is set to 5, so the histogram will have exactly 5 bins.

❓ visualization

advanced

Identify the histogram shape

Given this data and histogram code, what shape does the histogram plot show?

Pandas

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

data = pd.Series(np.concatenate([np.random.normal(0, 1, 500), np.random.normal(5, 1, 500)]))
plt.hist(data, bins=30, edgecolor='black')
plt.close()

A Skewed distribution with long tail

B Uniform distribution with flat shape

C Single peak normal distribution

D Bimodal distribution with two peaks

💡 Hint

The data is combined from two normal distributions centered at different points.

The data combines two normal distributions centered at 0 and 5, so the histogram shows two peaks, making it bimodal.

🔧 Debug

advanced

Error in histogram plotting code

What error does this code raise when trying to plot a histogram?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = pd.Series(['a', 'b', 'c', 'a', 'b'])
plt.hist(data)
plt.close()

A No error, histogram plots categorical data

B TypeError: float() argument must be a string or a number, not 'Series'

C TypeError: unorderable types: str() < str()

D ValueError: bins must be positive, when an integer

💡 Hint

Histograms require numeric data to bin values.

Matplotlib histogram expects numeric data to create bins. Strings cannot be converted to float for binning, causing a TypeError.

🚀 Application

expert

Choosing bin size for histogram analysis

You have a dataset of 10,000 numeric values with a wide range. You want to plot a histogram to analyze the distribution clearly. Which bin size choice is best to balance detail and readability?

A Use 50 bins to balance detail and smoothness

B Use 1000 bins to capture every small variation

C Use 10 bins to get a broad overview with less noise

D Use 1 bin to see only the total count

💡 Hint

Too few bins hide details; too many bins create noise.

50 bins provide enough detail to see distribution shape without overwhelming noise or empty bins, suitable for 10,000 data points.

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