Histogram computation with np.histogram() in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the function np.histogram() do?

np.histogram() calculates how many data points fall into each bin of a histogram. It helps us understand the distribution of data by grouping values into ranges.

beginner

What are the two main outputs of np.histogram()?

The function returns two arrays: counts (number of data points in each bin) and bin edges (the boundaries of each bin).

beginner

How do you control the number of bins in np.histogram()?

You use the bins parameter to set how many bins you want. For example, bins=5 divides the data into 5 bins (not necessarily equal-width if data is uneven).

beginner

What happens if you don't specify the bins parameter in np.histogram()?

By default, np.histogram() chooses 10 bins to split your data into equal-width intervals.

beginner

Why is histogram useful in data science?

Histograms help us see the shape of data, spot patterns, and find out if data is spread out or clustered. This helps in making decisions or choosing the right analysis method.

What does the np.histogram() function return?

A Counts of data points per bin and bin edges

B Mean and median of the data

C Sorted data array

D Standard deviation and variance

np.histogram() returns two arrays: counts per bin and the edges of those bins.

If you want to split data into 4 groups, which parameter do you set in np.histogram()?

A bins=4

B range=4

C count=4

D groups=4

The bins parameter controls how many bins (groups) the data is split into.

What is the default number of bins if you do not specify bins in np.histogram()?

A 5

B 8

C 12

D 10

The default number of bins is 10.

Which of these best describes a histogram?

A A chart showing data points over time

B A graph showing frequency of data in intervals

C A table of raw data values

D A list of sorted numbers

A histogram shows how often data falls into certain ranges or bins.

What type of data is best analyzed with a histogram?

A Categorical data

B Boolean data

C Continuous numerical data

D Text data

Histograms are best for continuous numerical data to see distribution.

Explain how np.histogram() helps us understand data distribution.

Think about how grouping data into ranges reveals patterns.

Describe how you would use np.histogram() to create a histogram with 7 bins.

Focus on the parameter that controls the number of bins.

Practice

1. What does the np.histogram() function in NumPy do?

easy

A. It creates a line plot of the data points.

B. It sorts the data points in ascending order.

C. It counts how many data points fall into specified bins and returns counts and bin edges.

D. It calculates the mean and median of the data points.

Solution

1. Step 1: Understand the purpose of np.histogram()

   The function divides data into bins and counts how many points fall into each bin.

2. Step 2: Identify the output of np.histogram()

   It returns two arrays: one with counts per bin and one with the bin edges.

3. Final Answer:

   It counts how many data points fall into specified bins and returns counts and bin edges. -> Option C

4. Quick Check:

   np.histogram() = counts and edges [OK]

Hint: np.histogram() returns counts and bin edges separately [OK]

Common Mistakes:

• Thinking np.histogram() sorts data
• Confusing histogram with mean calculation
• Expecting a plot from np.histogram()

2. Which of the following is the correct way to call np.histogram() to create 4 bins for data array data?

easy

A. np.histogram(data, bins=4)

B. np.histogram(4, data)

C. np.histogram(bins=data, 4)

D. np.histogram(data, bins=[4])

Solution

1. Step 1: Check the correct parameter usage for bins

   The bins parameter can be passed as a keyword argument like bins=4.

2. Step 2: Identify the correct syntax

   np.histogram(data, bins=4) is the standard and clear way to specify 4 bins.

3. Final Answer:

   np.histogram(data, bins=4) -> Option A

4. Quick Check:

   Use bins=4 to specify number of bins [OK]

Hint: Use bins=number to set bin count in np.histogram() [OK]

Common Mistakes:

• Passing bins as a list with one number
• Swapping argument order incorrectly
• Using positional argument without data first

3. What is the output of this code?

import numpy as np
data = np.array([1, 2, 2, 3, 4, 5])
counts, bins = np.histogram(data, bins=[1, 3, 5])
print(counts)

medium

A. [2 4]

B. [3 3]

C. [1 5]

D. [3 2]

Solution

1. Step 1: Understand bins and data distribution

   Bins are [1, 3) and [3, 5). Count how many data points fall into each bin.

2. Step 2: Count data points in each bin

   Bin [1,3) includes 1, 2, 2 -> 3 points; Bin [3,5) includes 3, 4 -> 2 points. Note that 5 equals the rightmost bin edge and is included in the last bin.

3. Final Answer:

   [3 3] -> Option B

4. Quick Check:

   Counts per bin = [3, 3] [OK]

Hint: Count points in each bin range carefully [OK]

Common Mistakes:

• Including right edge in left bin
• Miscounting repeated values
• Confusing bin edges with counts

4. The following code throws an error. What is the problem?

import numpy as np
data = [1, 2, 3, 4]
counts, bins = np.histogram(data, bins='five')

medium

A. The counts and bins cannot be unpacked together.

B. The data must be a NumPy array, not a list.

C. np.histogram() requires at least 5 data points.

D. The bins parameter must be an integer or sequence, not a string.

Solution

1. Step 1: Check the bins parameter type

   bins='five' is a string, but bins must be an int or a sequence of numbers.

2. Step 2: Understand np.histogram() input requirements

   Data can be list or array, and unpacking counts and bins is valid.

3. Final Answer:

   The bins parameter must be an integer or sequence, not a string. -> Option D

4. Quick Check:

   bins must be int or sequence, not string [OK]

Hint: Bins must be int or list/array, not text [OK]

Common Mistakes:

• Thinking data must be np.array
• Assuming unpacking counts and bins is invalid
• Believing minimum data length is required

5. You have data points [0, 1, 2, 3, 4, 5, 6]. You want to create a histogram with bins of width 2 starting from 0 up to 6 (inclusive). Which bins parameter should you pass to np.histogram() to get correct bin counts?

hard

A. [0, 2, 4, 6, 8]

B. [0, 2, 4, 6]

C. 3

D. [0, 1, 2, 3, 4, 5, 6]

Solution

1. Step 1: Determine bin edges for width 2 from 0 to 6 inclusive

   Bins should cover ranges: 0-2, 2-4, 4-6, and include 6, so edges are [0, 2, 4, 6, 8].

2. Step 2: Understand np.histogram bin edge behavior

   Rightmost edge is inclusive for the last bin, so to include 6, bin edges must go beyond 6.

3. Final Answer:

   [0, 2, 4, 6, 8] -> Option A

4. Quick Check:

   Bin edges must cover all data points [OK]

Hint: Add one extra bin edge beyond max data to include last point [OK]

Common Mistakes:

• Using bins that exclude the max value
• Using number of bins instead of edges
• Using bin edges with wrong intervals