20 Essential NumPy Challenges to Master Matrix Operations

Welcome to the sixth installment of the NumPy advanced practice series, offering a collection of twenty focused problems that emphasize matrix operations and data handling techniques.

Problem 41 – Data Creation

Task: Generate a 6×6 array with random integers from 1 to 100.

data = np.random.randint(1,100, [6,6])

Problem 42 – Data Search

Task: Find the maximum value of each column.

np.amax(data, axis=0)

Problem 43 – Data Search

Task: Find the minimum value of each row.

np.amin(data, axis=1)

Problem 44 – Data Calculation

Task: Count the occurrences of each element in the array.

np.unique(data, return_counts=True)

Problem 45 – Data Calculation

Task: Obtain the ranking of elements in each row.

data.argsort()

Problem 46 – Data Processing

Task: Repeat each row once.

np.repeat(data, 2, axis=0)

Problem 47 – Data Processing

Task: Remove duplicate rows.

np.unique(data, axis=0)

Problem 48 – Data Sampling

Task: Sample three elements without replacement from the first row.

np.random.choice(data[0:1][0], 3, replace=False)

Problem 49 – Data Calculation

Task: Identify elements in the second row that are not present in the third row.

a = data[1:2]
b = data[2:3]
index = np.isin(a, b)
array = a[~index]
array

Problem 50 – Data Calculation

Task: Check whether the array contains any empty rows.

(~data.any(axis=1)).any()

Problem 51 – Data Sorting

Task: Sort each row in ascending order.

data.sort(axis=1)

Problem 52 – Data Conversion

Task: Convert the array to float type.

data1 = data.astype(float)

Problem 53 – Data Modification

Task: Replace elements smaller than 5 with NaN.

data1[data1 < 5] = np.nan

Problem 54 – Data Processing

Task: Delete rows that contain NaN values.

data1 = data1[~np.isnan(data1).any(axis=1), :]

Problem 55 – Data Calculation

Task: Find the most frequent value in the first row.

vals, counts = np.unique(data1[0,:], return_counts=True)
print(vals[np.argmax(counts)])

Problem 56 – Data Calculation

Task: Locate the element closest to 100.

a = 100
data1.flat[np.abs(data1 - a).argmin()]

Problem 57 – Data Calculation

Task: Subtract the mean of each row from its elements.

data1 - data1.mean(axis=1, keepdims=True)

Problem 58 – Data Calculation

Task: Normalize the array to the range [0, 1].

a = np.max(data1) - np.min(data1)
(data1 - np.min(data1)) / a

Problem 59 – Data Calculation

Task: Standardize the array (zero mean, unit variance).

mu = np.mean(data1, axis=0)
sigma = np.std(data1, axis=0)
(data1 - mu) / sigma

Problem 60 – Data Storage

Task: Save the array to a local text file.

np.savetxt('test.txt', data1)