20 Essential NumPy Challenges with Complete Solutions

Hello everyone, it's time for the NumPy advanced practice series.

01 Data Lookup

Question: How to obtain the common elements between two arrays?

Input:

import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
arr1 = np.random.randint(10,6,6)
arr2 = np.random.randint(10,6,6)

Answer:

arr1 = np.random.randint(10,6,6)
arr2 = np.random.randint(10,6,6)
print("arr1: %s" % arr1)
print("arr2: %s" % arr2)
np.intersect1d(arr1, arr2)

02 Data Modification

Question: How to delete elements of one array that exist in another array?

Input:

arr1 = np.random.randint(10,6,6)
arr2 = np.random.randint(10,6,6)

Answer:

arr1 = np.random.randint(1,10,10)
arr2 = np.random.randint(1,10,10)
print("arr1: %s" % arr1)
print("arr2: %s" % arr2)
np.setdiff1d(arr1, arr2)

03 Data Modification

Question: How to set an array to read‑only mode?

Input: arr1 = np.random.randint(1,10,10) Answer:

arr1 = np.random.randint(1,10,10)
arr1.flags.writeable = False

04 Data Conversion

Question: How to convert a Python list to a NumPy array?

Input: a = [1,2,3,4,5] Answer:

a = [1,2,3,4,5]
np.array(a)

05 Data Conversion

Question: How to convert a pandas DataFrame to a NumPy array?

Input:

df = pd.DataFrame({'A':[1,2,3],'B':[4,5,6],'C':[7,8,9]})

Answer:

df.values

06 Data Analysis

Question: How to perform descriptive statistics on a NumPy array?

Input:

arr1 = np.random.randint(1,10,10)
arr2 = np.random.randint(1,10,10)

Answer:

print("Mean: %s" % np.mean(arr1))
print("Median: %s" % np.median(arr1))
print("Variance: %s" % np.var(arr1))
print("Std dev: %s" % np.std(arr1))
print("Covariance matrix: %s" % np.cov(arr1, arr2))
print("Correlation matrix: %s" % np.corrcoef(arr1, arr2))

07 Data Sampling

Question: How to perform probability sampling with NumPy?

Input:

arr = np.array([1,2,3,4,5])
np.random.choice(arr, 10, p=[0.1,0.1,0.1,0.1,0.6])

08 Data Creation

Question: How to create a copy of a NumPy array?

Input: arr = np.array([1,2,3,4,5]) Answer:

# Modifying the copy does not affect the original
arr = np.array([1,2,3,4,5])
arr1 = arr.copy()

09 Data Slicing

Question: How to slice an array from index 2 to 8 with a step of 2?

Input: arr = np.arange(10) Answer:

arr = np.arange(10)
a = slice(2,8,2)
arr[a]  # equivalent to arr[2:8:2]

10 String Operations

Question: How to concatenate and title‑case strings with NumPy?

Input:

str1 = ['I love']
str2 = [' Python']

Answer:

# Concatenate strings
str1 = ['I love']
str2 = [' Python']
print(np.char.add(str1, str2))
# Capitalize first letters
str3 = np.char.add(str1, str2)
print(np.char.title(str3))

11 Data Modification

Question: How to round numbers up or down in a NumPy array?

Input: arr = np.random.uniform(0,10,10) Answer:

arr = np.random.uniform(0,10,10)
print(arr)
# ceil
print(np.ceil(arr))
# floor
print(np.floor(arr))

12 Format Modification

Question: How to suppress scientific notation when printing NumPy arrays?

Answer:

np.set_printoptions(suppress=True)

13 Data Modification

Question: How to reverse rows or columns of a 2‑D NumPy array?

Input: arr = np.random.randint(1,10,[3,3]) Answer:

arr = np.random.randint(1,10,[3,3])
print(arr)
print('Column reverse')
print(arr[:, -1::-1])
print('Row reverse')
print(arr[-1::-1, :])

14 Data Lookup

Question: How to find elements in NumPy by position using np.take?

Input:

arr1 = np.random.randint(1,10,5)
arr2 = np.random.randint(1,20,10)

Answer:

arr1 = np.random.randint(1,10,5)
arr2 = np.random.randint(1,20,10)
print(arr1)
print(arr2)
print(np.take(arr2, arr1))

15 Data Calculation

Question: How to compute the remainder of two numbers with NumPy?

Input:

a = 10
b = 3

Answer:

np.mod(a, b)

16 Matrix SVD

Question: How to perform singular value decomposition on a matrix?

Input: A = np.random.randint(1,10,[3,3]) Answer:

np.linalg.svd(A)

17 Data Filtering

Question: How to filter NumPy data with multiple conditions?

Input: arr = np.random.randint(1,20,10) Answer:

arr = np.random.randint(1,20,10)
print(arr[(arr>1)&(arr<7)&(arr%2==0)])

18 Data Classification

Question: How to label elements greater than or equal to 7 or less than 3 as 1, others as 0?

Input: arr = np.random.randint(1,20,10) Answer:

arr = np.random.randint(1,20,10)
print(arr)
print(np.piecewise(arr, [arr<3, arr>=7], [-1, 1]))

19 Matrix Compression

Question: How to remove single‑dimensional entries from an array shape (squeeze)?

Input: arr = np.random.randint(1,10,[3,1]) Answer:

arr = np.random.randint(1,10,[3,1])
print(arr)
print(np.squeeze(arr))

20 Solving Linear Equations

Question: How to solve the linear system Ax = b with NumPy?

Input:

A = np.array([[1,2,3],[2,-1,1],[3,0,-1]])
b = np.array([9,8,3])

Answer:

A = np.array([[1,2,3],[2,-1,1],[3,0,-1]])
b = np.array([9,8,3])
x = np.linalg.solve(A, b)
print(x)

These twenty classic NumPy questions cover a broad range of array operations and provide a solid foundation for further data‑science work.