NumPy and pandas Basics Practice

Practise NumPy arrays, creating and inspecting DataFrames, selecting, filtering, sorting and adding columns.

Beginner7 problemsUpdated October 24, 2026

Review first: NumPy Arrays, Aggregations and Random Numbers, DataFrames, Selecting Data with loc and iloc, Filtering Rows, Sorting and Ranking

1.Exam score summary
Beginner
Given the scores [67, 82, 91, 58, 74, 88, 95, 61], use NumPy to print the mean (1 decimal), the median, the highest score and its position, and how many scores are 75 or more.
Show hint
np.array, .mean(), np.median, .max(), .argmax(), and (arr >= 75).sum().
Show solution
Python
import numpy as np scores = np.array([67, 82, 91, 58, 74, 88, 95, 61]) print(round(scores.mean(), 1), np.median(scores)) print(scores.max(), scores.argmax()) print((scores >= 75).sum())
Output
77.0 78.0 95 6 4
2.Prices with tax
Beginner
A 2 × 3 array holds prices of three products in two shops: [[100, 250, 80], [110, 240, 85]]. Add 18% GST to every price (rounded to 2 decimals) and print the cheapest shop for each product as 0 or 1.
Show hint
Multiply the whole array by 1.18. argmin(axis=0) gives the row with the smallest value in each column.
Show solution
Python
import numpy as np prices = np.array([[100, 250, 80], [110, 240, 85]]) with_gst = np.round(prices * 1.18, 2) print(with_gst) print(with_gst.argmin(axis=0))
Output
[[118. 295. 94.4] [129.8 283.2 100.3]] [0 1 0]
3.Build and inspect a DataFrame
Beginner
Create a DataFrame of five employees with columns name (Asha, Ravi, Meera, John, Priya), dept (Sales, IT, IT, HR, Sales) and salary (52000, 78000, 81000, 46000, 61000). Print its shape, the average salary, and the name of the highest earner.
Show hint
df.shape, df["salary"].mean(), and df.loc[df["salary"].idxmax(), "name"].
Show solution
Python
import pandas as pd df = pd.DataFrame({ "name": ["Asha", "Ravi", "Meera", "John", "Priya"], "dept": ["Sales", "IT", "IT", "HR", "Sales"], "salary": [52000, 78000, 81000, 46000, 61000], }) print(df.shape) print(df["salary"].mean()) print(df.loc[df["salary"].idxmax(), "name"])
Output
(5, 3) 63600.0 Meera

4.Filter and sort orders

Beginner

Orders have city = ["Mumbai", "Delhi", "Pune", "Delhi", "Mumbai", "Chennai"] and amount = [250, 700, 90, 320, 410, 150]. Print the orders over 200 from Mumbai or Delhi, sorted by amount from highest to lowest.

Show hint

isin(["Mumbai", "Delhi"]) combined with amount > 200 using &, then sort_values("amount", ascending=False).

Show solution

Python

import pandas as pd

orders = pd.DataFrame({
    "city": ["Mumbai", "Delhi", "Pune", "Delhi", "Mumbai", "Chennai"],
    "amount": [250, 700, 90, 320, 410, 150],
})
result = orders[orders["city"].isin(["Mumbai", "Delhi"]) & (orders["amount"] > 200)]
print(result.sort_values("amount", ascending=False))

Output

     city  amount
1   Delhi     700
4  Mumbai     410
3   Delhi     320
0  Mumbai     250

5.loc and iloc
Beginner
With names as the index of scores = pd.DataFrame({"maths": [92, 67, 78], "science": [88, 74, 81]}, index=["Asha", "Ravi", "Meera"]), print Ravi's science score with loc, the first row with iloc, and the maths column for the last two students using iloc.
Show hint
loc["Ravi", "science"], iloc[0], and iloc[1:, 0].
Show solution
Python
import pandas as pd scores = pd.DataFrame({"maths": [92, 67, 78], "science": [88, 74, 81]}, index=["Asha", "Ravi", "Meera"]) print(scores.loc["Ravi", "science"]) print(scores.iloc[0]) print(scores.iloc[1:, 0])
Output
74 maths 92 science 88 Name: Asha, dtype: int64 Ravi 67 Meera 78 Name: maths, dtype: int64

6.Totals and categories

Beginner

Items have price = [20, 60, 900, 350] and qty = [10, 5, 1, 2]. Add a total column and a size column that is "big" for totals of 500 or more and "small" otherwise, then print the table.

Show hint

df["total"] = df["price"] * df["qty"] and np.where(df["total"] >= 500, "big", "small").

Show solution

Python

import numpy as np
import pandas as pd

df = pd.DataFrame({"price": [20, 60, 900, 350], "qty": [10, 5, 1, 2]})
df["total"] = df["price"] * df["qty"]
df["size"] = np.where(df["total"] >= 500, "big", "small")
print(df)

Output

   price  qty  total   size
0     20   10    200  small
1     60    5    300  small
2    900    1    900    big
3    350    2    700    big

7.Counting categories

Beginner

Survey answers for preferred payment are ["upi", "card", "upi", "cash", "upi", "card", "upi"]. Print the count of each answer and each answer's share as a percentage (1 decimal).

Show hint

value_counts() and value_counts(normalize=True) * 100.

Show solution

Python

import pandas as pd

answers = pd.Series(["upi", "card", "upi", "cash", "upi", "card", "upi"])
print(answers.value_counts())
print((answers.value_counts(normalize=True) * 100).round(1))

Output

upi     4
card    2
cash    1
Name: count, dtype: int64
upi     57.1
card    28.6
cash    14.3
Name: proportion, dtype: float64

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