Introduction to Data Analysis using Python

Question 1

What is data analysis in Python?

Answer 1

The process of inspecting, cleaning, and modeling data to extract insights.

Python offers built-in functions and libraries like pandas and numpy.

Question 2

Name two built-in Python functions useful for data analysis.

Answer 2

• sum()
• len()
• count()

Other useful functions: min(), max(), sorted().

Question 3

True or False:

Base Python has built-in support for data frames.

Answer 3

False.

Data frames require external libraries like pandas.

Question 4

Fill in the blank:

The function ____ () returns a sequence of numbers.

Answer 4

range()

Example: range(5) gives [0,1,2,3,4].

Question 5

Which module in Python provides mathematical functions for data analysis?

Answer 5

math

Example: math.sqrt(25) → 5.

Question 6

What is list comprehension in Python?

Answer 6

A concise way to create lists using a single line of code.

Example: [x**2 for x in range(5)].

Question 7

How do you create a dictionary comprehension?

Answer 7

{key: value for key, value in iterable}

Example: {x: x**2 for x in range(3)}.

Question 8

True or False:

List comprehensions are faster than traditional loops.

Answer 8

True.

They are optimized for performance in Python.

Question 9

Fill in the blank:

[x for x in range(5) if x % 2 == 0] creates a list of ____ numbers.

Answer 9

Even

Output: [0, 2, 4].

Question 10

How do you modify a list comprehension to include an else statement?

Answer 10

Using if-else before for

Example: [x if x % 2 == 0 else “odd” for x in range(5)].

Question 11

What function opens a file in Python?

Answer 11

open()

Example: f = open(‘data.txt’, ‘r’).

Question 12

How do you read all lines of a file at once?

Answer 12

.readlines()

Returns a list of lines.

Question 13

True or False:

The with statement automatically closes a file.

Answer 13

True.

Example: with open(‘file.txt’) as f:.

Question 14

Fill in the blank:

To write to a file, use the mode ______.

Answer 14

w

“w” creates a new file or overwrites an existing one.

Question 15

What does file.write(“Hello”) do?

Answer 15

Writes “Hello” to the file.

Does not automatically add a newline.

Question 16

Which module helps read CSV files in base Python?

Answer 16

csv

Example: import csv.

Question 17

How do you read a CSV file in Python?

Answer 17

Using csv.reader()

Example: csv.reader(file).

Question 18

True or False:

CSV files can only contain numerical data.

Answer 18

False.

They can contain text, dates, and more.

Question 19

Fill in the blank:

csv.writer() is used to ____ data to a CSV file.

Answer 19

Write

Example: writer.writerow([‘Name’, ‘Age’]).

Question 20

Which library makes reading CSV files easier than using csv?

Answer 20

pandas

Example: df = pd.read_csv(‘data.csv’).

Question 21

What is Pandas?

Answer 21

A Python library for data manipulation and analysis.

Provides DataFrame and Series structures.

Question 22

How do you import Pandas?

Answer 22

import pandas as pd

pd is the most used alias.

Question 23

What is a DataFrame in Pandas?

Answer 23

A 2D labeled data structure.

Similar to a table in SQL or Excel.

Question 24

True or False:

Pandas requires NumPy to function.

Answer 24

True.

Pandas is built on top of NumPy.

Question 25

# Fill in the blank: A Pandas **Series** is similar to a ______.

Answer 25

**Column** in a spreadsheet ## Footnote Series is a 1D labeled array.

Question 26

How do you access the **first five** rows of a DataFrame?

Answer 26

**.head()** ## Footnote Example: df.head(). If zou want a specific number of rows, define the number inside the brackets. eg df.head(7)

Question 27

What does **df.loc[2]** return?

Answer 27

The row at **index 2**. ## Footnote .loc[] is label-based.

Question 28

# True or False: **.iloc[]** is label-based indexing.

Answer 28

**False.** ## Footnote .iloc[] is position-based.

Question 29

How do you filter rows where **age > 30**?

Answer 29

df[df['age'] > 30] ## Footnote Boolean filtering.

Question 30

# Fill in the blank: **df['column']** accesses a ______.

Answer 30

**Series** ## Footnote Example: df['name'].

Question 31

How do you check for *missing* values?

Answer 31

**.isnull()** ## Footnote Returns True for missing values.

Question 32

What does **.isnull().sum()** do?

Answer 32

Summarize the totals of missing values in each column

Question 33

What does **df.dropna()** do?

Answer 33

**Removes rows** with missing values. ## Footnote Default: drops any row with at least one missing value.

Question 34

# True or False: **.fillna()** removes NaN values.

Answer 34

**False.** ## Footnote .fillna() replaces NaNs with a specified value.

Question 35

# Fill in the blank: **.dropna(how='all')** removes rows where ____ values are missing.

Answer 35

**All** ## Footnote Keeps rows with at least one non-null value.

Question 36

How do you replace **missing values** with the column **mean**?

Answer 36

**df['col'].fillna(df['col'].mean())** ## Footnote Useful for numerical data.

Question 37

What does **df.groupby('column')** do?

Answer 37

Groups rows by a column's values. ## Footnote Used for aggregating data.

Question 38

Which method calculates the **mean for each group**?

Answer 38

**.mean()** ## Footnote Example: df.groupby('gender').mean().

Question 39

# True or False: **Pivot tables** are a form of aggregation.

Answer 39

True. ## Footnote They reshape and summarize data.

Question 40

How do you **count** occurrences of **unique values**?

Answer 40

**.value_counts()** ## Footnote Example: df['city'].value_counts().

Question 41

# Fill in the blank: .agg({'col': 'sum'}) applies ______.

Answer 41

A specific function to a column ## Footnote Example: df.groupby('category').agg({'sales': 'sum'}).