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June 26, 2026

Chapter -1 Python Pandas Question and Answer

2 marks Question and Answer

1. What is a Series in Pandas? Write the syntax to create a Series.

Answer:-

A Series in Pandas is a one-dimensional labeled array that can store data of any type such as integers, strings, floats, etc. Each value in a Series is associated with an index.

Syntax:

import pandas as pd
series_name = pd.Series(data)

Example:

import pandas as pd

s = pd.Series([10, 20, 30, 40])
print(s)

Output:

0    10
1    20
2    30
3    40
dtype: int64

2. Differentiate between Series and DataFrame.

Answer :-

Series DataFrame
A Series is a one-dimensional labeled array. A DataFrame is a two-dimensional labeled data structure.
It contains a single column of data. It contains multiple rows and columns.
Created using pd.Series(). Created using pd.DataFrame().
Can store data of a single type or mixed types. Each column can have different data types.
Has only one axis (index). Has two axes (rows and columns).

Example of Series:

import pandas as pd

s = pd.Series([10, 20, 30])
print(s)

Output:

0    10
1    20
2    30
dtype: int64

Example of DataFrame:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Amit', 'Riya'],
    'Marks': [85, 90]
})

print(df)

Output:

   Name  Marks
0  Amit     85
1  Riya     90

3. Write any two advantages of Pandas.

Answer :-

  1. Efficient Data Handling: Pandas provides powerful data structures like Series and DataFrame to store, organize, and manipulate large amounts of data efficiently.
  2. Data Analysis and Cleaning: Pandas offers built-in functions for filtering, sorting, handling missing values, merging datasets, and performing statistical analysis, making data preparation easy.

4. What is a DataFrame? Give an example.

Answer :-

A DataFrame is a two-dimensional data structure in Pandas that stores data in the form of rows and columns, similar to a spreadsheet or a database table. Each column can contain different types of data such as integers, strings, or floats.

Example:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78]
})

print(df)

Output:

    Name  Marks
0   Amit     85
1   Riya     90
2  Karan     78

5. Explain the use of shape and size attributes.

Answer :-

In Pandas, the shape and size attributes are used to obtain information about the dimensions of a DataFrame.

1. shape Attribute

The shape attribute returns a tuple representing the number of rows and columns in a DataFrame.

Syntax:

df.shape

Example:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78]
})

print(df.shape)

Output:

(3, 2)

Here, 3 represents the number of rows and 2 represents the number of columns.

2. size Attribute

The size attribute returns the total number of elements in a DataFrame.

Syntax:

df.size

Example:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78]
})

print(df.size)

Output:

6

6. Differentiate between head() and tail() functions in Pandas.

Answer :-

The head() and tail() functions in Pandas are used to view records from a DataFrame.

head() tail()
Displays the first few rows of a DataFrame. Displays the last few rows of a DataFrame.
By default, it shows the first 5 rows. By default, it shows the last 5 rows.
Useful for checking the beginning of a dataset. Useful for checking the end of a dataset.
Syntax: df.head(n) Syntax: df.tail(n)

Example:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan', 'Neha', 'Rahul', 'Pooja'],
    'Marks': [85, 90, 78, 88, 92, 80]
})

print(df.head(3))
print(df.tail(3))

Output:

head(3)

    Name  Marks
0   Amit     85
1   Riya     90
2  Karan     78

tail(3)

    Name  Marks
3   Neha     88
4  Rahul     92
5  Pooja     80

7. Write any two functions used to obtain statistical information from a DataFrame.

Answer :-

Pandas provides several functions to obtain statistical information from a DataFrame. Any two of them are:

1. mean()

Returns the average (mean) value of numeric data in a DataFrame column.

Example:

df['Marks'].mean()

2. max()

Returns the maximum value from a DataFrame column.

Example:

df['Marks'].max()

Other Statistical Functions:

  • min() – Returns the minimum value.
  • sum() – Returns the sum of values.
  • count() – Returns the number of non-null values.
  • median() – Returns the median value.
  • std() – Returns the standard deviation.

8. Explain the use of the info() function in Pandas.

Answer :-

The info() function in Pandas is used to display a concise summary of a DataFrame. It provides important information such as:

  • Number of rows and columns.
  • Column names.
  • Number of non-null values in each column.
  • Data type (dtype) of each column.
  • Memory usage of the DataFrame.

Syntax:

df.info()

Example:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78]
})

df.info()

Output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   Name    3 non-null      object
 1   Marks   3 non-null      int64
dtypes: int64(1), object(1)
memory usage: 176 bytes

9. Write the syntax to read a CSV file using Pandas.

Answer :-

In Pandas, the read_csv() function is used to read data from a CSV (Comma-Separated Values) file and load it into a DataFrame.

Syntax:

import pandas as pd

df = pd.read_csv("filename.csv")

Example:

import pandas as pd

df = pd.read_csv("students.csv")
print(df)

10. What are row labels and column labels in a DataFrame?

Answer :-

In a Pandas DataFrame, row labels and column labels are used to identify rows and columns.

Row Labels (Index):

Row labels are unique identifiers assigned to each row in a DataFrame. By default, Pandas assigns row labels starting from 0, 1, 2, ....

Column Labels:

Column labels are the names given to the columns of a DataFrame. They help identify the data stored in each column.

Example:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78]
})

print(df)

Output:

    Name  Marks
0   Amit     85
1   Riya     90
2  Karan     78

In the above DataFrame:

  • Row Labels (Index): 0, 1, 2
  • Column Labels: Name, Marks

3marks Question and Answer

1. Explain any three DataFrame attributes with examples.

Answer :-

DataFrame attributes provide information about the structure and contents of a DataFrame. Three commonly used DataFrame attributes are:

1. shape

The shape attribute returns the number of rows and columns in a DataFrame as a tuple (rows, columns).

Example:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78]
})

print(df.shape)

Output:

(3, 2)

2. size

The size attribute returns the total number of elements in a DataFrame.

Example:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78]
})

print(df.size)

Output:

6

(3 rows × 2 columns = 6 elements)

3. columns

The columns attribute returns the labels (names) of all columns in the DataFrame.

Example:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78]
})

print(df.columns)

Output:

Index(['Name', 'Marks'], dtype='object')

Summary Table

Attribute Purpose
shape Returns the number of rows and columns.
size Returns the total number of elements.
columns Returns the names of all columns.

2. Write a program to create a Series of five numbers and display its maximum and minimum value.

Answer :-

import pandas as pd

# Creating a Series of five numbers
s = pd.Series([10, 25, 5, 40, 15])

# Displaying the Series
print("Series:")
print(s)

# Displaying maximum and minimum values
print("Maximum Value:", s.max())
print("Minimum Value:", s.min())

Output:

Series:
0    10
1    25
2     5
3    40
4    15
dtype: int64

Maximum Value: 40
Minimum Value: 5

3. Explain the purpose of describe() function with an example.

Answer :-

The describe() function in Pandas is used to generate summary statistics of numerical data in a DataFrame. It provides useful statistical information such as:

  • Count of values (count)
  • Mean (mean)
  • Standard Deviation (std)
  • Minimum value (min)
  • 25%, 50%, and 75% percentile values
  • Maximum value (max)

Syntax:

df.describe()

Example:

import pandas as pd

df = pd.DataFrame({
    'Marks': [85, 90, 78, 88, 92]
})

print(df.describe())

Output:

           Marks
count   5.000000
mean   86.600000
std     5.595534
min    78.000000
25%    85.000000
50%    88.000000
75%    90.000000
max    92.000000

Q4. Create a DataFrame containing Name and Marks of three students and display the first two records.

Answer :-

import pandas as pd

# Creating a DataFrame
df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78]
})

# Displaying the first two records
print(df.head(2))

Output:

   Name  Marks
0  Amit     85
1  Riya     90

Q5. Differentiate between Series and DataFrame with suitable examples.

Answer :-

Series and DataFrame are the two main data structures provided by the Pandas library. A Series is a one-dimensional labeled array, whereas a DataFrame is a two-dimensional labeled data structure that consists of rows and columns.

Difference between Series and DataFrame

Series DataFrame
A Series is a one-dimensional labeled array. A DataFrame is a two-dimensional labeled data structure.
It contains a single column of data. It contains multiple rows and columns.
Created using pd.Series(). Created using pd.DataFrame().
Has only one axis (Index). Has two axes (Rows and Columns).
Suitable for storing a single list of values. Suitable for storing tabular data.
Can store one column of data. Can store multiple columns with different data types.
Uses index labels only. Uses both row labels (index) and column labels.

Example of Series

import pandas as pd

s = pd.Series([10, 20, 30, 40])

print(s)

Output

0    10
1    20
2    30
3    40
dtype: int64

Example of DataFrame

import pandas as pd

df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78]
})

print(df)

Output

    Name  Marks
0   Amit     85
1   Riya     90
2  Karan     78

Conclusion

A Series is used to store a single column of labeled data, while a DataFrame is used to store data in a tabular form consisting of multiple rows and columns. DataFrames are more flexible and are widely used for data analysis and data manipulation in Pandas.

Q6. Write a program to create a DataFrame and display its shape, size, and dimensions.

Answer :-

In Pandas, a DataFrame is a two-dimensional data structure used to store data in rows and columns. The attributes shape, size, and ndim provide important information about the DataFrame.

  • shape – Returns the number of rows and columns as a tuple (rows, columns).
  • size – Returns the total number of elements in the DataFrame.
  • ndim – Returns the number of dimensions of the DataFrame. For a DataFrame, the value is always 2.

Program:

import pandas as pd

# Creating a DataFrame
df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78]
})

# Displaying the DataFrame
print("DataFrame:")
print(df)

# Displaying shape
print("\nShape:", df.shape)

# Displaying size
print("Size:", df.size)

# Displaying dimensions
print("Dimensions:", df.ndim)

Output:

DataFrame:
    Name  Marks
0   Amit     85
1   Riya     90
2  Karan     78

Shape: (3, 2)
Size: 6
Dimensions: 2

Explanation:

Attribute Output Description
shape (3, 2) Shows that the DataFrame has 3 rows and 2 columns.
size 6 Shows the total number of elements (3 × 2 = 6).
ndim 2 Indicates that a DataFrame is two-dimensional.

Conclusion:

The shape, size, and ndim attributes help users understand the structure and dimensions of a DataFrame. These attributes are frequently used in data analysis to inspect datasets before performing further operations.

Q7. Explain the functions count(), max(), and min() with examples.

Answer :-

Pandas provides several built-in functions to perform statistical operations on the data stored in a DataFrame or Series. The count(), max(), and min() functions are commonly used to analyze numerical data.

1. count() Function

The count() function returns the number of non-null (non-missing) values in a DataFrame or Series.

Syntax:

df['Column_Name'].count()

Example:

import pandas as pd

df = pd.DataFrame({
    'Marks': [85, 90, 78, 88, 92]
})

print(df['Marks'].count())

Output:

5

2. max() Function

The max() function returns the largest value from a DataFrame column or Series.

Syntax:

df['Column_Name'].max()

Example:

import pandas as pd

df = pd.DataFrame({
    'Marks': [85, 90, 78, 88, 92]
})

print(df['Marks'].max())

Output:

92

3. min() Function

The min() function returns the smallest value from a DataFrame column or Series.

Syntax:

df['Column_Name'].min()

Example:

import pandas as pd

df = pd.DataFrame({
    'Marks': [85, 90, 78, 88, 92]
})

print(df['Marks'].min())

Output:

78

Summary Table

Function Purpose Example Output
count() Returns the number of non-null values. 5
max() Returns the largest value. 92
min() Returns the smallest value. 78

Conclusion

The count(), max(), and min() functions are essential statistical functions in Pandas. They help users quickly analyze data by finding the number of available values, the highest value, and the lowest value in a DataFrame or Series.

Q8. Write a program to create a Series with custom indexes.

Answer :-

In Pandas, a Series is a one-dimensional labeled array. By default, Pandas assigns index values starting from 0. However, we can also create a Series with custom indexes using the index parameter of the pd.Series() function.

Syntax:

series_name = pd.Series(data, index=index_values)

Program:

import pandas as pd

# Creating a Series with custom indexes
s = pd.Series(
    [85, 90, 78],
    index=['Amit', 'Riya', 'Karan']
)

# Displaying the Series
print(s)

Output:

Amit     85
Riya     90
Karan    78
dtype: int64

Explanation:

  • The pd.Series() function is used to create a Series.
  • The index parameter is used to assign custom labels instead of the default numeric indexes.
  • Here, Amit, Riya, and Karan are the custom index labels.
  • The corresponding values are 85, 90, and 78.

Summary Table

Function Purpose
pd.Series() Creates a Series object.
index Assigns custom labels to the Series.

Conclusion:

Custom indexes make the data more meaningful and easier to access. Instead of using default numeric indexes, descriptive labels can be assigned to identify each value in the Series.

Q9. What is the purpose of dtypes and columns attributes? Explain with example.

Answer :-

In Pandas, dtypes and columns are important DataFrame attributes that provide information about the structure of the DataFrame. The dtypes attribute is used to display the data type of each column, while the columns attribute is used to display the names of all the columns present in the DataFrame.

1. dtypes Attribute

The dtypes attribute returns the data type (dtype) of every column in the DataFrame. It helps identify whether a column contains integers, floating-point numbers, strings, boolean values, etc.

Syntax:

df.dtypes

2. columns Attribute

The columns attribute returns the names (labels) of all columns in the DataFrame. It is useful when we want to know or access the available columns in a dataset.

Syntax:

df.columns

Example Program

import pandas as pd

# Creating a DataFrame
df = pd.DataFrame({
    'Name': ['Amit', 'Riya', 'Karan'],
    'Marks': [85, 90, 78],
    'Passed': [True, True, False]
})

# Displaying data types
print("Data Types:")
print(df.dtypes)

# Displaying column names
print("\nColumn Names:")
print(df.columns)

Output

Data Types:
Name      object
Marks      int64
Passed      bool
dtype: object

Column Names:
Index(['Name', 'Marks', 'Passed'], dtype='object')

Explanation

  • Name has the data type object because it stores text values.
  • Marks has the data type int64 because it stores integer values.
  • Passed has the data type bool because it stores Boolean values (True or False).
  • The columns attribute returns the names of all columns: Name, Marks, and Passed.

Summary Table

Attribute Purpose Example Output
dtypes Displays the data type of each column. Name → object, Marks → int64, Passed → bool
columns Displays the names of all columns. Index(['Name', 'Marks', 'Passed'])

Conclusion

The dtypes and columns attributes are very useful while working with DataFrames. They help users understand the data types of each column and identify all column names, making data analysis and manipulation easier.

Q10. Write a program to read a CSV file and display the first five records.

Answer :-

A CSV (Comma-Separated Values) file is a text file used to store tabular data such as student records, employee details, and sales information. In Pandas, the read_csv() function is used to read data from a CSV file and store it in a DataFrame. The head() function is then used to display the first few records of the DataFrame. By default, head() displays the first 5 rows.

Syntax:

import pandas as pd

df = pd.read_csv("filename.csv")
print(df.head())

Program:

import pandas as pd

# Reading the CSV file
df = pd.read_csv("students.csv")

# Displaying the first five records
print(df.head())

Sample CSV File (students.csv):

Name,Marks,Grade
Amit,85,A
Riya,90,A+
Karan,78,B
Neha,88,A
Rahul,92,A+
Pooja,80,B+

Output:

    Name  Marks Grade
0   Amit     85     A
1   Riya     90    A+
2  Karan     78     B
3   Neha     88     A
4  Rahul     92    A+

Explanation:

  • The import pandas as pd statement imports the Pandas library.
  • The read_csv() function reads the contents of the students.csv file and stores them in a DataFrame.
  • The head() function displays the first five records of the DataFrame.
  • If the DataFrame contains fewer than five rows, all available rows are displayed.

Summary Table

Function Purpose
read_csv() Reads data from a CSV file and creates a DataFrame.
head() Displays the first five records of a DataFrame by default.

Conclusion:

The read_csv() function is used to import data from a CSV file, while the head() function is used to quickly view the first five records of the dataset. These functions are widely used in data analysis to inspect data before performing further operations.

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