Which statement correctly describes creating a Pandas Series from a list when no index is provided?

The default integer index 0,1,2,... is assigned automatically

The values are treated as keys and the list indices become values

An error occurs unless an index is specified

The Series becomes a dictionary

When creating a Series from a dictionary with a provided index that partially matches the dictionary keys, what happens to labels not found in the dictionary?

They are filled with NaN (Not a number)

They are replaced with the first key

Which statement about labeled vs positional indexing in a Series is correct?

Positional index retrieves values from START to END by default

Labeled index retrieves values from END to START by default

Negative step is not allowed in slicing

Labeled indexing uses integer positions only

Given s = pd.Series([5,20,10,80,25], index=['a','b','c','d','e']), what is the result of applying s>20 on the whole Series?

A single True/False for the entire Series

A Series of values greater than 20

A single True/False based on whether any value exceeds 20

A single True/False per label stored as booleans

Using the same Series, what will s[s>20] return?

A filtered Series containing values greater than 20

The indices where values are greater than 20

Only the maximum value in the Series

A boolean indicating if all values are greater than 20

Which statement best defines a DataFrame in Pandas?

A 2-dimensional heterogeneous data structure arranged in tabular form with rows and columns

A fixed-size numeric matrix without labels

A collection of Python dictionaries without indexing

Which code correctly creates an empty DataFrame using Pandas?

df = pd.DataFrame([]).fillna(0)

You have a nested list lst = [['Amar',60,68,45], ['Akbar',65,65,56], ['Anthony',70,77,65]]. Which call builds a DataFrame with row index [1,2,3] and columns ['Name','Phy','Chem','Maths']?

pd.DataFrame(lst, index=[1,2,3], columns=['Name','Phy','Chem','Maths'])

pd.DataFrame(columns=['Name','Phy','Chem','Maths'], index=[1,2,3])

pd.DataFrame(lst).set_index(['Name','Phy','Chem','Maths'])

pd.DataFrame.from_records(lst, columns='Name Phy Chem Maths')

When creating a DataFrame from a list of dictionaries, what becomes the column labels by default?

Sequential numbers starting at 0

Given Series s1=pd.Series(['Amar','Akbar','Anthony'], index=[1,2,3]), s2=pd.Series([60,65,70], index=[1,2,3]), s3=pd.Series([68,65,77], index=[1,2,3]), s4=pd.Series([45,56,65], index=[1,2,3]); which command creates a DataFrame with columns 'Name','Phy','Chem','Maths' using a dictionary of Series?

pd.DataFrame({'Name':s1,'Phy':s2,'Chem':s3,'Maths':s4})

pd.DataFrame.from_dict([s1,s2,s3,s4])

pd.DataFrame({'Name','Phy','Chem','Maths': [s1,s2,s3,s4]})

Which statement about accessing rows in a DataFrame is correct based on the material?

Rows can be accessed by giving an index range label like , and if labels are positional numbers, START to END will display

Rows must be accessed only by integer positions using iloc

Rows cannot be sliced; only single rows can be printed

head() cannot be used with DataFrames

Given a DataFrame df and the statement df['Phy']>60, what is produced?

A Boolean Series indicating which rows satisfy Phy > 60

A filtered DataFrame with rows where Phy > 60

An error because comparison is not allowed

A new column added automatically

Which command filters df to only rows where the 'Chem' column is greater than 60 and selects columns 'CS' and 'Maths'?

df.loc[df['Chem']>60, ['CS','Maths']]

df[df['Chem']>60]['CS','Maths']

df.iloc[df['Chem']>60, ['CS','Maths']]

df.at[df['Chem']>60, ['CS','Maths']]

What happens when you execute df['IP'] = [45,54,67] on a DataFrame?

It adds a new column named 'IP' with given values or overwrites it if it exists

It modifies an existing column only if it exists; otherwise raises an error

Which expression correctly creates a 'Total' column as the sum of 'Phy', 'Chem', and 'Maths'?

df['Total'] = df['Phy'] + df['Chem'] + df['Maths']

df['Total'] = sum(df['Phy'], df['Chem'], df['Maths'])

df['Total'] = df[['Phy','Chem','Maths']].sum

df['Total'] = df.sum(axis='columns')['Phy','Chem','Maths']

Which command best matches the example to rename row labels P,Q,R,S to one,two,three,four respectively?

df.rename(index={'P':'one','Q':'two','R':'three','S':'four'})

df.rename(columns={'P':'one','Q':'two','R':'three','S':'four'})

df.set_index({'P':'one','Q':'two','R':'three','S':'four'})

df.reindex({'P':'one','Q':'two','R':'three','S':'four'})

You need to delete two columns, 'Phy' and 'Chem', from a DataFrame and return a new object without modifying the original. Which option aligns with the examples?

df.drop(['Phy','Chem'], axis=1)

df.drop(['Phy','Chem'], axis=0)

df.loc[:, ['Phy','Chem']] = None

When applying min() with axis=1 on a DataFrame, what does it compute?

Minimum of the entire DataFrame

In vectorized operations on a DataFrame, what happens when you execute df['Chem']=df['Chem']-5 and then df['Maths']=df['Maths'].add(5)?

Subtracts 5 from every value in 'Chem' and adds 5 to every value in 'Maths'

Adds 5 to every value in 'Chem' and subtracts 5 from 'Maths'

Replaces 'Chem' with only 5 and 'Maths' with NaN

Performs no change because arithmetic is not allowed on columns

UNIT-I: Introduction to Python Libraries and Pandas Series

Authored by Tanuja B

Computers

12th Grade

Used 1+ times

UNIT-I: Introduction to Python Libraries and Pandas Series

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MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which statement best describes a Python library?

A collection of Python modules providing ready-to-use functions for specific tasks

A standalone application that replaces the Python interpreter

A data file used only for plotting graphs

A hardware-specific driver package

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which library is commonly used for data manipulation and analysis, especially with Series and DataFrame?

matplotlib

numpy

pandas

statistics

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Identify a listed feature of the Pandas library.

Provides only unlabelled arrays

Supports label-based slicing and indexing

Requires manual memory management for arrays

Works only with scalar values

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In Pandas, which statement about a Series is correct?

It is a two-dimensional heterogeneous data structure

It is one-dimensional and has labelled index

It cannot be modified after creation

Its default positional index starts from 1

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Choose the correct import statement to use standard shorthand names for Pandas and NumPy as shown.

import pandas as pd; import numpy as np

import pandas as np; import numpy as pd

import pandas; import numpy

from pandas import numpy

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

A DataFrame in Pandas is described as which of the following?

One-dimensional homogeneous data with labelled index

Two-dimensional heterogeneous data with rows and columns like a sheet

A plotting interface for graphs

An immutable table that cannot add rows or columns

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

When creating an empty Series using the Series() function without parameters, what is the dtype shown in the output example?

int64

object

float64

bool

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UNIT-I: Introduction to Python Libraries and Pandas Series

Which statement best describes a Python library?

Which library is commonly used for data manipulation and analysis, especially with Series and DataFrame?

Identify a listed feature of the Pandas library.

In Pandas, which statement about a Series is correct?

Choose the correct import statement to use standard shorthand names for Pandas and NumPy as shown.

A DataFrame in Pandas is described as which of the following?

When creating an empty Series using the Series() function without parameters, what is the dtype shown in the output example?

Which statement correctly describes creating a Pandas Series from a list when no index is provided?

When creating a Series from a dictionary with a provided index that partially matches the dictionary keys, what happens to labels not found in the dictionary?

Consider s = pd.Series([10,20,30,40,50,60], index=['a','b','c','d','e','f']). What is returned by s[1]?

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