Programming In Python

In Python, the most fundamental library for numerical computations is numpy.
The core feature of numpy is the ndarray (N-dimensional array) object.
It allows efficient storage and manipulation of large numerical datasets.
pandas uses ndarray internally but does not define it itself.
matplotlib is used for plotting and creating visualizations.
scipy extends numpy with scientific computing tools but relies on numpy's ndarray.
Therefore, numpy is the library that defines ndarray.

In Python Pandas, the standard way to change a column name is by using the rename() method.
This method allows you to pass a dictionary that maps the old column name to the new column name.
You must also specify axis='columns' or axis=1 to indicate that the renaming is for column labels.
So the syntax looks like: df = df.rename({old_name : new_name}, axis='columns').
Option (B) is invalid because rename() does not accept positional arguments like old_name and new_name separately.
Option (C) is invalid because there is no method called change_name() in Pandas.
Option (D) is invalid because update() does not rename columns; it updates data values.
Therefore, the correct way to rename a column is option (A).

The matplotlib library is widely used in Python for creating static, animated, and interactive visualizations.
After creating a plot using matplotlib.pyplot, you often want to save it as an image file (e.g., PNG, PDF, SVG).
To do this, matplotlib provides the function savefig().
Here’s how it works:

import matplotlib.pyplot as plt plt.plot([1, 2, 3], [4, 5, 6])
plt.title("Example Plot")
plt.savefig("plot.png")

This saves the current figure as a file named plot.png in the working directory.
Other options like save(), saveplot(), or export() do not exist in matplotlib.
Therefore, the correct answer is savefig().

The statement given is False.
In Python, using the pandas library, it is entirely possible and very common to create an empty DataFrame.
An empty DataFrame is simply a DataFrame object that contains no rows and no columns initially.
This is often useful when you want to create a structure first and then append data later through operations such as loops, conditional statements, or by reading in data from external sources.
Example:
To create an empty DataFrame, you can write:

import pandas as pd
df = pd.DataFrame()
print(df)

When you run this code, the output will be:

Empty DataFrame
Columns: []
Index: []

This shows that the DataFrame has no columns and no index values, confirming that it is empty.
Moreover, you can also create an empty DataFrame with specified column names but no data. For example:

df2 = pd.DataFrame(columns=['Name', 'Age', 'Marks'])
print(df2)

This will output:

Empty DataFrame
Columns: [Name, Age, Marks]
Index: []

This is very useful when you know the structure of your data in advance but will fill it dynamically later.
Therefore, the claim that we cannot create an empty DataFrame in Python is not correct.