The correct answer is D. all of the mentioned.
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language.
Pandas DataFrame is a two-dimensional labeled data structure with columns of potentially different types. It is very similar to a spreadsheet or SQL table, but with more flexibility. DataFrames can be used to store and manipulate data for a variety of purposes, such as data analysis, statistical modeling, and machine learning.
Pandas supports a variety of data types, including:
- Python dicts
- NumPy arrays
- Scalar values
Any of these data types can be used to create a Pandas DataFrame. For example, the following code creates a DataFrame from a Python dict:
“`
import pandas as pd
df = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})
print(df)
A B
0 1 4
1 2 5
2 3 6
“`
The following code creates a DataFrame from a NumPy array:
“`
import pandas as pd
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6]]))
print(df)
A B
0 1 4
1 2 5
2 3 6
“`
The following code creates a DataFrame from a scalar value:
“`
import pandas as pd
df = pd.DataFrame({‘A’: 1})
print(df)
A
0 1
“`
As you can see, Pandas supports a variety of data types, which makes it a very flexible tool for data analysis.