Pandas and NumPy connection - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the relationship between Pandas and NumPy?

Pandas is built on top of NumPy. It uses NumPy arrays to store data internally, which allows fast numerical operations and efficient memory use.

beginner

How can you convert a Pandas DataFrame column to a NumPy array?

You can use the .to_numpy() method on a DataFrame column to get a NumPy array of its values.

intermediate

Why is NumPy important for Pandas performance?

NumPy provides fast, vectorized operations on arrays. Pandas uses these to perform calculations quickly on large datasets without slow Python loops.

intermediate

How do Pandas and NumPy handle missing data differently?

NumPy uses np.nan to represent missing values in float arrays. Pandas extends this by supporting NaN in numeric columns and None or pd.NA in other types.

beginner

Show a simple example of creating a Pandas Series from a NumPy array.

Example:<br><pre>import numpy as np
import pandas as pd
arr = np.array([10, 20, 30])
series = pd.Series(arr)
print(series)</pre>

Which method converts a Pandas DataFrame column to a NumPy array?

A .array()

B .to_list()

C .asarray()

D .to_numpy()

The correct method is .to_numpy() which returns the column as a NumPy array.

What does Pandas use internally to store data?

A Python lists

B NumPy arrays

C Dictionaries

D Tuples

Pandas stores data internally as NumPy arrays for efficient computation.

Which of these is a missing value marker in NumPy arrays?

A NaN

B None

C pd.NA

D 0

NumPy uses np.nan (Not a Number) to represent missing values in float arrays.

Why does Pandas rely on NumPy for performance?

A NumPy uses less memory

B NumPy has faster loops

C NumPy provides vectorized operations

D NumPy supports strings better

NumPy provides vectorized operations that allow fast calculations without slow Python loops.

Which of these is NOT a way Pandas handles missing data?

A 0

B pd.NA

C np.nan

D None

Zero (0) is a valid number, not a missing data marker.

Explain how Pandas and NumPy work together to handle data efficiently.

Think about how Pandas builds on NumPy's strengths.

Describe how to convert data between Pandas and NumPy formats and why you might do this.

Consider when you want to use NumPy functions on Pandas data.