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.

Practice

1. What is the main reason Pandas uses NumPy arrays internally?

easy

A. To handle text data only

B. To make data visualization easier

C. To store data efficiently and allow fast computations

D. To create interactive dashboards

Solution

1. Step 1: Understand Pandas data storage

   Pandas stores data in tables called DataFrames, which need efficient storage for speed.

2. Step 2: Role of NumPy arrays

   NumPy arrays provide fast, memory-efficient storage and operations on numerical data.

3. Final Answer:

   To store data efficiently and allow fast computations -> Option C

4. Quick Check:

   Pandas uses NumPy arrays for efficiency [OK]

Hint: Pandas uses NumPy arrays for speed and memory [OK]

Common Mistakes:

• Thinking Pandas uses NumPy only for visualization
• Believing Pandas stores data as plain Python lists
• Confusing NumPy arrays with text handling

2. Which of the following is the recommended way to convert a Pandas DataFrame df to a NumPy array?

easy

A. np.array(df)

B. df.to_numpy()

C. df.numpy()

D. df.asarray()

Solution

1. Step 1: Recall Pandas method for conversion

   Pandas DataFrames have a method to_numpy() to convert to NumPy arrays.

2. Step 2: Check other options

   np.array(df) works but is not the dedicated Pandas method; df.numpy() and df.asarray() do not exist.

3. Final Answer:

   df.to_numpy() -> Option B

4. Quick Check:

   Use to_numpy() to convert DataFrame [OK]

Hint: Use df.to_numpy() to get NumPy array from DataFrame [OK]

Common Mistakes:

• Using non-existent methods like df.numpy()
• Confusing np.array(df) as the best method
• Trying to use df.asarray() which is invalid

3. Given the code:

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
result = np.sum(df)

What is the value of result?

medium

A. array([6, 15])

B. [6 15]

C. 21

D. Error: cannot sum DataFrame with NumPy

Solution

1. Step 1: Understand np.sum on DataFrame

   Applying np.sum to a DataFrame sums each column and returns a NumPy array.

2. Step 2: Calculate sums of columns

   Column 'A' sums to 1+2+3=6, column 'B' sums to 4+5+6=15, so result is array([6, 15]).

3. Final Answer:

   array([6, 15]) -> Option A

4. Quick Check:

   NumPy sums columns into array [OK]

Hint: np.sum on DataFrame sums columns, returns NumPy array [OK]

Common Mistakes:

• Expecting a single number sum of all values
• Thinking np.sum causes error on DataFrame
• Confusing list output with NumPy array

4. What is wrong with this code snippet?

import pandas as pd
import numpy as np
df = pd.DataFrame({'X': [1, 2], 'Y': [3, 4]})
arr = df.values()
print(arr + 1)

medium

A. df.values() should be df.values without parentheses

B. You cannot add 1 to a NumPy array

C. DataFrame does not have a values attribute

D. The print statement syntax is incorrect

Solution

1. Step 1: Check usage of df.values

   df.values is an attribute, not a method, so parentheses cause an error.

2. Step 2: Understand addition to NumPy array

   Adding 1 to a NumPy array works element-wise, so no error there.

3. Final Answer:

   df.values() should be df.values without parentheses -> Option A

4. Quick Check:

   Use df.values without () to get NumPy array [OK]

Hint: df.values is attribute, not method; no parentheses [OK]

Common Mistakes:

• Calling df.values() as a method
• Thinking addition to array is invalid
• Assuming print syntax is wrong in Python 3

5. You have a Pandas DataFrame df with numeric columns. You want to apply the NumPy function np.log to all values and get a new DataFrame with the results preserving the original index and column names. Which code correctly does this?

hard

A. new_df = pd.DataFrame(np.log(df))

B. new_df = df.to_numpy().log()

C. new_df = np.log(df)

D. new_df = df.apply(np.log)

Solution

1. Step 1: Understand applying NumPy functions on DataFrame

   Using df.apply(np.log) applies np.log element-wise to each column and returns a DataFrame preserving index and columns.

2. Step 2: Check other options

   np.log(df) returns a NumPy array, not DataFrame; pd.DataFrame(np.log(df)) creates DataFrame but loses original index and column names; df.to_numpy().log() is invalid syntax.

3. Final Answer:

   new_df = df.apply(np.log) -> Option D

4. Quick Check:

   Use df.apply(np.log) for DataFrame output [OK]

Hint: Use df.apply(np.log) to keep DataFrame structure [OK]

Common Mistakes:

• Using np.log(df) which returns array, not DataFrame
• Trying df.to_numpy().log() which is invalid
• Wrapping np.log(df) inside pd.DataFrame unnecessarily