Creating new columns in Pandas - Practice Exercises

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of adding a new column with arithmetic operation

What is the output DataFrame after running this code?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df['C'] = df['A'] + df['B']
print(df)

A {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [1, 2, 3]}

B {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [5, 7, 9]}

C {'A': [1, 2, 3], 'B': [4, 5, 6]}

D {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [4, 5, 6]}

💡 Hint

Adding columns element-wise sums the values in each row.

The new column 'C' is created by adding columns 'A' and 'B' element-wise, resulting in [5, 7, 9].

❓ data_output

intermediate

Result of creating a column with a constant value

What is the DataFrame output after adding a new column 'D' with a constant value 10?

Pandas

import pandas as pd

df = pd.DataFrame({'X': [7, 8, 9]})
df['D'] = 10
print(df)

A {'X': [7, 8, 9]}

B {'X': [7, 8, 9], 'D': [7, 8, 9]}

C {'X': [7, 8, 9], 'D': [10, 10, 10]}

D {'X': [7, 8, 9], 'D': [0, 0, 0]}

💡 Hint

Assigning a single value to a new column repeats it for all rows.

The new column 'D' is filled with the constant value 10 for every row.

🔧 Debug

advanced

Identify the error when creating a new column with a list of wrong length

What error does this code raise?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})
df['B'] = [4, 5]
print(df)

A ValueError: Length of values (2) does not match length of index (3)

B KeyError: 'B'

C TypeError: unsupported operand type(s) for +: 'int' and 'list'

D No error, prints DataFrame with NaN in last row

💡 Hint

The list assigned must match the number of rows exactly.

Assigning a list of length 2 to a DataFrame with 3 rows causes a ValueError due to length mismatch.

🚀 Application

advanced

Create a new column based on condition from existing column

Given a DataFrame with column 'score', which option creates a new column 'passed' that is True if score >= 50, else False?

Pandas

import pandas as pd

df = pd.DataFrame({'score': [45, 55, 60, 30]})

A df['passed'] = df['score'] > 50

B df['passed'] = df['score'] < 50

C df['passed'] = df['score'] == 50

D df['passed'] = df['score'] >= 50

💡 Hint

Use comparison operator >= to include 50 as passing.

The condition 'score >= 50' correctly marks scores 50 and above as True for passing.

❓ visualization

expert

Visualize the effect of creating a new column with a function

Which option shows the correct bar chart after creating a new column 'length' with the length of strings in column 'words'?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'words': ['apple', 'banana', 'pear']})
df['length'] = df['words'].apply(len)
df.plot.bar(x='words', y='length')
plt.show()

A Bar chart with 'words' on x-axis and bars showing lengths [5, 6, 4]

B Bar chart with 'words' on x-axis and bars showing lengths [6, 5, 4]

C Line chart with 'words' on x-axis and lengths on y-axis

D Scatter plot with 'words' and 'length' values

💡 Hint

The length of 'apple' is 5, 'banana' is 6, 'pear' is 4.

Applying len to each string creates the 'length' column with correct values, shown as bars in the chart.