Adding and removing columns in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add a new column 'C' with all values set to 10.

Data Analysis Python

import pandas as pd

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

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A'10'

B[10, 10]

Crange(2)

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Assigning a single integer without a list causes the column to fill with that integer for all rows.

Assigning a string '10' creates a column of strings, not integers.

To add a new column with the same value for each row, you can assign a list with the values. Here, [10, 10] matches the number of rows.

2fill in blank

medium

Complete the code to remove the column 'B' from the DataFrame.

Data Analysis Python

import pandas as pd

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

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Adelete

Bremove

Cdrop

Dpop

Attempts:

3 left

💡 Hint

Common Mistakes

Using remove or delete which are not pandas DataFrame methods.

Forgetting to set axis=1 to specify column removal.

The drop method removes columns or rows. To remove a column, specify the column name and axis=1.

3fill in blank

hard

Fix the error in the code to add a new column 'D' as the sum of columns 'A' and 'B'.

Data Analysis Python

import pandas as pd

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

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A/

B-

C*

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using subtraction or multiplication instead of addition.

Using division which would not sum the columns.

To add two columns element-wise, use the plus operator +.

4fill in blank

hard

Fill both blanks to create a new column 'E' with values from column 'A' multiplied by 2, but only for rows where 'B' is greater than 3.

Data Analysis Python

import pandas as pd

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

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A*

B>

C<

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication.

Using less than operator instead of greater than for filtering.

Multiply column 'A' by 2 using *. Filter rows where 'B' is greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with keys as uppercase column names and values as the mean of each column, but only for columns where the mean is greater than 2.

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'a': [1, 3, 5], 'b': [2, 4, 6], 'c': [0, 1, 1]})
result = { [1]: df[[2]].mean() for [3] in df.columns if df[[3]].mean() > 2 }
print(result)

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Acol.upper()

Bcol

Dcol.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase for keys instead of uppercase.

Using wrong variable names in the comprehension.

The dictionary comprehension uses col.upper() as keys, col to select columns, and iterates over df.columns.