Selecting multiple columns in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select columns 'A' and 'B' from the DataFrame df.

Pandas

selected = df[[[1]]]

Drag options to blanks, or click blank then click option'

A["A", "B"]

B"AB"

C("A", "B")

D"A, B"

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string with comma instead of a list.

Using parentheses instead of brackets.

Selecting columns without double brackets.

To select multiple columns in pandas, you pass a list of column names inside double brackets.

2fill in blank

medium

Complete the code to select columns 'Name' and 'Age' from the DataFrame data.

Pandas

subset = data[[[1]]]

Drag options to blanks, or click blank then click option'

A("Name", "Age")

B["Name", "Age"]

C"Name, Age"

D"NameAge"

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string with commas instead of a list.

Using parentheses instead of square brackets.

Use a list of column names inside double brackets to select multiple columns.

3fill in blank

hard

Fix the error in selecting columns 'X' and 'Y' from df.

Pandas

result = df[[1]]

Drag options to blanks, or click blank then click option'

A["X", "Y"]

B"X, Y"

C("X", "Y")

D"XY"

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a string with commas instead of a list.

Using parentheses instead of square brackets.

To select multiple columns, use a list of column names inside double brackets.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 4 letters.

Pandas

lengths = { [1] : [2] for word in words if len(word) > 4 }

Drag options to blanks, or click blank then click option'

Aword

Blen(word)

Cword.upper()

Dlen(words)

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(words) instead of len(word).

Using word.upper() as key instead of word.

The dictionary comprehension uses the word as the key and its length as the value.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys, their lengths as values, but only for words longer than 3 letters.

Pandas

result = { [1] : [2] for word in words if [3] > 3 }

Drag options to blanks, or click blank then click option'

Aword.upper()

Blen(word)

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of word.upper() as key.

Using word instead of len(word) in condition.

The dictionary comprehension uses the uppercase word as key, length as value, and filters words longer than 3 letters.

Practice

1. What is the correct way to select multiple columns 'A' and 'B' from a pandas DataFrame df?

easy

A. df.loc['A', 'B']

B. df['A', 'B']

C. df[['A', 'B']]

D. df['A' & 'B']

Solution

1. Step 1: Understand pandas column selection syntax

   To select multiple columns, pandas requires a list of column names inside double brackets.

2. Step 2: Apply correct syntax for multiple columns

   Using df[['A', 'B']] correctly selects columns 'A' and 'B'. Other options are invalid syntax or select rows instead.

3. Final Answer:

   df[['A', 'B']] -> Option C

4. Quick Check:

   Multiple columns = list inside double brackets [OK]

Hint: Use double brackets with a list for multiple columns [OK]

Common Mistakes:

• Using single brackets with comma-separated names
• Using bitwise operators like & between column names
• Using .loc incorrectly for column selection

2. Which of the following is the correct syntax to select columns 'X' and 'Y' from DataFrame df?

easy

A. df.loc[:, 'X', 'Y']

B. df.loc['X', 'Y']

C. df['X', 'Y']

D. df[['X', 'Y']]

Solution

1. Step 1: Recall syntax for selecting multiple columns

   Using double brackets with a list of column names is the standard way: df[['X', 'Y']].

2. Step 2: Check other options for correctness

   df.loc[:, 'X', 'Y'] causes TypeError (invalid arguments to loc); df['X', 'Y'] raises KeyError (tuple not a column); df.loc['X', 'Y'] is incorrect syntax.

3. Final Answer:

   df[['X', 'Y']] -> Option D

4. Quick Check:

   Double brackets with list = correct syntax [OK]

Hint: Remember: double brackets with list select multiple columns [OK]

Common Mistakes:

• Using single brackets with multiple column names
• Confusing .loc row and column selection syntax
• Using tuple instead of list inside brackets

3. Given the DataFrame df below:

   A  B  C
0  1  4  7
1  2  5  8
2  3  6  9

What is the output of df[['B', 'C']]?

medium

A. B C
0 4 7
1 5 8
2 6 9

B. A B
0 1 4
1 2 5
2 3 6

C. A C
0 1 7
1 2 8
2 3 9

D. KeyError

Solution

1. Step 1: Identify columns requested

   The code selects columns 'B' and 'C' from the DataFrame.

2. Step 2: Extract columns 'B' and 'C' values

   Columns 'B' and 'C' have values [[4,7],[5,8],[6,9]] for rows 0,1,2 respectively.

3. Final Answer:

   B C 0 4 7 1 5 8 2 6 9 -> Option A

4. Quick Check:

   Selected columns 'B' and 'C' output matches

      B  C
   0  4  7
   1  5  8
   2  6  9

   [OK]

Hint: Selected columns show only those columns with all rows [OK]

Common Mistakes:

• Confusing columns selected and expecting all columns
• Getting KeyError by misspelling column names
• Selecting rows instead of columns

4. What error will occur with this code?

df = pd.DataFrame({'X':[1,2], 'Y':[3,4]})
selected = df['X', 'Y']

medium

A. TypeError

B. KeyError

C. SyntaxError

D. No error, returns both columns

Solution

1. Step 1: Analyze the indexing syntax

   The code uses single brackets with a comma, creating a tuple key ('X', 'Y').

2. Step 2: Understand pandas error type

   This raises KeyError because pandas looks for a column named ('X', 'Y'), which doesn't exist.

3. Final Answer:

   KeyError -> Option B

4. Quick Check:

   Tuple key in single brackets causes KeyError [OK]

Hint: Use list inside double brackets, not comma in single brackets [OK]

Common Mistakes:

• Using comma inside single brackets for multiple columns
• Expecting no error with wrong syntax
• Confusing TypeError with KeyError

5. You have a DataFrame df with columns ['Name', 'Age', 'City', 'Salary']. You want to create a new DataFrame df_selected with only 'Name' and 'Salary'. Which code correctly does this and also resets the index?

hard

A. df_selected = df[['Name', 'Salary']].reset_index(drop=True)

B. df_selected = df.loc[:, ['Name', 'Salary']].reset_index()

C. df_selected = df[['Name', 'Salary']].reset_index()

D. df_selected = df[['Name', 'Salary']].reset_index(drop=False)

Solution

1. Step 1: Select columns 'Name' and 'Salary'

   Use double brackets with list: df[['Name', 'Salary']] to select these columns.

2. Step 2: Reset index without adding old index as column

   Use reset_index(drop=True) to reset index and drop old index column.

3. Step 3: Check other options

   df.loc[:, ['Name', 'Salary']].reset_index() resets index but keeps old index as column. Using reset_index() without drop=True (default behavior) keeps the old index as a new column.

4. Final Answer:

   df_selected = df[['Name', 'Salary']].reset_index(drop=True) -> Option A

5. Quick Check:

   Select columns + reset index drop old index = df_selected = df[['Name', 'Salary']].reset_index(drop=True) [OK]

Hint: Select columns with double brackets, reset index with drop=True [OK]

Common Mistakes:

• Forgetting to use double brackets for multiple columns
• Not using drop=True in reset_index to avoid old index column
• Using .loc without understanding index reset effect