Creating DataFrame from list of lists in Pandas - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a DataFrame from the list of lists named data.

Pandas

import pandas as pd
data = [[1, 'Alice'], [2, 'Bob'], [3, 'Charlie']]
df = pd.[1](data)

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

Aread_csv

BSeries

CPanel

DDataFrame

Attempts:

3 left

💡 Hint

Common Mistakes

Using Series instead of DataFrame causes wrong data structure.

Using read_csv tries to read from a file, not from a list.

The DataFrame constructor creates a DataFrame from the list of lists.

2fill in blank

medium

Complete the code to assign column names 'ID' and 'Name' to the DataFrame.

Pandas

import pandas as pd
data = [[1, 'Alice'], [2, 'Bob'], [3, 'Charlie']]
df = pd.DataFrame(data, columns=[1])

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

A['ID', 'Name']

B'ID, Name'

C('ID', 'Name')

D{'ID', 'Name'}

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a string instead of a list causes a single column named with the whole string.

Using a set changes the order and causes errors.

Column names must be given as a list of strings.

3fill in blank

hard

Fix the error in the code to create a DataFrame from data with columns 'A' and 'B'.

Pandas

import pandas as pd
data = [[10, 20], [30, 40]]
df = pd.DataFrame(data, columns=[1])

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

A['A', 'B']

B'A, B'

C['A']

D['A', 'B', 'C']

Attempts:

3 left

💡 Hint

Common Mistakes

Giving more or fewer column names than data columns causes errors.

Passing a string instead of a list causes wrong column names.

The number of column names must match the number of columns in data.

4fill in blank

hard

Fill both blanks to create a DataFrame from data and set the index to the first column.

Pandas

import pandas as pd
data = [[101, 'John'], [102, 'Jane'], [103, 'Doe']]
df = pd.DataFrame(data, columns=[1]).set_index([2])

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

A['ID', 'Name']

B'ID'

C'Name'

D['Name', 'ID']

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name for index causes unexpected results.

Passing columns as a string instead of a list causes errors.

Columns are named with a list, and the index is set to the 'ID' column.

5fill in blank

hard

Fill all three blanks to create a DataFrame from data, name columns, and select rows where 'Age' is greater than 25.

Pandas

import pandas as pd
data = [[1, 'Anna', 22], [2, 'Ben', 30], [3, 'Cara', 27]]
df = pd.DataFrame(data, columns=[1])
adults = df[df[[2]] [3] 25]

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

A['ID', 'Name', 'Age']

B'Age'

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' selects wrong rows.

Not using a list for columns causes errors.

Using wrong column name in filter causes empty results.

Columns are named with a list. We filter rows where 'Age' is greater than 25 using > operator.

Practice

1. What does creating a DataFrame from a list of lists in pandas mainly help you do?

easy

A. Organize data into rows and columns for easy analysis

B. Run SQL queries directly on the list

C. Convert data into images automatically

D. Encrypt data for security

Solution

1. Step 1: Understand DataFrame purpose

   A DataFrame is a table-like structure with rows and columns to organize data.

2. Step 2: Connect list of lists to DataFrame

   A list of lists can be converted into this table format for easier data handling.

3. Final Answer:

   Organize data into rows and columns for easy analysis -> Option A

4. Quick Check:

   DataFrame = Table format [OK]

Hint: DataFrames = tables; list of lists = rows [OK]

Common Mistakes:

• Thinking DataFrame encrypts data
• Confusing DataFrame with image conversion
• Assuming list of lists runs SQL

2. Which of the following is the correct way to create a DataFrame from a list of lists data = [[1, 2], [3, 4]] with columns named 'A' and 'B'?

easy

A. pd.DataFrame(data, columns=['A', 'B'])

B. pd.DataFrame(data, names=['A', 'B'])

C. pd.DataFrame(data, cols=['A', 'B'])

D. pd.DataFrame(data, header=['A', 'B'])

Solution

1. Step 1: Recall DataFrame column naming syntax

   Use the parameter columns to name columns when creating a DataFrame.

2. Step 2: Check each option

   Only pd.DataFrame(data, columns=['A', 'B']) uses columns=['A', 'B'], which is correct syntax.

3. Final Answer:

   pd.DataFrame(data, columns=['A', 'B']) -> Option A

4. Quick Check:

   Use columns= for column names [OK]

Hint: Use columns= to name DataFrame columns [OK]

Common Mistakes:

• Using names= instead of columns=
• Using cols= or header= which are invalid here
• Forgetting to pass columns parameter

3. What will be the output of the following code?

import pandas as pd
data = [[10, 20], [30, 40]]
df = pd.DataFrame(data, columns=['X', 'Y'])
print(df)

medium

A. X Y
0 20 10
1 40 30

B. X Y
0 10 20
1 30 40

C. 0 1
X 10 20
Y 30 40

D. Error: columns parameter missing

Solution

1. Step 1: Create DataFrame with given data and columns

   The list of lists becomes rows; columns=['X', 'Y'] names columns accordingly.

2. Step 2: Print DataFrame

   Output shows index 0 and 1 with values 10,20 and 30,40 under columns X and Y.

3. Final Answer:

   X Y 0 10 20 1 30 40 -> Option B

4. Quick Check:

   DataFrame prints rows with columns X, Y [OK]

Hint: Rows = inner lists; columns = names list [OK]

Common Mistakes:

• Swapping row and column values
• Confusing index with columns
• Expecting error without columns parameter

4. Identify the error in this code snippet:

import pandas as pd
data = [[1, 2], [3, 4]]
df = pd.DataFrame(data, columns=['A'])
print(df)

medium

A. DataFrame cannot be created from list of lists

B. Data should be a dictionary, not list of lists

C. Missing import statement for pandas

D. The columns list length does not match data inner list length

Solution

1. Step 1: Check data shape and columns length

   Each inner list has 2 elements, but columns list has only 1 name.

2. Step 2: Understand pandas error

   pandas requires columns length to match number of elements per row.

3. Final Answer:

   The columns list length does not match data inner list length -> Option D

4. Quick Check:

   Columns count = data columns count [OK]

Hint: Columns count must match inner list length [OK]

Common Mistakes:

• Ignoring mismatch between columns and data
• Thinking list of lists is invalid input
• Forgetting to import pandas

5. You have sales data as a list of lists: sales = [[100, 200], [150, 250], [300, 400]] representing sales in two regions. How do you create a DataFrame with columns 'Region1' and 'Region2' and add a new column 'Total' that sums both regions for each row?

hard

A. df = pd.DataFrame(sales, columns=['Region1', 'Region2'])
df['Total'] = sales[0] + sales[1]
print(df)

B. df = pd.DataFrame(sales)
df['Total'] = df[0] + df[1]
print(df)

C. df = pd.DataFrame(sales, columns=['Region1', 'Region2'])
df['Total'] = df['Region1'] + df['Region2']
print(df)

D. df = pd.DataFrame(sales, columns=['Region1', 'Region2'])
df['Total'] = df.sum()
print(df)

Solution

1. Step 1: Create DataFrame with correct columns

   Use columns=['Region1', 'Region2'] to name columns properly.

2. Step 2: Add 'Total' column by summing the two region columns

   Use df['Total'] = df['Region1'] + df['Region2'] to sum row-wise.

3. Final Answer:

   df = pd.DataFrame(sales, columns=['Region1', 'Region2']) df['Total'] = df['Region1'] + df['Region2'] print(df) -> Option C

4. Quick Check:

   Sum columns row-wise for total [OK]

Hint: Sum columns with df['Total'] = df['A'] + df['B'] [OK]

Common Mistakes:

• Adding lists instead of DataFrame columns
• Using df.sum() without axis=1 for row sums
• Not naming columns when creating DataFrame