Creating DataFrame from list of lists in Pandas - Try It Yourself

Creating DataFrame from list of lists

📖 Scenario: You work in a small store and have a list of daily sales data. Each entry has the product name, quantity sold, and price per item.

🎯 Goal: You will create a pandas DataFrame from this list of lists to organize the sales data in a table format.

📋 What You'll Learn

Use the pandas library

Create a DataFrame from a list of lists

Assign column names to the DataFrame

Print the DataFrame to see the organized data

💡 Why This Matters

🌍 Real World

Stores and businesses often collect sales data in lists or spreadsheets. Organizing this data into tables helps analyze sales and make decisions.

💼 Career

Data scientists and analysts use pandas DataFrames to clean, organize, and analyze data from many sources.

1

Create the sales data list

Create a variable called sales_data that is a list of lists with these exact entries: ["Apple", 10, 0.5], ["Banana", 5, 0.3], and ["Orange", 8, 0.7].

Pandas

# Create the sales_data list with the exact entries
# Your code here

Hint

Use square brackets to create a list of lists. Each inner list has three elements: product name, quantity, and price.

2

Import pandas and set column names

Import the pandas library as pd. Then create a list called columns with these exact strings: "Product", "Quantity", and "Price".

Pandas

sales_data = [["Apple", 10, 0.5], ["Banana", 5, 0.3], ["Orange", 8, 0.7]]
# Import pandas as pd
# Create the columns list with the exact names
# Your code here

Hint

Use import pandas as pd to import pandas. Create a list with the column names exactly as shown.

3

Create the DataFrame from the list

Create a pandas DataFrame called df from the sales_data list using the columns list for column names.

Pandas

import pandas as pd

sales_data = [["Apple", 10, 0.5], ["Banana", 5, 0.3], ["Orange", 8, 0.7]]
columns = ["Product", "Quantity", "Price"]
# Create the DataFrame df from sales_data with columns
# Your code here

Hint

Use pd.DataFrame() with the data and columns arguments.

4

Print the DataFrame

Print the DataFrame df to display the organized sales data.

Pandas

import pandas as pd

sales_data = [["Apple", 10, 0.5], ["Banana", 5, 0.3], ["Orange", 8, 0.7]]
columns = ["Product", "Quantity", "Price"]

df = pd.DataFrame(sales_data, columns=columns)
# Print the DataFrame df
# Your code here

Hint

Use print(df) to show the DataFrame.

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