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

Creating DataFrame from list of dictionaries

📖 Scenario: You work in a small bookstore. You have a list of books with their details stored as dictionaries. You want to organize this data into a table to analyze it easily.

🎯 Goal: Create a pandas DataFrame from a list of dictionaries representing books. Then display the DataFrame.

📋 What You'll Learn

Create a list of dictionaries called books with exact entries

Create a pandas DataFrame called df from the books list

Print the DataFrame df to show the table

💡 Why This Matters

🌍 Real World

Organizing data from lists of dictionaries into tables is common in data analysis, such as managing inventory or customer data.

💼 Career

Data scientists and analysts often convert raw data into DataFrames to clean, analyze, and visualize information efficiently.

1

Create the list of dictionaries

Create a list called books with these exact dictionaries:
{'title': 'The Alchemist', 'author': 'Paulo Coelho', 'year': 1988},
{'title': '1984', 'author': 'George Orwell', 'year': 1949},
{'title': 'To Kill a Mockingbird', 'author': 'Harper Lee', 'year': 1960}

Pandas

# Create the list of dictionaries called books
# Your code here

Hint

Use square brackets [] to create a list. Each book is a dictionary inside the list.

2

Import pandas and create DataFrame

Import the pandas library as pd. Then create a DataFrame called df from the list books using pd.DataFrame(books).

Pandas

books = [
    {'title': 'The Alchemist', 'author': 'Paulo Coelho', 'year': 1988},
    {'title': '1984', 'author': 'George Orwell', 'year': 1949},
    {'title': 'To Kill a Mockingbird', 'author': 'Harper Lee', 'year': 1960}
]
# Import pandas as pd and create DataFrame df from books
# Your code here

Hint

Use import pandas as pd to import pandas. Then use pd.DataFrame() to create the table.

3

Print the DataFrame

Use print(df) to display the DataFrame df.

Pandas

import pandas as pd

books = [
    {'title': 'The Alchemist', 'author': 'Paulo Coelho', 'year': 1988},
    {'title': '1984', 'author': 'George Orwell', 'year': 1949},
    {'title': 'To Kill a Mockingbird', 'author': 'Harper Lee', 'year': 1960}
]

df = pd.DataFrame(books)
# Print the DataFrame df
# Your code here

Hint

Use print(df) to see the table of books.

4

Add a filter for books after 1950

Create a new DataFrame called recent_books that contains only the rows from df where the year is greater than 1950. Use df[df['year'] > 1950].

Pandas

import pandas as pd

books = [
    {'title': 'The Alchemist', 'author': 'Paulo Coelho', 'year': 1988},
    {'title': '1984', 'author': 'George Orwell', 'year': 1949},
    {'title': 'To Kill a Mockingbird', 'author': 'Harper Lee', 'year': 1960}
]

df = pd.DataFrame(books)

print(df)
# Create recent_books DataFrame with books where year > 1950
# Your code here

Hint

Use df[df['year'] > 1950] to filter rows where year is greater than 1950.

Practice

1. What happens when you create a pandas DataFrame from a list of dictionaries?

easy

A. Each dictionary becomes a row, and keys become column names.

B. Each dictionary becomes a column, and keys become row labels.

C. The list is converted into a single column with dictionary objects.

D. The DataFrame will be empty because dictionaries cannot be converted.

Solution

1. Step 1: Understand DataFrame creation from list of dicts

   Each dictionary in the list is treated as one row in the DataFrame.

2. Step 2: Identify how keys are used

   Keys from dictionaries become the column names in the DataFrame.

3. Final Answer:

   Each dictionary becomes a row, and keys become column names. -> Option A

4. Quick Check:

   List of dicts -> rows, keys -> columns [OK]

Hint: Remember: dict keys become columns, dicts become rows [OK]

Common Mistakes:

• Thinking dictionaries become columns instead of rows
• Assuming keys become row labels
• Believing the DataFrame will be empty

2. Which of the following is the correct syntax to create a DataFrame from a list of dictionaries named data?

easy

A. df = pd.DataFrame(data)

B. df = pd.DataFrame.from_list(data)

C. df = pd.DataFrame.create(data)

D. df = pd.DataFrame.list(data)

Solution

1. Step 1: Recall pandas DataFrame constructor

   The correct way to create a DataFrame from data is using pd.DataFrame().

2. Step 2: Check method names

   There is no from_list, create, or list method in pandas DataFrame.

3. Final Answer:

   df = pd.DataFrame(data) -> Option A

4. Quick Check:

   Use pd.DataFrame() constructor [OK]

Hint: Use pd.DataFrame(your_list) to create DataFrame [OK]

Common Mistakes:

• Using non-existent methods like from_list or create
• Confusing DataFrame constructor syntax
• Missing pd prefix or parentheses

3. Given the code:

import pandas as pd
data = [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'city': 'NY'}]
df = pd.DataFrame(data)
print(df)

What will be the output?

medium

A. A DataFrame with columns 'name' and 'city' only.

B. A DataFrame with only columns 'name' and 'age', ignoring 'city'.

C. An error because dictionaries have different keys.

D. A DataFrame with columns 'name', 'age', 'city' and NaN where data is missing.

Solution

1. Step 1: Check keys across dictionaries

   Keys are 'name', 'age' in first dict and 'name', 'city' in second dict.

2. Step 2: Understand pandas behavior with missing keys

   Pandas creates columns for all keys found and fills missing values with NaN.

3. Final Answer:

   DataFrame with columns 'name', 'age', 'city' and NaN where data is missing. -> Option D

4. Quick Check:

   Missing keys -> NaN in DataFrame [OK]

Hint: Missing keys become NaN in DataFrame columns [OK]

Common Mistakes:

• Assuming missing keys cause errors
• Thinking columns are limited to first dict keys
• Ignoring NaN values for missing data

4. What is wrong with this code snippet?

import pandas as pd
data = [{'a': 1, 'b': 2}, {'a': 3, 'c': 4}]
df = pd.DataFrame(data, columns=['a', 'b', 'c', 'd'])
print(df)

medium

A. DataFrame will only include columns 'a' and 'b'.

B. No error; columns not in data will be filled with NaN.

C. Error because columns list length does not match dictionaries.

D. Error because column 'd' does not exist in any dictionary.

Solution

1. Step 1: Understand columns parameter effect

   Specifying columns includes those columns in DataFrame, even if missing in data.

2. Step 2: Check missing columns behavior

   Columns not found in dictionaries are filled with NaN, no error occurs.

3. Final Answer:

   No error; columns not in data will be filled with NaN. -> Option B

4. Quick Check:

   Extra columns -> NaN, no error [OK]

Hint: Extra columns in list become NaN, no error [OK]

Common Mistakes:

• Expecting error for missing columns
• Thinking columns list must match keys exactly
• Assuming DataFrame excludes columns not in data

5. You have a list of dictionaries representing sales data:

sales = [
  {'product': 'A', 'units': 10, 'price': 2.5},
  {'product': 'B', 'units': 5},
  {'product': 'A', 'price': 2.5},
  {'product': 'C', 'units': 7, 'price': 3.0}
]

You want to create a DataFrame and add a new column 'total' which is units * price, treating missing values as zero. Which code correctly does this?

hard

A. df = pd.DataFrame(sales)
df['total'] = df['units'].fillna(0) + df['price'].fillna(0)
print(df)

B. df = pd.DataFrame(sales)
df['total'] = df['units'] * df['price']
print(df)

C. df = pd.DataFrame(sales)
df['units'] = df['units'].fillna(0)
df['price'] = df['price'].fillna(0)
df['total'] = df['units'] * df['price']
print(df)

D. df = pd.DataFrame(sales)
df['total'] = df['units'] * df['price']
df.fillna(0)
print(df)

Solution

1. Step 1: Create DataFrame from list of dicts

   Use pd.DataFrame(sales) to convert list to DataFrame with NaNs for missing keys.

2. Step 2: Replace NaN with 0 before multiplication

   Fill missing 'units' and 'price' with 0 to avoid NaN in multiplication.

3. Step 3: Calculate 'total' as product of 'units' and 'price'

   Multiply filled columns to get correct totals.

4. Final Answer:

   Fill NaNs first, then multiply -> Option C

5. Quick Check:

   Fill NaN before math to avoid NaN results [OK]

Hint: Fill missing values with 0 before multiplying columns [OK]

Common Mistakes:

• Multiplying columns with NaN without filling
• Adding instead of multiplying for total
• Filling NaN after multiplication