Creating DataFrame from list of dictionaries in Pandas - Practice Exercises

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this DataFrame creation?

Given the list of dictionaries, what will be the output DataFrame?

Pandas

import pandas as pd

data = [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}]
df = pd.DataFrame(data)
print(df)

A

   name  age
0  Alice  25.0
1    Bob  30.0

B

   name  age
0  Alice   25
1    Bob   30

C

   name  age
0  Alice   25
1    Bob  NaN

D

   name  age
0  Alice  NaN
1    Bob   30

💡 Hint

Each dictionary becomes a row; keys become columns.

The DataFrame is created with columns 'name' and 'age'. Both dictionaries have these keys, so values fill the rows without missing data.

❓ data_output

intermediate

How many rows and columns does this DataFrame have?

What is the shape (rows, columns) of the DataFrame created from this list?

Pandas

import pandas as pd

data = [{'x': 1, 'y': 2}, {'x': 3, 'z': 4}, {'y': 5, 'z': 6}]
df = pd.DataFrame(data)
print(df.shape)

A (3, 3)

B (3, 2)

C (2, 3)

D (2, 2)

💡 Hint

Count unique keys across all dictionaries for columns; count dictionaries for rows.

There are 3 dictionaries (rows) and 3 unique keys ('x', 'y', 'z') making 3 columns.

🔧 Debug

advanced

What error does this code raise?

Identify the error raised by this code snippet:

Pandas

import pandas as pd

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

A KeyError

B TypeError

C No error, prints DataFrame with NaN in column 'c'

D ValueError

💡 Hint

Check how pandas handles missing columns when specifying columns parameter.

Pandas creates the DataFrame with columns 'a' and 'c'. Since 'c' is missing in data, its values are NaN.

🚀 Application

advanced

Which option creates a DataFrame with only keys 'name' and 'score'?

Given this list of dictionaries, which code creates a DataFrame with only 'name' and 'score' columns?

Pandas

data = [{'name': 'Anna', 'score': 90, 'age': 20}, {'name': 'Ben', 'score': 85, 'age': 22}]

A All of the above

B pd.DataFrame(data).drop('age', axis=1)

C pd.DataFrame([{k: d[k] for k in ['name', 'score']} for d in data])

D pd.DataFrame(data, columns=['name', 'score'])

💡 Hint

Consider how each method selects or removes columns.

All options produce a DataFrame with only 'name' and 'score' columns.

🧠 Conceptual

expert

What happens when dictionaries have nested dictionaries as values?

If you create a DataFrame from a list of dictionaries where some values are nested dictionaries, what is the type of those nested values in the DataFrame?

Pandas

import pandas as pd

data = [{'id': 1, 'info': {'height': 170, 'weight': 65}}, {'id': 2, 'info': {'height': 180, 'weight': 75}}]
df = pd.DataFrame(data)
print(type(df.loc[0, 'info']))

A <class 'float'>

B <class 'str'>

C <class 'pandas.Series'>

D <class 'dict'>

💡 Hint

Think about how pandas stores complex objects inside cells.

Pandas stores nested dictionaries as objects in cells, so the type remains dict.

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