Creating DataFrame from list of dictionaries in Pandas - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

import pandas as pd
data = [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}]
df = pd.[1](data)
print(df)

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

Aconcat

BDataFrame

CSeries

Dread_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using pd.Series instead of pd.DataFrame

Trying to read data with pd.read_csv when data is already in memory

The DataFrame constructor creates a DataFrame from a list of dictionaries.

2fill in blank

medium

Complete the code to print the first two rows of the DataFrame df.

Pandas

import pandas as pd
data = [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}, {'name': 'Cara', 'age': 22}]
df = pd.DataFrame(data)
print(df.[1](2))

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

Asample

Btail

Chead

Ddescribe

Attempts:

3 left

💡 Hint

Common Mistakes

Using tail() which shows the last rows

Using describe() which summarizes statistics

The head() method shows the first rows of a DataFrame. Passing 2 shows the first two rows.

3fill in blank

hard

Fix the error in the code to create a DataFrame from data.

Pandas

import pandas as pd
data = [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}]
df = pd.DataFrame[1]data
print(df)

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

A(data)

B[data]

Cdata

D{data}

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting parentheses after DataFrame

Using square or curly brackets instead of parentheses

The DataFrame constructor requires parentheses around the data argument.

4fill in blank

hard

Fill both blanks to create a DataFrame from data and select only the 'name' column.

Pandas

import pandas as pd
data = [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}]
df = pd.[1](data)
names = df[[2]]
print(names)

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

ADataFrame

B'age'

C'name'

DSeries

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting the wrong column name

Using Series instead of DataFrame to create the table

Use DataFrame to create the table and select the 'name' column by its string key.

5fill in blank

hard

Fill all three blanks to create a DataFrame from data, filter rows where age is greater than 25, and print the result.

Pandas

import pandas as pd
data = [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}, {'name': 'Cara', 'age': 22}]
df = pd.[1](data)
filtered = df[df[[2]] [3] 25]
print(filtered)

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

ADataFrame

B'age'

C>

D'name'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong column name for filtering

Using wrong comparison operator

Forgetting to create the DataFrame first

Create the DataFrame, then filter rows where the 'age' column is greater than 25 using the > operator.

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