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.