What is Pandas - Interactive Quiz & Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the pandas library with the common alias.

Pandas

import [1] as pd

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Anumpy

Bpandas

Cmatplotlib

Dseaborn

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'numpy' instead of 'pandas'.

Forgetting to use the alias 'pd'.

The pandas library is imported using the name 'pandas'. The common alias is 'pd'.

2fill in blank

medium

Complete the code to create a pandas DataFrame from a dictionary.

Pandas

import pandas as pd

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

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Alist

Barray

CSeries

DDataFrame

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Series' which creates a one-dimensional array.

Using 'array' or 'list' which are not pandas objects.

Use pd.DataFrame() to create a DataFrame from a dictionary.

3fill in blank

hard

Fix the error in the code to display the first 3 rows of the DataFrame.

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie', 'David'], 'age': [25, 30, 35, 40]}
df = pd.DataFrame(data)
print(df.[1](3))

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Ahead

Btail

Cfirst

Dshow

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'tail' which shows the last rows.

Using 'first' or 'show' which are not pandas methods.

The head() method shows the first rows of a DataFrame. Use head(3) to show the first 3 rows.

4fill in blank

hard

Fill both blanks to create a DataFrame and select the 'age' column.

Pandas

import pandas as pd

data = {'name': ['Anna', 'Ben'], 'age': [22, 28]}
df = pd.[1](data)
age_column = df[2]

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ADataFrame

BSeries

C['age']

D.age

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Series' to create the DataFrame.

Using dot notation to select the column which may fail if column name has spaces.

Create a DataFrame with pd.DataFrame() and select the 'age' column using df['age'].

5fill in blank

hard

Fill all three blanks to filter rows where age is greater than 25 and create a new DataFrame.

Pandas

import pandas as pd

data = {'name': ['Tom', 'Jerry', 'Mickey'], 'age': [20, 27, 30]}
df = pd.DataFrame(data)
adults = df[df['age'] [1] [2]]
result = adults.[3]()

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A>

B25

Ccopy

Dhead

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>'.

Forgetting to use copy() which can cause warnings.

Using head() which returns only first rows.

Use df['age'] > 25 to filter rows where age is greater than 25. Then use copy() to create a new DataFrame.