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

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

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)

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

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))

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

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]

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

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]()

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

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.

Practice

1. What is pandas mainly used for in Python?

easy

A. Writing system drivers

B. Creating 3D graphics

C. Building websites

D. Working with tables of data easily

Solution

1. Step 1: Understand pandas purpose

   Pandas is a Python library designed to handle and analyze data in tables.

2. Step 2: Match purpose with options

   Only Working with tables of data easily describes working with tables of data, which matches pandas' main use.

3. Final Answer:

   Working with tables of data easily -> Option D

4. Quick Check:

   pandas = tables of data [OK]

Hint: Remember pandas = data tables in Python [OK]

Common Mistakes:

• Confusing pandas with graphics or web tools
• Thinking pandas is for system programming

2. Which of these is the correct way to import pandas in Python?

easy

A. import pandas as pn

B. import pandas as pd

C. from pandas import table

D. import panda as pd

Solution

1. Step 1: Recall pandas import convention

   The common and correct way is to import pandas as pd for short.

2. Step 2: Check each option

   import pandas as pd matches the standard import. Others have wrong names or syntax.

3. Final Answer:

   import pandas as pd -> Option B

4. Quick Check:

   Standard import = import pandas as pd [OK]

Hint: Use 'import pandas as pd' always [OK]

Common Mistakes:

• Misspelling 'pandas' as 'panda'
• Using wrong alias like 'pn'
• Trying to import non-existent modules

3. What will this code print?

import pandas as pd
data = {'A': [1, 2], 'B': [3, 4]}
df = pd.DataFrame(data)
print(df)

medium

A. A table with columns A and B showing values 1,2 and 3,4

B. A list of dictionaries

C. An error because DataFrame is not defined

D. Empty output

Solution

1. Step 1: Create DataFrame from dictionary

   The dictionary has keys 'A' and 'B' with lists as values. pd.DataFrame converts this into a table with columns A and B.

2. Step 2: Print DataFrame

   Printing df shows a table with two rows: first row 1 and 3, second row 2 and 4 under columns A and B.

3. Final Answer:

   A table with columns A and B showing values 1,2 and 3,4 -> Option A

4. Quick Check:

   DataFrame prints table = A table with columns A and B showing values 1,2 and 3,4 [OK]

Hint: DataFrame from dict shows columns and rows [OK]

Common Mistakes:

• Thinking it prints a list or dictionary
• Expecting an error without importing pandas
• Assuming empty output

4. Find the error in this code:

import pandas as pd
data = {'X': [5, 6], 'Y': [7, 8]}
df = pd.Dataframe(data)
print(df)

medium

A. Typo in DataFrame capitalization

B. Missing import statement

C. Dictionary keys must be strings

D. print() function is incorrect

Solution

1. Step 1: Check pandas DataFrame spelling

   The correct class name is DataFrame with a capital 'F'. The code uses Dataframe which causes an error.

2. Step 2: Verify other parts

   Import is correct, dictionary keys are strings, and print() is used properly.

3. Final Answer:

   Typo in DataFrame capitalization -> Option A

4. Quick Check:

   Class name is DataFrame, not Dataframe [OK]

Hint: Check capitalization of DataFrame class [OK]

Common Mistakes:

• Writing Dataframe instead of DataFrame
• Ignoring case sensitivity in Python
• Assuming print() causes error

5. You have a CSV file with sales data. Which pandas function helps you load this file into a DataFrame for analysis?

hard

A. pd.load_file()

B. pd.open_csv()

C. pd.read_csv()

D. pd.import_csv()

Solution

1. Step 1: Identify pandas function for CSV files

   Pandas provides read_csv() to load CSV files into DataFrames easily.

2. Step 2: Check options for correctness

   Only pd.read_csv() is a valid pandas function. Others do not exist.

3. Final Answer:

   pd.read_csv() -> Option C

4. Quick Check:

   CSV load = pd.read_csv() [OK]

Hint: Use pd.read_csv() to load CSV files [OK]

Common Mistakes:

• Using non-existent pandas functions
• Confusing with file open methods
• Trying to load CSV without pandas