Why Pandas for data analysis - Test Your Understanding

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'

Apandas

Bseaborn

Cmatplotlib

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing numpy instead of pandas

Using a wrong alias like 'np' for pandas

We use import pandas as pd to import pandas with the alias pd. This is the standard way to use pandas in Python.

2fill in blank

medium

Complete the code to create a DataFrame from a dictionary.

Pandas

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

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

ADataFrame

BSeries

Carray

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using Series instead of DataFrame

Trying to create a list or array instead

Use pd.DataFrame() to create a DataFrame from a dictionary. This structure is like a table with rows and columns.

3fill in blank

hard

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

Pandas

df.[1](3)

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

Ashow

Bprint

Ctail

Dhead

Attempts:

3 left

💡 Hint

Common Mistakes

Using tail() instead of head()

Trying to use print() as a method on DataFrame

The head() method shows the first rows of a DataFrame. Here, df.head(3) shows the first 3 rows.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Pandas

{word: [1] for word in words if [2] > 3}

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

Alen(word)

Bword

Clen(words)

Dword.length

Attempts:

3 left

💡 Hint

Common Mistakes

Using length of the whole list instead of each word

Using an attribute 'length' which does not exist for strings

We use len(word) to get the length of each word. The condition checks if the length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary from data items where values are positive.

Pandas

result = [1]: [2] for [1] , [2] in data.items() if [2] [3] 0}

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

Ak

Bv

C>

Dk.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same variable name for key and value

Using a wrong comparison operator

We use k and v as variable names for keys and values. The condition v > 0 filters positive values.

Practice

1. Why is pandas a popular tool for data analysis?

easy

A. It only works with images and videos.

B. It makes working with tables of data easy and fast.

C. It is mainly used for creating websites.

D. It replaces all programming languages.

Solution

1. Step 1: Understand pandas purpose

   Pandas is designed to handle and analyze data tables efficiently.

2. Step 2: Compare options with pandas use

   Options A, B, and D describe unrelated uses or exaggerations, so they are incorrect.

3. Final Answer:

   It makes working with tables of data easy and fast. -> Option B

4. Quick Check:

   Data tables + easy use = C [OK]

Hint: Pandas is for easy and fast table data handling [OK]

Common Mistakes:

• Thinking pandas is for web development
• Confusing pandas with image tools
• Believing pandas replaces all coding

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

easy

A. import pd as pandas

B. import pandas from pd

C. from pandas import pd

D. import pandas as pd

Solution

1. Step 1: Recall pandas import syntax

   The standard way is to import pandas with alias pd using 'import pandas as pd'.

2. Step 2: Check other options for syntax errors

   Options A, B, and C have incorrect syntax or order and will cause errors.

3. Final Answer:

   import pandas as pd -> Option D

4. Quick Check:

   Standard import = D [OK]

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

Common Mistakes:

• Using wrong import order
• Confusing 'from' and 'import' keywords
• Swapping alias and module names

3. What will the following code output?

import pandas as pd
data = {'Name': ['Anna', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
print(df.shape)

medium

A. (2, 2)

B. (2, 1)

C. (1, 2)

D. (3, 2)

Solution

1. Step 1: Understand DataFrame creation

   The data dictionary has 2 keys (columns) and 2 values each (rows), so DataFrame has 2 rows and 2 columns.

2. Step 2: Interpret df.shape output

   df.shape returns a tuple (rows, columns), so it will be (2, 2).

3. Final Answer:

   (2, 2) -> Option A

4. Quick Check:

   Rows=2, Columns=2 = B [OK]

Hint: DataFrame shape = (rows, columns) from data dict size [OK]

Common Mistakes:

• Confusing rows and columns order
• Counting keys as rows
• Misreading shape output as list

4. Find the error in this code snippet:

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

medium

A. TypeError because data is not a list

B. SyntaxError due to missing comma

C. KeyError because column 'C' does not exist

D. No error, prints column 'C'

Solution

1. Step 1: Check DataFrame columns

   DataFrame has columns 'A' and 'B' only, no 'C'.

2. Step 2: Accessing missing column

   Trying to print df['C'] causes a KeyError because 'C' is not found.

3. Final Answer:

   KeyError because column 'C' does not exist -> Option C

4. Quick Check:

   Missing column access = KeyError = A [OK]

Hint: Access only existing columns to avoid KeyError [OK]

Common Mistakes:

• Assuming missing columns print empty
• Confusing KeyError with SyntaxError
• Ignoring error messages

5. You have a CSV file with sales data. Which pandas feature helps you quickly load and analyze this data?

medium

A. pd.read_csv() to load data and DataFrame methods to analyze

B. pd.plot() to create charts directly from CSV

C. pd.save_excel() to convert CSV to Excel

D. pd.clean_data() to fix all errors automatically

Solution

1. Step 1: Identify pandas function for CSV loading

   pd.read_csv() is the standard function to load CSV files into DataFrames.

2. Step 2: Use DataFrame methods for analysis

   Once loaded, DataFrame methods help explore and analyze data quickly.

3. Final Answer:

   pd.read_csv() to load data and DataFrame methods to analyze -> Option A

4. Quick Check:

   Load CSV + analyze = pd.read_csv + DataFrame [OK]

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

Common Mistakes:

• Thinking pd.plot() loads data
• Believing pandas auto-fixes data errors
• Confusing saving and loading functions