Selecting columns in Data Analysis Python - Mini Project: Build & Apply

Selecting columns

📖 Scenario: You work in a small shop that keeps track of sales data in a table. You want to look at only some parts of the data to understand it better.

🎯 Goal: You will create a table of sales data and then select only the columns for Product and Price to see just those details.

📋 What You'll Learn

Create a pandas DataFrame with specific sales data

Create a list of column names to select

Select only the specified columns from the DataFrame

Print the selected columns DataFrame

💡 Why This Matters

🌍 Real World

Selecting specific columns from data tables is common when you want to focus on certain details, like product names and prices in sales data.

💼 Career

Data analysts and scientists often select columns to prepare data for reports, visualizations, or further analysis.

1

Create the sales data table

Create a pandas DataFrame called sales_data with these exact columns and rows:
Product: 'Apple', 'Banana', 'Carrot'
Price: 0.5, 0.3, 0.2
Quantity: 10, 20, 15

Data Analysis Python

import pandas as pd
# Create the sales_data DataFrame with the given data
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of column values.

2

Create a list of columns to select

Create a list called columns_to_select that contains the exact column names 'Product' and 'Price'.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': [0.5, 0.3, 0.2],
    'Quantity': [10, 20, 15]
})
# Create the list columns_to_select with 'Product' and 'Price'
# Your code here

Hint

Make a list with the two column names as strings.

3

Select the columns from the DataFrame

Create a new DataFrame called selected_data by selecting only the columns in columns_to_select from sales_data.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': [0.5, 0.3, 0.2],
    'Quantity': [10, 20, 15]
})
columns_to_select = ['Product', 'Price']
# Select the columns from sales_data and assign to selected_data
# Your code here

Hint

Use bracket notation with the list of columns to select from the DataFrame.

4

Print the selected columns

Print the selected_data DataFrame to see only the Product and Price columns.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': [0.5, 0.3, 0.2],
    'Quantity': [10, 20, 15]
})
columns_to_select = ['Product', 'Price']
selected_data = sales_data[columns_to_select]
# Print the selected_data DataFrame
# Your code here

Hint

Use print(selected_data) to show the selected columns.

Practice

1. Which of the following is the correct way to select a single column named 'Age' from a DataFrame df?

easy

A. df[['Age']]

B. df.select('Age')

C. df.Age()

D. df['Age']

Solution

1. Step 1: Understand single column selection syntax

   To select one column from a DataFrame, use df['column_name'].

2. Step 2: Check each option

   df['Age'] uses df['Age'], which is correct. df[['Age']] returns a DataFrame, not a Series. df.Age() uses parentheses incorrectly. df.select('Age') is not a valid pandas method.

3. Final Answer:

   df['Age'] -> Option D

4. Quick Check:

   Single column selection = df['Age'] [OK]

Hint: Use single brackets for one column, double for multiple [OK]

Common Mistakes:

• Using double brackets for single column returns DataFrame, not Series
• Using dot notation with parentheses like a function
• Trying non-existent methods like select()

2. Which syntax correctly selects multiple columns 'Name' and 'Salary' from DataFrame df?

easy

A. df['Name', 'Salary']

B. df[['Name', 'Salary']]

C. df.Name, df.Salary

D. df.select(['Name', 'Salary'])

Solution

1. Step 1: Recall syntax for selecting multiple columns

   Use double brackets with a list of column names: df[['col1', 'col2']].

2. Step 2: Evaluate options

   df[['Name', 'Salary']] matches the correct syntax. df['Name', 'Salary'] is invalid syntax. df.Name, df.Salary selects columns separately, not together. df.select(['Name', 'Salary']) is not a pandas method.

3. Final Answer:

   df[['Name', 'Salary']] -> Option B

4. Quick Check:

   Multiple columns = df[['Name', 'Salary']] [OK]

Hint: Use double brackets with list for multiple columns [OK]

Common Mistakes:

• Using single brackets with multiple column names separated by commas
• Trying to select multiple columns with dot notation
• Using non-existent select() method

3. Given the DataFrame df below:

   Name  Age  Salary
0  John   28   50000
1  Anna   22   60000
2  Mike   32   55000

What is the output of df[['Name', 'Age']]?

medium

A. Name Age
0 John 28
1 Anna 22
2 Mike 32

B. Name Age Salary
0 John 28 50000
1 Anna 22 60000
2 Mike 32 55000

C. 0 John
1 Anna
2 Mike
Name: Name, dtype: object

D. 28
22
32

Solution

1. Step 1: Understand what df[['Name', 'Age']] does

   This selects only the columns 'Name' and 'Age' from the DataFrame, returning a smaller DataFrame with those columns.

2. Step 2: Match output to options

      Name  Age
   0  John   28
   1  Anna   22
   2  Mike   32

   shows only 'Name' and 'Age' columns with all rows, which matches the expected output.

      Name  Age  Salary
   0  John   28   50000
   1  Anna   22   60000
   2  Mike   32   55000

   includes 'Salary' which is not selected.

   0    John
   1    Anna
   2    Mike
   Name: Name, dtype: object

   shows only the 'Name' column as a Series.

   28
   22
   32

   shows only ages as a list, which is incorrect.

3. Final Answer:

   DataFrame with columns 'Name' and 'Age' only -> Option A

4. Quick Check:

   Selected columns = 'Name' and 'Age' only [OK]

Hint: Double brackets return DataFrame with selected columns [OK]

Common Mistakes:

• Expecting single brackets to return multiple columns
• Confusing Series output with DataFrame output
• Including unselected columns in output

4. What is wrong with this code to select the column 'Score' from DataFrame df?

df.Score()

medium

A. Parentheses cause an error; dot notation should be without ()

B. Column names cannot have uppercase letters

C. Column name must be in double brackets

D. Dot notation cannot be used to select columns

Solution

1. Step 1: Understand dot notation for column selection

   Dot notation selects columns as attributes without parentheses, like df.Score.

2. Step 2: Identify error in code

   Using parentheses () after Score tries to call it like a function, causing an error.

3. Final Answer:

   Parentheses cause an error; dot notation should be without () -> Option A

4. Quick Check:

   Dot notation = df.Score without parentheses [OK]

Hint: Dot notation is attribute access, no parentheses [OK]

Common Mistakes:

• Adding parentheses after column name with dot notation
• Thinking dot notation works for all column names
• Using double brackets with dot notation

5. You have a DataFrame df with columns 'A', 'B', 'C', and 'D'. You want to create a new DataFrame df_new that contains only columns 'B' and 'D' but in reverse order ('D' first, then 'B'). Which code correctly does this?

hard

A. df_new = df['D', 'B']

B. df_new = df[['B', 'D']]

C. df_new = df[['D', 'B']]

D. df_new = df[['B', 'D']].reindex(columns=['B', 'D'])

Solution

1. Step 1: Understand column selection order

   The order of columns in the list inside double brackets determines the order in the new DataFrame.

2. Step 2: Check each option

   df_new = df[['D', 'B']] selects columns 'D' then 'B', which matches the requirement. df_new = df[['B', 'D']] selects 'B' then 'D', wrong order. df_new = df['D', 'B'] is invalid tuple syntax inside single brackets. df_new = df[['B', 'D']].reindex(columns=['B', 'D']) reindexes to the same order 'B' then 'D', incorrect.

3. Final Answer:

   df_new = df[['D', 'B']] -> Option C

4. Quick Check:

   Order columns by list inside double brackets [OK]

Hint: Order columns in list to set output order [OK]

Common Mistakes:

• Selecting columns in wrong order
• Using reindex but specifying the original order
• Using tuple inside single brackets for multiple columns