Numeric types (int64, float64) in Pandas - Mini Project: Build & Apply

Working with Numeric Types in pandas

📖 Scenario: Imagine you are analyzing sales data for a small store. The data includes the number of items sold and the total sales amount. You want to make sure the numbers are stored correctly to do calculations later.

🎯 Goal: You will create a pandas DataFrame with sales data, check and set the numeric types for the columns to int64 and float64, and then display the data types to confirm.

📋 What You'll Learn

Create a pandas DataFrame with specific sales data

Check and set the data types of columns to int64 and float64

Print the data types of the DataFrame columns

💡 Why This Matters

🌍 Real World

Data scientists often need to ensure numeric data is stored in the correct format for calculations, memory efficiency, and compatibility with analysis tools.

💼 Career

Understanding and managing numeric types is essential for data cleaning, preprocessing, and preparing data for machine learning or reporting.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with two columns: 'items_sold' and 'total_sales'. Use these exact values: items_sold as [10, 15, 7, 20], and total_sales as [250.0, 375.5, 175.75, 500.0].

Pandas

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

Hint

Use pd.DataFrame with a dictionary containing the two columns and their values.

2

Check the current data types

Create a variable called current_types that stores the data types of the columns in sales_data using the dtypes attribute.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'items_sold': [10, 15, 7, 20],
    'total_sales': [250.0, 375.5, 175.75, 500.0]
})
# Store the data types of sales_data columns in current_types
# Your code here

Hint

Use sales_data.dtypes to get the data types of each column.

3

Set the correct numeric types for columns

Use the astype method to convert the 'items_sold' column to int64 and the 'total_sales' column to float64. Update the sales_data DataFrame with these new types.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'items_sold': [10, 15, 7, 20],
    'total_sales': [250.0, 375.5, 175.75, 500.0]
})
current_types = sales_data.dtypes
# Convert 'items_sold' to int64 and 'total_sales' to float64
# Your code here

Hint

Use sales_data.astype({'items_sold': 'int64', 'total_sales': 'float64'}) to set the types.

4

Print the data types to confirm

Print the data types of the sales_data DataFrame columns using the dtypes attribute.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'items_sold': [10, 15, 7, 20],
    'total_sales': [250.0, 375.5, 175.75, 500.0]
})
current_types = sales_data.dtypes
sales_data = sales_data.astype({'items_sold': 'int64', 'total_sales': 'float64'})
# Print the data types of sales_data columns
# Your code here

Hint

Use print(sales_data.dtypes) to show the column types.

Practice

1. Which pandas numeric type is best for storing whole numbers without decimals?

easy

A. bool

B. float64

C. int64

D. object

Solution

1. Step 1: Understand numeric types in pandas

   int64 is used for integers (whole numbers), float64 for decimals.

2. Step 2: Identify the type for whole numbers

   Whole numbers require int64 because float64 stores decimals.

3. Final Answer:

   int64 -> Option C

4. Quick Check:

   Whole numbers = int64 [OK]

Hint: Whole numbers use int64, decimals use float64 [OK]

Common Mistakes:

• Confusing float64 as default for all numbers
• Choosing object type for numeric data
• Using bool for numeric storage

2. Which of the following is the correct way to convert a pandas DataFrame column df['col'] to float64 type?

easy

A. df.astype('float64')['col']

B. df['col'].convert('float64')

C. astype(df['col'], 'float64')

D. df['col'].astype('float64')

Solution

1. Step 1: Recall pandas method to change column type

   The method to change a column's type is astype() called on the column.

2. Step 2: Apply correct syntax

   Use df['col'].astype('float64') to convert the column to float64.

3. Final Answer:

   df['col'].astype('float64') -> Option D

4. Quick Check:

   astype() on column = correct syntax [OK]

Hint: Use astype() on the column to change type [OK]

Common Mistakes:

• Calling astype() on the whole DataFrame incorrectly
• Using non-existent convert() method
• Wrong function syntax like astype(df['col'], 'float64')

3. Given the code:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.0, 2.5, 3.5]})
df['A'] = df['A'].astype('float64')
print(df.dtypes)

What will be the output?

medium

A. A float64
B float64
dtype: object

B. A int64
B float64
dtype: object

C. A object
B float64
dtype: object

D. A float64
B int64
dtype: object

Solution

1. Step 1: Check initial dtypes

   Column 'A' starts as int64, 'B' as float64.

2. Step 2: Convert 'A' to float64

   Using astype('float64') changes 'A' from int64 to float64.

3. Final Answer:

   A float64 B float64 dtype: object -> Option A

4. Quick Check:

   astype changes int64 to float64 [OK]

Hint: astype changes column type; check dtypes after conversion [OK]

Common Mistakes:

• Assuming original types remain unchanged
• Confusing int64 and float64 outputs
• Misreading print output format

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'num': [1, 2, 3]})
df['num'] = df['num'].astype(int64)

medium

A. int64 is not defined without quotes

B. astype() cannot convert to int64

C. DataFrame column cannot be reassigned

D. Missing import for astype

Solution

1. Step 1: Identify the error in astype argument

   int64 must be passed as a string 'int64', not as a bare name.

2. Step 2: Understand Python name error

   Without quotes, Python looks for a variable named int64 which is undefined.

3. Final Answer:

   int64 is not defined without quotes -> Option A

4. Quick Check:

   Use quotes around type names in astype() [OK]

Hint: Always quote type names in astype() [OK]

Common Mistakes:

• Passing type names without quotes
• Thinking astype() needs import
• Believing columns cannot be reassigned

5. You have a DataFrame df with a column 'values' containing numbers as strings, e.g., ['1', '2.5', '3']. You want to convert this column to numeric types efficiently, storing integers as int64 and decimals as float64 where needed. Which approach is best?

hard

A. Use pd.to_numeric(df['values'], downcast='integer')

B. Use pd.to_numeric(df['values'], errors='coerce') and then convert integers to int64

C. Use df['values'] = df['values'].astype('float64') for all values

D. Manually check each value and convert to int64 or float64

Solution

1. Step 1: Understand mixed numeric strings

   Strings include integers and decimals, so direct astype('float64') converts all to float.

2. Step 2: Use pd.to_numeric with errors='coerce'

   This converts strings to numbers, coercing errors to NaN, allowing safe conversion.

3. Step 3: Convert integers to int64 after identifying them

   After conversion, integers can be cast to int64 to save memory.

4. Final Answer:

   Use pd.to_numeric(df['values'], errors='coerce') and then convert integers to int64 -> Option B

5. Quick Check:

   pd.to_numeric + careful conversion handles mixed types best [OK]

Hint: Use pd.to_numeric with errors='coerce' for mixed numeric strings [OK]

Common Mistakes:

• Converting all to float64 losing integer type
• Trying manual conversion for each value
• Ignoring errors in conversion causing crashes