dtypes and data type checking in Pandas - Mini Project: Build & Apply

Understanding dtypes and Data Type Checking in pandas

📖 Scenario: You work in a small store and keep track of daily sales data. You want to make sure the data you have is in the right format before doing any analysis.

🎯 Goal: Learn how to create a pandas DataFrame, check the data types of each column, and understand how pandas stores data types.

📋 What You'll Learn

Create a pandas DataFrame with specific sales data

Check the data types of the DataFrame columns using dtypes

Use type() to check the data type of a specific column

Print the data types to see the results

💡 Why This Matters

🌍 Real World

Checking data types is important before analyzing data to avoid errors and understand the kind of data you have.

💼 Career

Data scientists and analysts often check and convert data types to prepare data for analysis and modeling.

1

Create the sales DataFrame

Import pandas as pd and create a DataFrame called sales with these exact columns and values: 'day': ['Monday', 'Tuesday', 'Wednesday'], 'items_sold': [10, 15, 7], 'revenue': [100.5, 150.75, 70.0]

Pandas

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

Need a hint?

Use pd.DataFrame and pass a dictionary with the exact keys and values.

2

Check the data types of all columns

Create a variable called data_types and set it to sales.dtypes to get the data types of each column.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'day': ['Monday', 'Tuesday', 'Wednesday'],
    'items_sold': [10, 15, 7],
    'revenue': [100.5, 150.75, 70.0]
})
# Get the data types of each column
# Your code here

Need a hint?

Use the dtypes attribute of the DataFrame to see each column's data type.

3

Check the data type of the 'items_sold' column

Create a variable called items_sold_type and set it to the result of type(sales['items_sold']) to check the data type of the 'items_sold' column.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'day': ['Monday', 'Tuesday', 'Wednesday'],
    'items_sold': [10, 15, 7],
    'revenue': [100.5, 150.75, 70.0]
})
data_types = sales.dtypes
# Check the data type of the 'items_sold' column
# Your code here

Need a hint?

Use the type() function on the column sales['items_sold'].

4

Print the data types

Print the variables data_types and items_sold_type to see the data types of the DataFrame columns and the 'items_sold' column type.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'day': ['Monday', 'Tuesday', 'Wednesday'],
    'items_sold': [10, 15, 7],
    'revenue': [100.5, 150.75, 70.0]
})
data_types = sales.dtypes
items_sold_type = type(sales['items_sold'])
# Print the data_types and items_sold_type variables
# Your code here

Need a hint?

Use two print() statements to show the variables.