dtypes for column data types in Pandas - Mini Project: Build & Apply

Understanding dtypes for column data types in pandas

📖 Scenario: You work in a small shop and keep track of your sales data in a table. You want to understand what kind of data each column holds so you can analyze it better.

🎯 Goal: You will create a pandas DataFrame with sales data, then check the data types of each column using dtypes.

📋 What You'll Learn

Create a pandas DataFrame with specific sales data

Use the dtypes attribute to find the data types of each column

Print the data types to see the result

💡 Why This Matters

🌍 Real World

Understanding data types helps you clean and analyze data correctly, like knowing which columns hold numbers, text, or dates.

💼 Career

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

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales with these exact columns and values:
'Product': ['Apple', 'Banana', 'Carrot'],
'Quantity': [10, 5, 7],
'Price': [0.5, 0.3, 0.2],
'Date': ['2024-06-01', '2024-06-02', '2024-06-03']

Pandas

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

Need a hint?

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

2

Add a configuration to convert the Date column to datetime

Create a variable called date_column and set it to the string 'Date' to identify the date column.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Quantity': [10, 5, 7],
    'Price': [0.5, 0.3, 0.2],
    'Date': ['2024-06-01', '2024-06-02', '2024-06-03']
})
# Create a variable called date_column and set it to 'Date'
# Your code here

Need a hint?

This variable will help us later to convert the date column.

3

Convert the Date column to datetime and check dtypes

Use pd.to_datetime() to convert the sales[date_column] column to datetime format. Then assign it back to sales[date_column]. Finally, create a variable called column_types and set it to sales.dtypes to get the data types of all columns.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Quantity': [10, 5, 7],
    'Price': [0.5, 0.3, 0.2],
    'Date': ['2024-06-01', '2024-06-02', '2024-06-03']
})
date_column = 'Date'
# Convert the Date column to datetime
# Create column_types to hold sales.dtypes
# Your code here

Need a hint?

Use pd.to_datetime() to convert strings to dates. Then use sales.dtypes to get the data types.

4

Print the data types of each column

Print the variable column_types to display the data types of all columns in the sales DataFrame.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Quantity': [10, 5, 7],
    'Price': [0.5, 0.3, 0.2],
    'Date': ['2024-06-01', '2024-06-02', '2024-06-03']
})
date_column = 'Date'
sales[date_column] = pd.to_datetime(sales[date_column])
column_types = sales.dtypes
# Print the column_types variable
# Your code here

Need a hint?

Just use print(column_types) to see the data types.