Basic DataFrame info (shape, dtypes, describe) in Data Analysis Python - Mini Project: Build & Apply

Basic DataFrame info (shape, dtypes, describe)

📖 Scenario: You work in a small store and have a list of products with their prices and quantities. You want to organize this data in a table to learn more about it.

🎯 Goal: Create a DataFrame with product data, then find out its size, data types of each column, and some basic statistics.

📋 What You'll Learn

Create a pandas DataFrame with given product data

Create a variable to hold the DataFrame shape

Create a variable to hold the DataFrame data types

Create a variable to hold the DataFrame descriptive statistics

Print the shape, data types, and descriptive statistics

💡 Why This Matters

🌍 Real World

Stores and businesses often keep product data in tables and need to quickly understand the data size, types, and summary statistics to make decisions.

💼 Career

Data analysts and scientists use these basic DataFrame methods daily to explore and clean data before deeper analysis.

1

Create the product DataFrame

Import pandas as pd and create a DataFrame called df with these exact data: products 'Apple', 'Banana', 'Carrot', prices 0.5, 0.3, 0.2, and quantities 30, 45, 50.

Data Analysis Python

# Your code here

Need a hint?

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

2

Get the DataFrame shape

Create a variable called shape and set it to the shape of the DataFrame df using the shape attribute.

Data Analysis Python

import pandas as pd

data = {'Product': ['Apple', 'Banana', 'Carrot'], 'Price': [0.5, 0.3, 0.2], 'Quantity': [30, 45, 50]}
df = pd.DataFrame(data)
# Your code here

Need a hint?

Use df.shape to get the number of rows and columns as a tuple.

3

Get the DataFrame data types

Create a variable called dtypes and set it to the data types of each column in df using the dtypes attribute.

Data Analysis Python

import pandas as pd

data = {'Product': ['Apple', 'Banana', 'Carrot'], 'Price': [0.5, 0.3, 0.2], 'Quantity': [30, 45, 50]}
df = pd.DataFrame(data)
shape = df.shape
# Your code here

Need a hint?

Use df.dtypes to see the type of data in each column.

4

Get descriptive statistics and print all info

Create a variable called description and set it to the result of df.describe(). Then print shape, dtypes, and description each on its own line.

Data Analysis Python

import pandas as pd

data = {'Product': ['Apple', 'Banana', 'Carrot'], 'Price': [0.5, 0.3, 0.2], 'Quantity': [30, 45, 50]}
df = pd.DataFrame(data)
shape = df.shape
dtypes = df.dtypes
# Your code here

Need a hint?

Use df.describe() to get statistics like mean and min. Use print() to show the results.