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

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

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

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

Hint

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

Practice

1. What does df.shape tell you about a DataFrame?

easy

A. The data types of each column

B. The number of rows and columns in the DataFrame

C. Summary statistics like mean and median

D. The first few rows of the DataFrame

Solution

1. Step 1: Understand what df.shape returns

   df.shape gives a tuple with two numbers: rows and columns count.

2. Step 2: Match the description to the options

   The number of rows and columns in the DataFrame correctly describes this as the number of rows and columns.

3. Final Answer:

   The number of rows and columns in the DataFrame -> Option B

4. Quick Check:

   Shape = (rows, columns) [OK]

Hint: Remember shape shows size: rows and columns [OK]

Common Mistakes:

• Confusing shape with data types
• Thinking shape shows data summary
• Mixing shape with head() output

2. Which of the following is the correct way to check the data types of columns in a DataFrame named df?

easy

A. df.dtypes

B. df.columns.types

C. df.describe()

D. df.types()

Solution

1. Step 1: Recall the attribute for column data types

   The correct attribute to check data types is df.dtypes, not a method.

2. Step 2: Verify the options

   df.dtypes uses df.dtypes correctly. Others are invalid or do not exist.

3. Final Answer:

   df.dtypes -> Option A

4. Quick Check:

   dtypes attribute shows column types [OK]

Hint: Use df.dtypes without parentheses to check types [OK]

Common Mistakes:

• Adding parentheses like df.dtypes()
• Using non-existent methods like df.types()
• Confusing describe() with dtypes

3. Given the DataFrame df below:

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4.0, 5.5, 6.1], 'C': ['x', 'y', 'z']}
df = pd.DataFrame(data)
print(df.describe())

What will be the output of df.describe()?

medium

A. Summary statistics for all columns including C

B. An error because column C is non-numeric

C. Summary statistics for columns A and B only, excluding C

D. Only the count of rows

Solution

1. Step 1: Understand what df.describe() does

   It provides summary stats (count, mean, std, min, max, quartiles) for numeric columns only by default.

2. Step 2: Check the DataFrame columns

   Columns A and B are numeric (int and float), C is string (non-numeric), so C is excluded.

3. Final Answer:

   Summary statistics for columns A and B only, excluding C -> Option C

4. Quick Check:

   describe() skips non-numeric columns by default [OK]

Hint: describe() shows stats only for numeric columns by default [OK]

Common Mistakes:

• Expecting describe() to include string columns
• Thinking describe() throws error on strings
• Assuming describe() shows only count

4. You run the code below and get an error:

import pandas as pd
data = {'X': [10, 20, 30], 'Y': [5, 15, 25]}
df = pd.DataFrame(data)
print(df.dtypes())

What is the cause of the error?

medium

A. dtypes is an attribute, not a method, so parentheses cause error

B. DataFrame columns have mixed types

C. DataFrame is empty

D. Missing import statement for pandas

Solution

1. Step 1: Identify the error source

   The code calls df.dtypes() with parentheses, but dtypes is an attribute, not a method.

2. Step 2: Understand correct usage

   Correct usage is df.dtypes without parentheses to access data types.

3. Final Answer:

   dtypes is an attribute, not a method, so parentheses cause error -> Option A

4. Quick Check:

   Use df.dtypes without () [OK]

Hint: Remember dtypes is attribute, no parentheses [OK]

Common Mistakes:

• Adding parentheses to dtypes
• Assuming error is from missing import
• Thinking DataFrame is empty

5. You have a DataFrame df with columns: Age (int), Salary (float), and Department (string). You want to quickly check how many rows and columns it has, the data types of each column, and summary statistics for numeric columns. Which sequence of commands gives you all this information?

hard

A. df.columns; df.dtypes; df.tail()

B. df.describe(); df.shape; df.columns

C. df.info(); df.head(); df.describe()

D. df.shape; df.dtypes; df.describe()

Solution

1. Step 1: Identify commands for each info type

   df.shape gives rows and columns count, df.dtypes shows data types, and df.describe() gives summary stats for numeric columns.

2. Step 2: Check options for correct sequence

   df.shape; df.dtypes; df.describe() lists these three commands in order, so it provides all required info.

3. Final Answer:

   df.shape; df.dtypes; df.describe() -> Option D

4. Quick Check:

   shape, dtypes, describe cover size, types, stats [OK]

Hint: Use shape, dtypes, then describe for full quick info [OK]

Common Mistakes:

• Using df.columns instead of df.dtypes for types
• Confusing df.info() with df.describe()
• Missing shape to check size