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

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

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

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

Hint

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

Practice

1. What does the dtypes attribute in a pandas DataFrame tell you?

easy

A. The names of the columns

B. The number of rows in the DataFrame

C. The total memory used by the DataFrame

D. The type of data stored in each column

Solution

1. Step 1: Understand what dtypes means

   The dtypes attribute shows the data type of each column in a DataFrame, like integer, float, or string.

2. Step 2: Compare options with the definition

   Only the type of data stored in each column correctly describes that dtypes tells the type of data stored in each column.

3. Final Answer:

   The type of data stored in each column -> Option D

4. Quick Check:

   dtypes = data type per column [OK]

Hint: Remember: dtypes show column data types, not counts or names [OK]

Common Mistakes:

• Confusing dtypes with DataFrame shape
• Thinking dtypes shows column names
• Assuming dtypes shows memory usage

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

easy

A. df.dtypes

B. df.types()

C. df.get_dtypes()

D. df.columns.types

Solution

1. Step 1: Recall the correct pandas syntax

   To check column data types, pandas uses the attribute dtypes without parentheses.

2. Step 2: Evaluate each option

   df.dtypes uses df.dtypes, which is correct. Options B and C use incorrect method names, and D is not a valid attribute.

3. Final Answer:

   df.dtypes -> Option A

4. Quick Check:

   Use df.dtypes to check column types [OK]

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

Common Mistakes:

• Adding parentheses after dtypes
• Using non-existent methods like get_dtypes()
• Confusing dtypes with columns attribute

3. Given the following code:

import pandas as pd
data = {'A': [1, 2, 3], 'B': [1.5, 2.5, 3.5], 'C': ['x', 'y', 'z']}
df = pd.DataFrame(data)
print(df.dtypes)

What will be the output?

medium

A. A object
B object
C object
dtype: object

B. A float64
B int64
C object
dtype: object

C. A int64
B float64
C object
dtype: object

D. A int32
B float32
C string
dtype: object

Solution

1. Step 1: Identify data types from the data dictionary

   Column 'A' has integers (1,2,3) so type is int64 by default. Column 'B' has floats (1.5,2.5,3.5) so type is float64. Column 'C' has strings ('x','y','z') which pandas treats as object type.

2. Step 2: Match with the printed output format

   The output shows each column name with its dtype, followed by 'dtype: object'. A int64 B float64 C object dtype: object matches this exactly.

3. Final Answer:

   A int64 B float64 C object dtype: object -> Option C

4. Quick Check:

   Integers=int64, floats=float64, strings=object [OK]

Hint: Integers default to int64, floats to float64, strings to object [OK]

Common Mistakes:

• Assuming strings show as 'string' dtype
• Confusing int32 with int64
• Mixing float and int types

4. You run this code:

import pandas as pd
df = pd.DataFrame({'X': [1, 2, 3], 'Y': ['a', 'b', 'c']})
print(df.dtypes())

But get an error. What is the problem?

medium

A. The DataFrame is empty

B. You should use df.dtypes without parentheses

C. Column names must be integers

D. You need to import numpy first

Solution

1. Step 1: Identify the error cause

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

2. Step 2: Correct the syntax

   Remove the parentheses and use df.dtypes to get the data types without error.

3. Final Answer:

   You should use df.dtypes without parentheses -> Option B

4. Quick Check:

   dtypes is attribute, not method [OK]

Hint: Remember: dtypes has no parentheses, it's not a function [OK]

Common Mistakes:

• Calling dtypes as a function
• Thinking DataFrame is empty causes error
• Believing numpy import is needed for dtypes

5. You have a DataFrame df with columns: age (integers), income (floats), and member (True/False). You want to check the data types and convert member to integer (1 for True, 0 for False). Which code correctly does this?

hard

A. print(df.dtypes)
df['member'] = df['member'].astype(int)

B. print(df.types)
df['member'] = int(df['member'])

C. print(df.dtypes())
df['member'] = df['member'].to_int()

D. print(df.columns)
df['member'] = df['member'].astype('bool')

Solution

1. Step 1: Check data types correctly

   Use df.dtypes (no parentheses) to print data types. Options B and C use incorrect syntax.

2. Step 2: Convert boolean column to integer

   Use astype(int) to convert True/False to 1/0. print(df.dtypes) df['member'] = df['member'].astype(int) does this correctly. print(df.columns) df['member'] = df['member'].astype('bool') converts to bool again, which is unnecessary.

3. Final Answer:

   print(df.dtypes) df['member'] = df['member'].astype(int) -> Option A

4. Quick Check:

   Use df.dtypes and astype(int) for boolean to int [OK]

Hint: Use df.dtypes and astype(int) to convert booleans to 0/1 [OK]

Common Mistakes:

• Calling dtypes as a method
• Trying to convert column with int() directly
• Converting boolean to bool again instead of int