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

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

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

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

Hint

Use two print() statements to show the variables.

Practice

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

easy

A. The type of data stored in each column

B. The number of rows in the DataFrame

C. The names of the columns

D. The total memory used by the DataFrame

Solution

1. Step 1: Understand what dtypes represents

   The dtypes attribute shows the data type of each column in a DataFrame, such as integer, float, or object (text).

2. Step 2: Compare with other DataFrame attributes

   Other attributes like shape give row and column counts, and columns give column names, but dtypes specifically shows data types.

3. Final Answer:

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

4. Quick Check:

   dtypes = data types per column [OK]

Hint: Remember: dtypes = data types of columns [OK]

Common Mistakes:

• Confusing dtypes with number of rows
• Thinking dtypes shows column names
• Assuming dtypes shows memory usage

2. Which of the following is the correct way to check the data type of a single column named 'age' in a pandas DataFrame df?

easy

A. df.dtypes('age')

B. df['age'].dtype

C. df.dtype['age']

D. df.get_dtype('age')

Solution

1. Step 1: Recall syntax for accessing column dtype

   To get the data type of a single column, use df['column_name'].dtype.

2. Step 2: Check other options for correctness

   Options B, C, and D use incorrect syntax or methods that do not exist in pandas.

3. Final Answer:

   df['age'].dtype -> Option B

4. Quick Check:

   Single column dtype = df['column'].dtype [OK]

Hint: Use df['column'].dtype to check one column's type [OK]

Common Mistakes:

• Using parentheses with dtypes like a function
• Trying to access dtype as a dictionary
• Using non-existent methods like get_dtype

3. Given the following code:

import pandas as pd
data = {'name': ['Anna', 'Bob'], 'score': [85, 92]}
df = pd.DataFrame(data)
print(df.dtypes)

What will be the output?

medium

A. name object
score object
dtype: object

B. name int64
score int64
dtype: object

C. name float64
score int64
dtype: object

D. name object
score int64
dtype: object

Solution

1. Step 1: Identify data types of each column

   The 'name' column contains text, so pandas assigns it the 'object' dtype. The 'score' column contains integers, so it gets 'int64'.

2. Step 2: Understand pandas output format

   Printing df.dtypes shows each column name with its dtype, followed by 'dtype: object' indicating the Series type.

3. Final Answer:

   name object score int64 dtype: object -> Option D

4. Quick Check:

   Text = object, integers = int64 [OK]

Hint: Text columns show as object, numbers as int64 or float64 [OK]

Common Mistakes:

• Assuming text columns show as string dtype
• Confusing int64 with float64 for integers
• Expecting score to be object dtype

4. You run this code:

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

What error will you get?

medium

A. AttributeError: 'Series' object has no attribute 'dtypes'

B. TypeError: 'dtype' object is not callable

C. SyntaxError: invalid syntax

D. No error, prints int64

Solution

1. Step 1: Identify the issue with the code

   A pandas Series (df['A']) has a .dtype attribute (singular), not .dtypes (plural, which is for DataFrames).

2. Step 2: Understand the error caused

   Accessing .dtypes on a Series raises AttributeError: 'Series' object has no attribute 'dtypes'. The parentheses would cause further issues if the attribute existed.

3. Final Answer:

   AttributeError: 'Series' object has no attribute 'dtypes' -> Option A

4. Quick Check:

   Series: .dtype | DataFrame: .dtypes [OK]

Hint: Series: .dtype | DataFrame: .dtypes [OK]

Common Mistakes:

• Confusing Series .dtype with DataFrame .dtypes
• Adding parentheses after attributes
• Expecting no error from wrong attribute

5. You have a DataFrame df with columns ['id', 'value', 'date']. The value column is stored as object dtype but contains numbers as strings. Which pandas command will convert value to numeric dtype safely, turning invalid parsing into NaN?

hard

A. df['value'] = df['value'].astype('float64')

B. df['value'] = df['value'].astype(int)

C. df['value'] = pd.to_numeric(df['value'], errors='coerce')

D. df['value'] = df['value'].convert_dtypes()

Solution

1. Step 1: Understand the problem with object dtype

   The 'value' column has numbers stored as strings, so converting directly with astype(int) or astype(float64) may fail if there are invalid strings.

2. Step 2: Use pd.to_numeric with errors='coerce'

   This function converts strings to numbers and replaces invalid parsing with NaN, preventing errors.

3. Step 3: Check other options

   df['value'] = df['value'].convert_dtypes() uses convert_dtypes() which infers types but does not coerce invalid strings safely.

4. Final Answer:

   df['value'] = pd.to_numeric(df['value'], errors='coerce') -> Option C

5. Quick Check:

   Use pd.to_numeric with errors='coerce' for safe conversion [OK]

Hint: Use pd.to_numeric with errors='coerce' to convert safely [OK]

Common Mistakes:

• Using astype(int) on strings causing errors
• Assuming convert_dtypes fixes string numbers
• Not handling invalid strings causing crashes