Common dtype errors and fixes in Pandas - Mini Project: Build & Apply

Common dtype errors and fixes

📖 Scenario: You work as a data analyst. You receive a small sales dataset with product names, quantities sold, and prices. Some numbers are stored as text, causing problems when you try to analyze the data.Your job is to fix these data type errors so you can do calculations correctly.

🎯 Goal: Fix the data types of columns in a pandas DataFrame to enable correct numerical calculations.

📋 What You'll Learn

Create a pandas DataFrame with given data including some numbers stored as strings

Create a list of columns that need to be converted to numeric types

Convert the specified columns to numeric types using pandas functions

Print the fixed DataFrame to verify changes

💡 Why This Matters

🌍 Real World

Data often comes with numbers stored as text, especially from Excel or CSV files. Fixing data types is essential before analysis.

💼 Career

Data scientists and analysts frequently clean data by converting columns to correct types to avoid errors in calculations and visualizations.

1

Create the initial sales DataFrame

Create a pandas DataFrame called sales with these exact columns and values:
'Product': ['Pen', 'Notebook', 'Eraser'],
'Quantity': ['10', '5', '8'],
'Price': ['1.5', '3.0', '0.5'].
Note that Quantity and Price columns are strings representing numbers.

Pandas

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

Hint

Use pd.DataFrame with a dictionary of lists for columns.

2

Create a list of columns to convert

Create a list called cols_to_convert containing the column names 'Quantity' and 'Price' that need to be converted to numeric types.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Quantity': ['10', '5', '8'],
    'Price': ['1.5', '3.0', '0.5']
})
# Create a list of columns to convert to numeric
# Your code here

Hint

Just create a list with the two column names as strings.

3

Convert columns to numeric types

Use a for loop with variable col to iterate over cols_to_convert. Inside the loop, convert the sales[col] column to numeric type using pd.to_numeric with errors='coerce'.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Quantity': ['10', '5', '8'],
    'Price': ['1.5', '3.0', '0.5']
})
cols_to_convert = ['Quantity', 'Price']
# Convert columns in cols_to_convert to numeric types
# Your code here

Hint

Use a for loop and pd.to_numeric with errors='coerce' to convert each column.

4

Print the fixed DataFrame

Write a print statement to display the sales DataFrame after conversion.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Quantity': ['10', '5', '8'],
    'Price': ['1.5', '3.0', '0.5']
})
cols_to_convert = ['Quantity', 'Price']
for col in cols_to_convert:
    sales[col] = pd.to_numeric(sales[col], errors='coerce')
# Print the sales DataFrame
# Your code here

Hint

Use print(sales) to show the DataFrame.

Practice

1. What is the main reason to fix data types (dtypes) in a pandas DataFrame?

easy

A. To change column names automatically

B. To make the DataFrame look colorful

C. To increase the number of rows

D. To avoid errors and get correct analysis results

Solution

1. Step 1: Understand the role of dtypes

   Dtypes tell pandas how to treat data, like numbers or text.

2. Step 2: Recognize the impact of wrong dtypes

   If dtypes are wrong, calculations or filters may fail or give wrong answers.

3. Final Answer:

   To avoid errors and get correct analysis results -> Option D

4. Quick Check:

   Fixing dtypes = correct results [OK]

Hint: Fix dtypes to prevent errors and wrong answers [OK]

Common Mistakes:

• Ignoring dtype issues and expecting correct results
• Thinking dtype fixes change data values
• Confusing dtype fixes with styling

2. Which of the following is the correct syntax to convert a pandas column named 'age' to integer type?

easy

A. df['age'].astype(int)

B. df.astype('age', int)

C. astype(df['age'], 'int')

D. df['age'].convert(int)

Solution

1. Step 1: Recall the correct method to change dtype

   Use the astype() method on the DataFrame column.

2. Step 2: Check the syntax for astype()

   The correct syntax is df['column'].astype(type).

3. Final Answer:

   df['age'].astype(int) -> Option A

4. Quick Check:

   astype() method syntax = df['age'].astype(int) [OK]

Hint: Use df['col'].astype(type) to fix dtype [OK]

Common Mistakes:

• Using astype on DataFrame instead of column
• Wrong method names like convert()
• Passing arguments in wrong order

3. Given the code:

import pandas as pd
df = pd.DataFrame({'score': ['10', '20', '30']})
df['score'] = df['score'].astype(int)
print(df['score'].sum())

What is the output?

medium

A. '102030'

B. TypeError

C. 60

D. None

Solution

1. Step 1: Convert 'score' column from string to int

   The astype(int) changes strings like '10' to integer 10.

2. Step 2: Calculate the sum of integer values

   Sum of 10 + 20 + 30 = 60.

3. Final Answer:

   60 -> Option C

4. Quick Check:

   Sum of integers = 60 [OK]

Hint: Convert strings to int before sum to get numeric result [OK]

Common Mistakes:

• Not converting strings to int before sum
• Expecting string concatenation instead of sum
• Confusing sum output with error

4. You run this code:

df = pd.DataFrame({'price': ['100', '200', '300']})
df['price'] = df['price'].astype(float)
df['price'] = df['price'].astype(int)

But get an error. What is the likely cause?

medium

A. DataFrame missing import statement

B. Strings cannot be converted to float

C. Cannot convert float to int directly if float has decimals

D. astype() cannot be chained

Solution

1. Step 1: Examine the code snippet

   The code uses pd.DataFrame but no import statement is shown.

2. Step 2: Identify the runtime error

   Without import pandas as pd, pd raises NameError: name 'pd' is not defined.

3. Final Answer:

   DataFrame missing import statement -> Option A

4. Quick Check:

   Missing import causes NameError [OK]

Hint: Always import pandas as pd before using pd.DataFrame [OK]

Common Mistakes:

• Forgetting the import pandas as pd statement
• Assuming pd is predefined
• Focusing on dtype conversions instead of basic syntax

5. You have a DataFrame with a column 'date' containing strings like '2023-01-01'. You want to analyze by month but get errors when grouping. What is the best way to fix the dtype before grouping?

hard

A. Convert 'date' column to integer using astype(int)

B. Convert 'date' column to datetime using pd.to_datetime()

C. Leave 'date' as string and group directly

D. Drop the 'date' column before grouping

Solution

1. Step 1: Identify the problem with string dates

   Grouping by string dates does not allow easy extraction of months.

2. Step 2: Convert strings to datetime type

   Use pd.to_datetime() to convert strings to datetime objects.

3. Step 3: Group by month after conversion

   Datetime dtype allows grouping by month using dt.month.

4. Final Answer:

   Convert 'date' column to datetime using pd.to_datetime() -> Option B

5. Quick Check:

   Datetime dtype needed for date grouping [OK]

Hint: Use pd.to_datetime() to fix date strings before grouping [OK]

Common Mistakes:

• Trying to group by string dates directly
• Converting dates to int which loses meaning
• Dropping date column instead of fixing dtype