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

Need a 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

Need a 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

Need a 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

Need a hint?

Use print(sales) to show the DataFrame.