Data type conversions

Introduction Reading Time: 12 min

Table of Contents

Description

Data Type Conversion (also called type casting) is the process of converting one data type into another. In Python and data science, it's crucial for ensuring compatibility between different data sources and preparing data for analysis or modeling. Common conversions include:
String ↔ Integer/Float
Object ↔ DateTime
Integer ↔ Float
Categorical ↔ Numeric
In Pandas, this is often done using .astype() or functions like pd.to_numeric(), pd.to_datetime(), or pd.to_timedelta().

Prerequisites

  • Basic Python data types
  • Understanding of Pandas and NumPy
  • Familiarity with data loading and cleaning

Examples

Here's a simple example of a data science task using Python:


import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'age': ['25', '30', '28'],           # String numbers
    'salary': [40000.0, 50000.0, 45000.0],
    'joining_date': ['2021-01-01', '2022-03-15', '2020-07-20']
})

# Convert 'age' from string to integer
df['age'] = df['age'].astype(int)

# Convert 'salary' from float to int
df['salary'] = df['salary'].astype(int)

# Convert 'joining_date' to datetime
df['joining_date'] = pd.to_datetime(df['joining_date'])

# Convert 'age' to string again
df['age'] = df['age'].astype(str)

print(df.dtypes)  # Check data types after conversion
          

Real-World Applications

Healthcare
Convert string-based dates to datetime objects for tracking patient history

Finance
Change transaction amounts from strings to numeric types for aggregation

E-commerce
Transform product IDs to strings or customer ratings to integers

Where topic Is Applied

Finance

  • Casting transaction amounts for analysis
  • Converting date strings into date objects for time-series analysis

E-commerce

  • Parsing product prices and quantities correctly
  • Handling data imported from Excel or CSV

Manufacturing

  • Converting time logs to datetime
  • Handling numeric sensor readings

Resources

Data Science topic PDF

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Harvard Data Science Course

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Interview Questions

➤ It's the process of converting data from one type to another, like int(), float(), or str()

➤ Use .astype() method or functions like pd.to_numeric().

➤ astype() strictly converts to the specified type and may raise an error; pd.to_numeric() allows more flexibility (e.g., errors='coerce')..

➤ To convert invalid parsing into NaN instead of throwing an error.

➤ When loading data from external sources like CSVs where numbers or dates are interpreted as strings.