Data Type Conversions

Why Convert Types?

Sometimes data comes in wrong format:

Numbers stored as text: "100" instead of 100
Dates stored as text: "2024-01-15" instead of date
Categories stored as text: takes more memory

Check Current Data Types

code.py

import pandas as pd

df = pd.DataFrame({
    'Price': ['100', '200', '150'],
    'Quantity': [5, 10, 8],
    'Date': ['2024-01-01', '2024-01-02', '2024-01-03']
})

print(df.dtypes)

Output:

Price       object   <- text (should be number!)
Quantity     int64   <- number (good)
Date        object   <- text (should be date!)

Convert Text to Number

code.py

# Convert Price from text to number
df['Price'] = pd.to_numeric(df['Price'])
print(df.dtypes)

Now Price is int64 (number).

Handle Bad Data in Conversion

code.py

df = pd.DataFrame({
    'Price': ['100', '200', 'unknown', '150']
})

# This will error because 'unknown' can't be a number
# df['Price'] = pd.to_numeric(df['Price'])

# Use errors='coerce' to make bad values NaN
df['Price'] = pd.to_numeric(df['Price'], errors='coerce')
print(df)

Output:

   Price
0  100.0
1  200.0
2    NaN  <- 'unknown' became NaN
3  150.0

Convert to Text (String)

code.py

df = pd.DataFrame({
    'ID': [1, 2, 3]
})

df['ID'] = df['ID'].astype(str)
print(df.dtypes)

ID is now object (text).

Convert Text to Date

code.py

df = pd.DataFrame({
    'Date': ['2024-01-15', '2024-02-20', '2024-03-10']
})

df['Date'] = pd.to_datetime(df['Date'])
print(df.dtypes)
print(df)

Output:

Date    datetime64[ns]

        Date
0 2024-01-15
1 2024-02-20
2 2024-03-10

Convert to Category

Categories use less memory for repeated text.

code.py

df = pd.DataFrame({
    'Status': ['Active', 'Inactive', 'Active', 'Active', 'Inactive']
})

print("Before:", df['Status'].memory_usage())

df['Status'] = df['Status'].astype('category')

print("After:", df['Status'].memory_usage())

Uses less memory after conversion!

Common Conversions

From	To	Method
Text → Number	pd.to_numeric()
Text → Date	pd.to_datetime()
Any → Text	.astype(str)
Any → Integer	.astype(int)
Any → Float	.astype(float)
Text → Category	.astype('category')

Convert Multiple Columns

code.py

df = df.astype({
    'Price': float,
    'Quantity': int,
    'Status': 'category'
})

Key Points

df.dtypes shows all column types
pd.to_numeric() converts to number
pd.to_datetime() converts to date
.astype() converts to any type
errors='coerce' turns bad values to NaN
Categories save memory for repeated text

What's Next?

Learn to clean and work with text data using string methods.