Python - Convert one datatype to another in a Pandas DataFrame

Use the astype() method in Pandas to convert one datatype to another in a DataFrame. This method allows you to convert columns to specific data types for memory optimization or compatibility requirements.

Creating a DataFrame with Mixed Data Types

Let's create a DataFrame with different column types ?

import pandas as pd

# Create DataFrame with mixed types
dataFrame = pd.DataFrame({
    "Reg_Price": [7000.5057, 1500, 5000, 8000, 9000.75768, 6000],
    "Units": [90, 120, 100, 150, 200, 130]
})

print("DataFrame:")
print(dataFrame)

DataFrame:
   Reg_Price  Units
0  7000.5057     90
1  1500.0000    120
2  5000.0000    100
3  8000.0000    150
4  9000.7577    200
5  6000.0000    130

Checking Current Data Types

Use the dtypes attribute to view column data types ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Reg_Price": [7000.5057, 1500, 5000, 8000, 9000.75768, 6000],
    "Units": [90, 120, 100, 150, 200, 130]
})

print("Original data types:")
print(dataFrame.dtypes)

Original data types:
Reg_Price    float64
Units          int64
dtype: object

Converting All Columns to Same Type

Convert all columns to int32 using astype() ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Reg_Price": [7000.5057, 1500, 5000, 8000, 9000.75768, 6000],
    "Units": [90, 120, 100, 150, 200, 130]
})

# Convert all columns to int32
converted_df = dataFrame.astype('int32')

print("After conversion to int32:")
print(converted_df.dtypes)
print("\nConverted DataFrame:")
print(converted_df)

After conversion to int32:
Reg_Price    int32
Units        int32
dtype: object

Converted DataFrame:
   Reg_Price  Units
0       7000     90
1       1500    120
2       5000    100
3       8000    150
4       9000    200
5       6000    130

Converting Specific Columns

You can also convert specific columns to different types ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Reg_Price": [7000.5057, 1500, 5000, 8000, 9000.75768, 6000],
    "Units": [90, 120, 100, 150, 200, 130]
})

# Convert specific columns to different types
dataFrame['Reg_Price'] = dataFrame['Reg_Price'].astype('int64')
dataFrame['Units'] = dataFrame['Units'].astype('float32')

print("After selective conversion:")
print(dataFrame.dtypes)
print("\nDataFrame:")
print(dataFrame)

After selective conversion:
Reg_Price      int64
Units        float32
dtype: object

DataFrame:
   Reg_Price  Units
0       7000   90.0
1       1500  120.0
2       5000  100.0
3       8000  150.0
4       9000  200.0
5       6000  130.0

Conclusion

The astype() method is essential for data type conversion in Pandas. Use it to optimize memory usage, ensure compatibility, or prepare data for specific operations. Remember that converting float to int truncates decimal values.