Write a program in Python to covert the datatype of a particular column in a dataframe

In Pandas, you can convert the datatype of a specific column using the astype() method. This is useful when you need to change data types for analysis, memory optimization, or data consistency.

Creating a Sample DataFrame

Let's start by creating a DataFrame with mixed data types ?

import pandas as pd

data = {'Name': ['David', 'Adam', 'Bob', 'Alex', 'Serina'],
        'Age': [13, 12, 12, 13, 12],
        'Maths': [98, 59, 66, 95, 70],
        'Science': [75, 96, 55, 49, 78],
        'English': [79, 45, 70, 60, 80],
        'Result': [8.1, 6.2, 6.3, 7.2, 8.3]}

df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)
print("\nData types:")
print(df.dtypes)

Original DataFrame:
     Name  Age  Maths  Science  English  Result
0   David   13     98       75       79     8.1
1    Adam   12     59       96       45     6.2
2     Bob   12     66       55       70     6.3
3    Alex   13     95       49       60     7.2
4  Serina   12     70       78       80     8.3

Data types:
Name       object
Age         int64
Maths       int64
Science     int64
English     int64
Result    float64
dtype: object

Converting Float to Integer

To convert the 'Result' column from float64 to int64, use the astype() method ?

import pandas as pd

data = {'Name': ['David', 'Adam', 'Bob', 'Alex', 'Serina'],
        'Age': [13, 12, 12, 13, 12],
        'Maths': [98, 59, 66, 95, 70],
        'Science': [75, 96, 55, 49, 78],
        'English': [79, 45, 70, 60, 80],
        'Result': [8.1, 6.2, 6.3, 7.2, 8.3]}

df = pd.DataFrame(data)
print("Before conversion:")
print(df.dtypes)

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

print("\nAfter conversion:")
print(df.dtypes)
print("\nConverted DataFrame:")
print(df)

Before conversion:
Name       object
Age         int64
Maths       int64
Science     int64
English     int64
Result    float64
dtype: object

After conversion:
Name       object
Age         int64
Maths       int64
Science     int64
English     int64
Result      int64
dtype: object

Converted DataFrame:
     Name  Age  Maths  Science  English  Result
0   David   13     98       75       79       8
1    Adam   12     59       96       45       6
2     Bob   12     66       55       70       6
3    Alex   13     95       49       60       7
4  Serina   12     70       78       80       8

Alternative Methods

You can also convert multiple columns at once using a dictionary ?

import pandas as pd

data = {'Name': ['David', 'Adam', 'Bob'],
        'Score': [85.5, 92.3, 78.8],
        'Grade': [3.2, 3.8, 2.9]}

df = pd.DataFrame(data)
print("Before conversion:")
print(df.dtypes)

# Convert multiple columns at once
df = df.astype({'Score': 'int', 'Grade': 'int'})

print("\nAfter conversion:")
print(df.dtypes)
print("\nConverted DataFrame:")
print(df)

Before conversion:
Name     object
Score   float64
Grade   float64
dtype: object

After conversion:
Name     object
Score     int64
Grade     int64
dtype: object

Converted DataFrame:
    Name  Score  Grade
0  David     85      3
1   Adam     92      3
2    Bob     78      2

Common Data Type Conversions

Conclusion

Use astype() to convert column data types in Pandas DataFrames. This method is essential for data cleaning and ensures your data has the correct types for analysis.