Write a program to separate date and time from the datetime column in Python Pandas

When working with datetime data in Pandas, you often need to separate date and time components into different columns. This is useful for data analysis, filtering, and visualization purposes.

Using the dt Accessor (Recommended)

The most efficient approach is using Pandas' dt accessor to extract date and time components directly ?

import pandas as pd

# Create sample DataFrame with datetime column
df = pd.DataFrame({'datetime': pd.date_range('2020-01-01 07:00', periods=6)})
print("Original DataFrame:")
print(df)

# Extract date and time using dt accessor
df['date'] = df['datetime'].dt.date
df['time'] = df['datetime'].dt.time

print("\nAfter separating date and time:")
print(df)

Original DataFrame:
            datetime
0 2020-01-01 07:00:00
1 2020-01-02 07:00:00
2 2020-01-03 07:00:00
3 2020-01-04 07:00:00
4 2020-01-05 07:00:00
5 2020-01-06 07:00:00

After separating date and time:
            datetime        date      time
0 2020-01-01 07:00:00  2020-01-01  07:00:00
1 2020-01-02 07:00:00  2020-01-02  07:00:00
2 2020-01-03 07:00:00  2020-01-03  07:00:00
3 2020-01-04 07:00:00  2020-01-04  07:00:00
4 2020-01-05 07:00:00  2020-01-05  07:00:00
5 2020-01-06 07:00:00  2020-01-06  07:00:00

Using String Formatting

You can also extract date and time as formatted strings for display purposes ?

import pandas as pd

df = pd.DataFrame({'datetime': pd.date_range('2020-01-01 07:00', periods=4)})

# Extract as formatted strings
df['date_str'] = df['datetime'].dt.strftime('%Y-%m-%d')
df['time_str'] = df['datetime'].dt.strftime('%H:%M:%S')

print(df)

            datetime   date_str  time_str
0 2020-01-01 07:00:00  2020-01-01  07:00:00
1 2020-01-02 07:00:00  2020-01-02  07:00:00
2 2020-01-03 07:00:00  2020-01-03  07:00:00
3 2020-01-04 07:00:00  2020-01-04  07:00:00

Working with String Datetime Data

If your datetime column contains strings, first convert it to datetime format ?

import pandas as pd

# Create DataFrame with string datetime
df = pd.DataFrame({
    'datetime_str': ['2020-01-01 09:30:15', '2020-01-02 14:45:30', '2020-01-03 18:20:45']
})

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

# Extract date and time
df['date'] = df['datetime'].dt.date
df['time'] = df['datetime'].dt.time

print(df[['datetime', 'date', 'time']])

            datetime        date      time
0 2020-01-01 09:30:15  2020-01-01  09:30:15
1 2020-01-02 14:45:30  2020-01-02  14:45:30
2 2020-01-03 18:20:45  2020-01-03  18:20:45

Comparison of Methods

Conclusion

Use the dt accessor with .date and .time for extracting date and time components from datetime columns. This method is vectorized and efficient for large datasets. For formatted output, use strftime() with appropriate format codes.