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@ -115,3 +115,52 @@ Output:
2024-05-24
2024-05-25
```
### Pandas `DateTime` is Efficient than Built-in `DateTime` library in various aspects like:
- **In pandas, you may add a time delta to a full column of dates in a single action, but Python's datetime requires a loop.**
Example using Pandas DateTime:
```python
import pandas as pd
dates = pd.DataFrame(pd.date_range('2023-01-01', periods=100000, freq='T'))
dates += pd.Timedelta(days=1)
print(dates)
```
Output:
```python
0
0 2023-01-02 00:00:00
1 2023-01-02 00:01:00
2 2023-01-02 00:02:00
3 2023-01-02 00:03:00
4 2023-01-02 00:04:00
... ...
99995 2023-03-12 10:35:00
99996 2023-03-12 10:36:00
99997 2023-03-12 10:37:00
99998 2023-03-12 10:38:00
99999 2023-03-12 10:39:00
```
Example using Built-In datetime library:
```python
from datetime import datetime, timedelta
dates = [datetime(2023, 1, 1) + timedelta(minutes=i) for i in range(100000)]
dates = [date + timedelta(days=1) for date in dates]
```
Output:
```The output is very large to display and taking more time to display```
- **Pandas employs NumPy's datetime64 dtype, which takes up a set amount of bytes (usually 8 bytes per date), to store datetime data more compactly and efficiently.**
- **Each datetime object in Python takes up extra memory since it contains not only the date and time but also the additional metadata and overhead associated with Python objects.**
- **Pandas Offers a wide range of convenient functions and methods for date manipulation, extraction, and conversion, such as `pd.to_datetime()`, `date_range()`, `timedelta_range()`, and more.**
- **datetime library requires manual implementation for many of these operations, leading to longer and less efficient code.**