Merge pull request #627 from HimakarC/main

Added Working with datetime using Pandas section under Pandas Module

contrib/pandas/datetime.md

@@ -0,0 +1,158 @@
+# Working with Date & Time in Pandas
+
+While working with data, it is common to come across data containing date and time. Pandas is a very handy tool for dealing with such data and provides a wide range of date and time data processing options.
+
+- **Parsing dates and times**: Pandas provides a number of functions for parsing dates and times from strings, including `to_datetime()` and `parse_dates()`. These functions can handle a variety of date and time formats, Unix timestamps, and human-readable formats.
+
+- **Manipulating dates and times**: Pandas provides a number of functions for manipulating dates and times, including `shift()`, `resample()`, and `to_timedelta()`. These functions can be used to add or subtract time periods, change the frequency of a time series, and calculate the difference between two dates or times.
+
+- **Visualizing dates and times**: Pandas provides a number of functions for visualizing dates and times, including `plot()`, `hist()`, and `bar()`. These functions can be used to create line charts, histograms, and bar charts of date and time data.
+
+### `Timestamp` function
+
+The timestamp function in Pandas is used to convert a datetime object to a Unix timestamp. A Unix timestamp is a numerical representation of datetime.
+
+Example for retrieving day, month and year from given date:
+
+```python
+import pandas as pd
+
+ts = pd.Timestamp('2024-05-05')
+y = ts.year
+print('Year is: ', y)
+m = ts.month
+print('Month is: ', m)
+d = ts.day
+print('Day is: ', d)
+```
+
+Output:
+
+```python
+Year is:  2024
+Month is:  5
+Day is:  5
+```
+
+Example for extracting time related data from given date:
+
+```python
+import pandas as pd
+
+ts = pd.Timestamp('2024-10-24 12:00:00')
+print('Hour is: ', ts.hour)
+print('Minute is: ', ts.minute)
+print('Weekday is: ', ts.weekday())
+print('Quarter is: ', ts.quarter)
+```
+
+Output:
+
+```python
+Hour is:  12
+Minute is:  0
+Weekday is:  1
+Quarter is:  4
+```
+
+### `Timestamp.now()`
+
+Example for getting current date and time:
+
+```python
+import pandas as pd
+
+ts = pd.Timestamp.now()
+print('Current date and time is: ', ts)
+```
+
+Output:
+```python
+Current date and time is:  2024-05-25 11:48:25.593213
+```
+
+### `date_range` function
+
+Example for generating dates' for next five days:
+
+```python
+import pandas as pd
+
+ts = pd.date_range(start = pd.Timestamp.now(), periods = 5)
+for i in ts:
+    print(i.date())
+```
+
+Output:
+
+```python
+2024-05-25
+2024-05-26
+2024-05-27
+2024-05-28
+2024-05-29
+```
+
+Example for generating dates' for previous five days:
+
+```python
+import pandas as pd
+
+ts = pd.date_range(end = pd.Timestamp.now(), periods = 5)
+for i in ts:
+    print(i.date())
+```
+
+Output:
+```python
+2024-05-21
+2024-05-22
+2024-05-23
+2024-05-24
+2024-05-25
+```
+
+### Built-in vs pandas date & time operations
+
+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 in Pandas:
+
+```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]
+```
+
+Why use pandas functions?
+
+- 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.

contrib/pandas/index.md

@@ -5,5 +5,6 @@
 - [Pandas Descriptive Statistics](Descriptive_Statistics.md)
 - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md)
 - [Excel using Pandas DataFrame](excel_with_pandas.md)
+- [Working with Date & Time in Pandas](datetime.md)
 - [Importing and Exporting Data in Pandas](import-export.md)
 - [Handling Missing Values in Pandas](handling-missing-values.md)