Time series data is everywhere in stock prices, weather data,server logs, sales records ,website traffic per hour,machine logs per second. Pandas has tools for parsing,indexing,resampling and analyzing datasets.

Note - The .dt accessor works for period and timedelta dtypes.

1.Converting to Datetime.

The first process convert the date column to date time before doing the time series analysis.

df['date'] = pd.to_datetime(df['date'])

2.Extracting day from a column date

a.Having the output as 0,1,2,3...

df['day_of_week_num'] = df['date'].dt.weekday

• df['day_of_week_num']: It extracts the numeric day of the week, where Monday is 0 and Sunday is 6.

b. Having the output as name Monday,tuesday,Wednesday,thursday

df['day_of_week_name'] = df['date'].dt.day_name()

• It returns the name of the day for each date in the Series or DataFrame, such as "Monday", "Tuesday"

c. Abbreviated day name (e.g., "Mon", "Tue", "Wed")

df['day_of_week_abbr'] = df['date'].dt.strftime('%a')

• This gives you a short form for the day name, which is often used when you want to save space or display compact labels.

3. Check for Weekend.

df['is_weekend'] = df['date'].dt.weekday >= 5

• This create a new column in your DataFrame that flags whether a date falls on a weekend (Saturday or Sunday).

4. Extracting time attributes .

Here we will extract Hours,Minutes and seconds.

.dt.hour: - This Extracts the hour from the datetime (0–23).
.dt.minute: - It Extracts the minute from the datetime (0–59)
.dt.second: It Extracts the second from the datetime (0–59).

5. Extract Month (Number and Name).

df['month'] = df['date'].dt.month

• Extracts the numeric month (1–12) from the date column.

df['month_name'] = df['date'].dt.month_name() 

• Extracts the full month name (e.g., "January", "February").

C. Three-letter abbreviation

df['month_abbr'] = df['month_name'].str[:3]

6.To Extract the quarter of the year from a datetime column use .dt.quarter

 df['quarter'] = df['date'].dt.quarter - 1

7.Half of the Year (0 = Jan–Jun, 1 = Jul–Dec)

df['half'] = np.where(df['month'] < 6, 0, 1)

It assumes your month column is zero-based (i.e., 0 = January, 11 = December).

• Months 0–5 → First Half (0)
• Months 6–11 → Second Half (1)

8 . Week of the Year (.dt.isocalendar().week)

df['week_of_year'] = df['date'].dt.isocalendar().week

Note: As of Pandas 1.1.0, .dt.week is deprecated and replaced by .dt.isocalendar().week.

Note.What is ISO Week?

• ISO weeks follow the ISO 8601 standard:
• Weeks start on Monday
• Week 1 is the week that contains the first Thursday of the year
• Can have 52 or 53 weeks.

9. Year.

df['year']=df['date'].dt.year

• Extracts the year as an interger.
• It's useful for grouping,filtering or feature engineering.
• Splits time series data by year.
• Groups data for annual trends.
• Creates Pivot tables or plots year-wise

10. 📅 Day of the month.

• This extracts the day of the month (1-31)

df['day_of_month'=df['date'].dt.day

11. How to handle holidays in pandas

You have to manually define your own list of public holidays and pass them to customBusinessDay.

from pandas.tseries.offsets import CustomBusinessDay
 Kenya_holidays=pd.to_datetime([
   '2025-01-01',  # New Year's Day
   '2025-03-29',  # Good Friday
    '2025-05-01',  # Labour Day
    '2025-06-01',  # Madaraka Day
    '2025-10-20',  # Mashujaa Day
    '2025-12-12',  # Jamhuri Day
    '2025-12-25',  # Christmas
    '2025-12-26',  # Boxing Day
])

• Create a custom businessDay (Holidays=kenya_holidays)

kenya_bday=CustomBusinessDay(holidays=kenya_holidays)

• used to generate business days

dates=pd.date_range('2025-12-20','2025-12-31' freq=kenya_bday)

12. dt.period is an accessor in pandas used with datetime-like columns to convert them into period objects.

a.Calendar Frequencies.

• This is an example of calendar frequency. df['period'] = df['date'].dt.to_period('M')

b.Business Frequencies.

• They are special time intervals that consider only working days(Monday to Friday) and can exclude holidays if well defined.
• Finance - No trading on weekends or holidays.
• Payroll/HR - Payments and attendance only on working days.
• Operations - Delivery and logistics exclude weekends. The key difference from calendar frequencies include.

.

• Business Day Frequencies

• Business Month Frequencies.

Get last business day of each month in 2024

bm_end = pd.date_range(start='2024-01-01', end='2024-12-31', freq='BM')
print(bm_end)

Output >>> DatetimeIndex(['2024-01-31', '2024-02-29', '2024-03-29', '2024-04-30', ...], dtype='datetime64[ns]', freq='BM')

-Business Quarter Frequencies.

• Business Year Frequencies.