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Move dates forward a given number of valid dates using Pandas

Let's work through a brief tutorial on moving dates forward by a given number of valid dates using Pandas in Python.

For this example, I'm assuming:

You have a starting date.
You want to move forward by a given number of business days (skipping weekends and potentially holidays).
You're using the pandas library which provides great utilities for this.

Let's begin!

1. Set Up Environment and Libraries:

import pandas as pd

2. Define a Starting Date:

For this example, we'll use 2023-08-31 as our starting date.

start_date = pd.Timestamp('2023-08-31')

3. Move Dates Forward by N Valid Dates:

If you want to move forward by, let's say, 5 business days:

n = 5  # number of valid dates to move forward
end_date = pd.bdate_range(start=start_date, periods=n+1)[-1]  # The +1 is because the start date is included

4. Optional: Accounting for Holidays:

If you want to account for holidays (let's consider New Year's Day and Christmas Day in our example), you can do so with a custom business day calendar:

from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday
from pandas.tseries.offsets import CustomBusinessDay

class CustomCalendar(AbstractHolidayCalendar):
    rules = [
        Holiday('New Year's Day', month=1, day=1, observance=lambda dt: dt if dt.weekday() < 5 else pd.NaT),
        Holiday('Christmas Day', month=12, day=25, observance=lambda dt: dt if dt.weekday() < 5 else pd.NaT)
    ]

custom_bday = CustomBusinessDay(calendar=CustomCalendar())

end_date_with_holidays = start_date + n * custom_bday

This end_date_with_holidays will be our new end date considering both weekends and the holidays we defined.

Final Output:

print(f"Start Date: {start_date.strftime('%Y-%m-%d')}")
print(f"End Date without holidays: {end_date.strftime('%Y-%m-%d')}")
print(f"End Date with holidays: {end_date_with_holidays.strftime('%Y-%m-%d')}")

This will show you the start date, the end date moved by 5 business days, and the end date moved by 5 business days while also considering the holidays.

Remember, this is a basic example, and you can extend it to consider more holidays, different rules for weekends (e.g., if you're in a country where the weekend is not Saturday-Sunday), and so on.

Shift dates by a specific period in Pandas:

Description: Use the .shift() method to shift dates in a Pandas DataFrame by a specific number of periods.

Code:

import pandas as pd

# Create DataFrame with dates
df = pd.DataFrame({'date': pd.date_range('2022-01-01', periods=5)})

# Shift dates by 2 days
df['shifted_date'] = df['date'].shift(2)

Increment dates in Pandas DataFrame:

Description: Increment dates in a Pandas DataFrame by adding a specific number of periods using pd.DateOffset.

Code:

import pandas as pd

# Create DataFrame with dates
df = pd.DataFrame({'date': pd.date_range('2022-01-01', periods=5)})

# Increment dates by 3 days
df['incremented_date'] = df['date'] + pd.DateOffset(days=3)

Advancing dates in Pandas by a certain number:

Description: Advance dates in a Pandas DataFrame by a certain number of periods using the .apply() method.

Code:

import pandas as pd

# Create DataFrame with dates
df = pd.DataFrame({'date': pd.date_range('2022-01-01', periods=5)})

# Advance dates by 4 weeks
df['advanced_date'] = df['date'].apply(lambda x: x + pd.DateOffset(weeks=4))

Moving dates forward using timedelta in Pandas:

Description: Move dates forward by a specified timedelta using the pd.to_timedelta() function.

Code:

import pandas as pd

# Create DataFrame with dates
df = pd.DataFrame({'date': pd.date_range('2022-01-01', periods=5)})

# Move dates forward by 2 days
df['forward_date'] = df['date'] + pd.to_timedelta('2 days')

Shifting time series data in Pandas:

Description: Use the .shift() method to shift time series data in a Pandas DataFrame.

Code:

import pandas as pd

# Create DataFrame with time series data
df = pd.DataFrame({'value': [10, 20, 30, 40, 50]}, index=pd.date_range('2022-01-01', periods=5))

# Shift time series data by 1 period
df['shifted_value'] = df['value'].shift(1)

Shift dates in Pandas DataFrame by frequency:

Description: Shift dates in a Pandas DataFrame by a specific frequency using pd.DateOffset.

Code:

import pandas as pd

# Create DataFrame with dates
df = pd.DataFrame({'date': pd.date_range('2022-01-01', periods=5)})

# Shift dates by 1 month
df['shifted_date'] = df['date'] + pd.DateOffset(months=1)

Advance datetime index by a number of days in Pandas:

Description: Advance the datetime index of a Pandas DataFrame by a specified number of days.

Code:

import pandas as pd

# Create DataFrame with datetime index
df = pd.DataFrame({'value': [10, 20, 30, 40, 50]}, index=pd.date_range('2022-01-01', periods=5))

# Advance datetime index by 3 days
df.index = df.index + pd.DateOffset(days=3)

Adding business days to dates in Pandas:

Description: Add business days (excluding weekends) to dates using the pd.tseries.offsets module.

Code:

import pandas as pd

# Create DataFrame with dates
df = pd.DataFrame({'date': pd.date_range('2022-01-01', periods=5)})

# Add 2 business days to dates
df['business_days'] = df['date'] + pd.tseries.offsets.BDay(2)

Shift and advance dates with Pandas date_offset:

Description: Use pd.DateOffset for both shifting and advancing dates in a Pandas DataFrame.

Code:

import pandas as pd

# Create DataFrame with dates
df = pd.DataFrame({'date': pd.date_range('2022-01-01', periods=5)})

# Shift dates by 1 week and advance by 3 days
df['shifted_advanced_date'] = df['date'] + pd.DateOffset(weeks=1) + pd.DateOffset(days=3)