How to technically implement filling missing dates with the last known exchange rate value?

To fill in missing dates with the last known exchange rate, the forward fill method is usually used. Technically, it can be done as follows:

1. There is a time series with dates and rates, where some dates are missing.
2. A complete list of dates with the required interval is created.
3. The data is merged with the complete list of dates, leaving gaps in the rate values.
4. The gaps are filled with the last known record's value (forward fill).

Example in Python using pandas:

import pandas as pd

# Original data
data = {'date': ['2024-01-01', '2024-01-03', '2024-01-06'], 'rate': [100, 102, 105]}
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df = df.set_index('date')

# Create a complete date index
full_idx = pd.date_range(start=df.index.min(), end=df.index.max(), freq='D')

# Reindex and fill gaps
df = df.reindex(full_idx)
df['rate'] = df['rate'].ffill()

print(df)

As a result, missing dates will be filled with the last known exchange rate.