How to Fill Missing Dates in a Pandas DataFrame?

Add Missing Dates to Pandas Dataframe

When dealing with time-series data, it's common to encounter missing dates. This can arise when events occur on certain dates but not on others. To accurately represent this data, it's necessary to account for the missing dates.

In the provided code, a Pandas dataframe is created with date as the index. While the date range includes all days within a specific time frame, the size of the dataframe is smaller because some dates have no associated events. This results in mismatched sizes when attempting to plot the date range and the dataframe.

The preferred approach is to add missing dates to the series with a count of 0. This ensures a complete graph with all dates accounted for. To do this, the reindex method can be utilized:

import pandas as pd

idx = pd.date_range('09-01-2013', '09-30-2013')

s = pd.Series({'09-02-2013': 2,
               '09-03-2013': 10,
               '09-06-2013': 5,
               '09-07-2013': 1})

s.index = pd.DatetimeIndex(s.index)

s = s.reindex(idx, fill_value=0)

This will output a new series s with all missing dates between '09-01-2013' and '09-30-2013' filled with 0 values:

2013-09-01     0
2013-09-02     2
2013-09-03    10
2013-09-04     0
2013-09-05     0
2013-09-06     5
2013-09-07     1
2013-09-08     0
...

By using reindex, the missing dates are added to the series, allowing for accurate plotting and analysis of the time-series data.

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