How Can I Fill Missing Dates in My Pandas DataFrame?

Filling Missing Dates in Pandas DataFrames

When working with pandas dataframes, it's common to encounter situations where dates are missing from the dataset. This can lead to errors when performing operations such as plotting or calculating statistics.

Consider the case where you have a dataframe with multiple events on a given date or no events on a date. If you group the dataframe by date and count the events, you may end up with a series that has fewer dates than the original range. This can cause an error when trying to plot the series against the original date range.

The solution to this issue is to add the missing dates to the series with a count of 0. This can be achieved using the reindex function. The reindex function takes a new index as an argument and fills the missing values with a specified value (defaulting to NaN).

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)

The above code uses reindex to add the missing dates to the series s, with a fill value of 0. This results in a series with all dates in the range '09-01-2013' to '09-30-2013', with counts of 0 for dates where no events occurred.

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