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copied!<p>You can just use reindex on a time series using your date range. Also it looks like you would be better off using a TimeSeries instead of a DataFrame (see <a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html" rel="noreferrer">documentation</a>), although reindexing is also the correct method for adding missing index values to DataFrames as well.</p> <p>For example, starting with:</p> <pre><code>date_index = pd.DatetimeIndex([pd.datetime(2003,6,24), pd.datetime(2003,8,13), pd.datetime(2003,8,19), pd.datetime(2003,8,22), pd.datetime(2003,8,24)]) ts = pd.Series([2,1,2,1,5], index=date_index) </code></pre> <p>Gives you a time series like your example dataframe's head:</p> <pre><code>2003-06-24 2 2003-08-13 1 2003-08-19 2 2003-08-22 1 2003-08-24 5 </code></pre> <p>Simply doing </p> <pre><code>ts.reindex(pd.date_range(min(date_index), max(date_index))) </code></pre> <p>then gives you a complete index, with NaNs for your missing values (you can use <code>fillna</code> if you want to fill the missing values with some other values - see <a href="http://pandas.pydata.org/pandas-docs/stable/missing_data.html#missing-data-fillna" rel="noreferrer">here</a>):</p> <pre><code>2003-06-24 2 2003-06-25 NaN 2003-06-26 NaN 2003-06-27 NaN 2003-06-28 NaN 2003-06-29 NaN 2003-06-30 NaN 2003-07-01 NaN 2003-07-02 NaN 2003-07-03 NaN 2003-07-04 NaN 2003-07-05 NaN 2003-07-06 NaN 2003-07-07 NaN 2003-07-08 NaN 2003-07-09 NaN 2003-07-10 NaN 2003-07-11 NaN 2003-07-12 NaN 2003-07-13 NaN 2003-07-14 NaN 2003-07-15 NaN 2003-07-16 NaN 2003-07-17 NaN 2003-07-18 NaN 2003-07-19 NaN 2003-07-20 NaN 2003-07-21 NaN 2003-07-22 NaN 2003-07-23 NaN 2003-07-24 NaN 2003-07-25 NaN 2003-07-26 NaN 2003-07-27 NaN 2003-07-28 NaN 2003-07-29 NaN 2003-07-30 NaN 2003-07-31 NaN 2003-08-01 NaN 2003-08-02 NaN 2003-08-03 NaN 2003-08-04 NaN 2003-08-05 NaN 2003-08-06 NaN 2003-08-07 NaN 2003-08-08 NaN 2003-08-09 NaN 2003-08-10 NaN 2003-08-11 NaN 2003-08-12 NaN 2003-08-13 1 2003-08-14 NaN 2003-08-15 NaN 2003-08-16 NaN 2003-08-17 NaN 2003-08-18 NaN 2003-08-19 2 2003-08-20 NaN 2003-08-21 NaN 2003-08-22 1 2003-08-23 NaN 2003-08-24 5 Freq: D, Length: 62 </code></pre>

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