How can I add rows for all dates between two columns?
<pre><code>import pandas as pd

mydata = [{'ID' : '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016'},
          {'ID' : '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016'}]

mydata2 = [{'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '10/10/2016'},
           {'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '11/10/2016'},
           {'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '12/10/2016'},
           {'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '13/10/2016'},
           {'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '14/10/2016'},
           {'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '15/10/2016'},
           {'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '10/10/2016'},
           {'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '11/10/2016'},
           {'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '12/10/2016'},
           {'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '13/10/2016'},
           {'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '14/10/2016'},
           {'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '15/10/2016'},
           {'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '16/10/2016'},
           {'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '17/10/2016'},
           {'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '18/10/2016'},]

df = pd.DataFrame(mydata)
df2 = pd.DataFrame(mydata2)
</code></pre>

<p>I can't find an answer on how to change 'df' into 'df2'. Maybe I'm not phrasing it right.</p>

<p>I want to take all dates between the dates in two columns 'Entry Date', 'Exit Date', and make a row for each, entering a corresponding date for each row in a new column, 'Date'.</p>

<p>Any help would be greatly appreciated.</p>

<p>You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html" rel="nofollow"><code>melt</code></a> for reshaping, <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.set_index.html" rel="nofollow"><code>set_index</code></a> and remove column <code>variable</code>:</p>

<pre><code>#convert columns to datetime
df['Entry Date'] = pd.to_datetime(df['Entry Date'])
df['Exit Date'] = pd.to_datetime(df['Exit Date'])

df2 = pd.melt(df, id_vars='ID', value_name='Date')
df2.Date = pd.to_datetime(df2.Date)
df2.set_index('Date', inplace=True)
df2.drop('variable', axis=1, inplace=True)
print (df2)
            ID
Date          
2016-10-10  10
2016-10-10  20
2016-10-15  10
2016-10-18  20
</code></pre>

<p>Then <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html" rel="nofollow"><code>groupby</code></a> with <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.DataFrameGroupBy.resample.html" rel="nofollow"><code>resample</code></a> and <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.tseries.resample.Resampler.ffill.html" rel="nofollow"><code>ffill</code></a> missing values:</p>

<pre><code>df3 = df2.groupby('ID').resample('D').ffill().reset_index(level=0, drop=True).reset_index()
print (df3)
         Date  ID
0  2016-10-10  10
1  2016-10-11  10
2  2016-10-12  10
3  2016-10-13  10
4  2016-10-14  10
5  2016-10-15  10
6  2016-10-10  20
7  2016-10-11  20
8  2016-10-12  20
9  2016-10-13  20
10 2016-10-14  20
11 2016-10-15  20
12 2016-10-16  20
13 2016-10-17  20
14 2016-10-18  20
</code></pre>

<p>Last <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.merge.html" rel="nofollow"><code>merge</code></a> original <code>DataFrame</code>:</p>

<pre><code>print (pd.merge(df, df3))
   Entry Date  Exit Date  ID       Date
0  2016-10-10 2016-10-15  10 2016-10-10
1  2016-10-10 2016-10-15  10 2016-10-11
2  2016-10-10 2016-10-15  10 2016-10-12
3  2016-10-10 2016-10-15  10 2016-10-13
4  2016-10-10 2016-10-15  10 2016-10-14
5  2016-10-10 2016-10-15  10 2016-10-15
6  2016-10-10 2016-10-18  20 2016-10-10
7  2016-10-10 2016-10-18  20 2016-10-11
8  2016-10-10 2016-10-18  20 2016-10-12
9  2016-10-10 2016-10-18  20 2016-10-13
10 2016-10-10 2016-10-18  20 2016-10-14
11 2016-10-10 2016-10-18  20 2016-10-15
12 2016-10-10 2016-10-18  20 2016-10-16
13 2016-10-10 2016-10-18  20 2016-10-17
14 2016-10-10 2016-10-18  20 2016-10-18
</code></pre>

python datetime pandas resampling melt