pandas lookup daily series value for half-hour dataframe index

Question

I have a pandas dataframe with a half-hour timeseries index and a series of daily data that I need to match based on date for an equation. The following code works using .get() in a loop but is slow and seems rather "unpythonic." I've tried turning the series into a dataframe with a dummy column in order to try merge or lookup but for various reasons I can't get that to work. There is missing data so key errors are possible with some potential methods. Previously answered questions don't seem to apply. Someone good with lambda functions or the .asfreq method might be able to come up with something. ``` import pandas as pd import numpy as np # Make a 2 day series days = 2 dates = pd.date_range('20130102',periods=days) ts_d = pd.Series(np.random.randn(days),index=dates) ts_d # Output 2013-01-02 -1.044139 2013-01-03 -1.061720 Freq: D, dtype: float64 # Make an overlapping 4 day dataframe with 60min index datetimes = pd.date_range('20130101 00:00',periods=4*24, freq = '60min') df_t = pd.Da…

Accepted Answer

you can do this pandathonically:

first, get the date-only field:

```
df_t['Date'] = pd.to_datetime(df_t.index.date)
```

set as index:

```
df_t = df_t.reset_index().set_index('Date')
```

set the price:

```
df_t['E'] = ts_d
```

reset old index:

```
df_t = df_t.reset_index().set_index('index')
```

verify:

```
df_t.ix[pd.to_datetime('20130102')]
```

*edit: changed to incorporate jeff's suggestions

pandas lookup daily series value for half-hour dataframe index

Solution

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