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    <p><em>Note: Now that Series have the dt accessor it's less important that date is the index, though Date/Time still needs to be a datetime64.</em></p> <p>Update: You can do the groupby more directly (without the lambda):</p> <pre><code>In [21]: df.groupby([df["Date/Time"].dt.year, df["Date/Time"].dt.hour]).mean() Out[21]: Value Date/Time Date/Time 2010 0 60 1 50 2 52 3 49 In [22]: res = df.groupby([df["Date/Time"].dt.year, df["Date/Time"].dt.hour]).mean() In [23]: res.index.names = ["year", "hour"] In [24]: res Out[24]: Value year hour 2010 0 60 1 50 2 52 3 49 </code></pre> <p>If it's a datetime64 <em>index</em> you can do:</p> <pre><code>In [31]: df1.groupby([df1.index.year, df1.index.hour]).mean() Out[31]: Value 2010 0 60 1 50 2 52 3 49 </code></pre> <hr> <p>Old answer (will be slower):</p> <p>Assuming Date/Time was the index* you can use a mapping function in the <a href="http://pandas.pydata.org/pandas-docs/stable/groupby.html" rel="noreferrer">groupby</a>:</p> <pre><code>In [11]: year_hour_means = df1.groupby(lambda x: (x.year, x.hour)).mean() In [12]: year_hour_means Out[12]: Value (2010, 0) 60 (2010, 1) 50 (2010, 2) 52 (2010, 3) 49 </code></pre> <p>For a more useful index, you could then create a MultiIndex from the tuples:</p> <pre><code>In [13]: year_hour_means.index = pd.MultiIndex.from_tuples(year_hour_means.index, names=['year', 'hour']) In [14]: year_hour_means Out[14]: Value year hour 2010 0 60 1 50 2 52 3 49 </code></pre> <p>* if not, then first use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.set_index.html" rel="noreferrer"><code>set_index</code></a>:</p> <pre><code>df1 = df.set_index('Date/Time') </code></pre>
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    1. POGetting the average of a certain hour on weekdays over several years in a pandas dataframe
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    1. USAndy Hayden
    1. USAndy Hayden
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    1. POGetting the average of a certain hour on weekdays over several years in a pandas dataframe
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    1. VO
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    1. COThanks a lot. I had been trying with loops but this is a much better way.
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      1. USMarkus W
    2. COP.S.: Does anybody how you can fill the "x.year" or "x.hour" of "df1.groupby(lambda x: (x.year, x.hour)).mean() " as a dynamic parameter into the lamda function? Defining Varialbe1=x.year and Variable2=x.hour for this "df1.groupby(lambda x: (Variable1, Variable2)).mean() " does not seem to work.
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      1. USMarkus W
    3. CO@MarkusW You should ask that as a new question :)... it sounds like you want to use a proper function (i.e. not a lambda)
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