title: Pandas Convert Year/Month Int Columns to Datetime and Quarter Average
body: <p>I have data in a df that is separated into a year and month column and I'm trying to find the average of observed data columns. I cannot find online how to convert the 'year' and 'month' columns to datetime and then to find the Q1, Q2, Q3, etc. averages.</p>
  <pre><code>    year    month   data
  0   2021    1   7.100427005789888
  1   2021    2   7.22523237179488
  2   2021    3   8.301528122415217
  3   2021    4   6.843885683760697
  4   2021    5   6.12365177832918
  5   2021    6   6.049659188034206
  6   2021    7   5.271174524400343
  7   2021    8   5.098493589743587
  8   2021    9   6.260155982906011
  </code></pre>
  <p>I need the final data to look like -</p>
  <pre><code>year    Quarter Q data
  2021    1       7.542395833
  2021    2       6.33906555
  2021    3       5.543274699
  </code></pre>
  <p>I've tried variations of this to change the 'year' and 'month' columns to datetime but it gives a long date starting with year = 1970</p>
  <pre><code>df.iloc[:, 1:2] = df.iloc[:, 1:2].apply(pd.to_datetime)
  
     year                         month  wind_speed_ms
  0  2021 1970-01-01 00:00:00.000000001       7.100427
  1  2021 1970-01-01 00:00:00.000000002       7.225232
  2  2021 1970-01-01 00:00:00.000000003       8.301528
  3  2021 1970-01-01 00:00:00.000000004       6.843886
  4  2021 1970-01-01 00:00:00.000000005       6.123652
  5  2021 1970-01-01 00:00:00.000000006       6.049659
  6  2021 1970-01-01 00:00:00.000000007       5.271175
  7  2021 1970-01-01 00:00:00.000000008       5.098494
  8  2021 1970-01-01 00:00:00.000000009       6.260156
  </code></pre>
  <p>Thank you,</p>