title: Parse dates and create time series from .csv
body: <p>I am using a simple csv file which contains data on calory intake. It has 4 columns: <code>cal</code>, <code>day</code>, <code>month</code>, year. It looks like this:</p>
  
  <pre><code>cal        month  year  day
  3668.4333      1  2002   10
  3652.2498      1  2002   11
  3647.8662      1  2002   12
  3646.6843      1  2002   13
  ...
  3661.9414      2  2003   14
  
  # data types
  cal      float64
  month      int64
  year       int64
  day        int64
  </code></pre>
  
  <p>I am trying to do some simple time series analysis. I hence would like to parse <code>month</code>, <code>year</code>, and <code>day</code> to a single column. I tried the following using <code>pandas</code>:</p>
  
  <pre><code>import pandas as pd
  from pandas import Series, DataFrame, Panel
  
  data = pd.read_csv('time_series_calories.csv', header=0, pars_dates=['day', 'month', 'year']], date_parser=True, infer_datetime_format=True)
  </code></pre>
  
  <p>My questions are: (1) How do I parse the data and (2) define the data type of the new column? I know there are quite a few other similar questions and answers (see e.g. <a href="https://stackoverflow.com/questions/23797491/parse-dates-in-pandas">here</a>, <a href="https://stackoverflow.com/questions/34146679/python-pandas-csv-parsing">here</a> and <a href="https://stackoverflow.com/questions/30864676/pandas-parse-dates-from-csv">here</a>) - but I can't make it work so far.</p>