In this section, we demonstrate the various ways to read in time series data, starting with the simple read_csv method:

    In [7]: ibmData=pd.read_csv('ibm-common-stock-closing-prices-1959_1960.csv')
      ibmData.head()
    Out[7]:   TradeDate  closingPrice
    0   1959-06-29   445
    1   1959-06-30   448
    2   1959-07-01   450
    3   1959-07-02   447
    4   1959-07-06   451
    5 rows 2 columns

The source of this information can be found at http://datamarket.com.

We would like the TradeDate column to be a series of datetime values so that we can index it and create a time series:

1. Let's first check the type of values in the TradeDate series:

    In [16]: type(ibmData['TradeDate'])
    Out[16]: pandas.core.series.Series
    In [12]: type(ibmData['TradeDate'][0])
    Out[12]: str

2. Next, we convert these values to a Timestamp type:

    In [17]: ibmData['TradeDate']=pd.to_datetime(ibmData['TradeDate'])
            type(ibmData['TradeDate'][0])
    Out[17]: pandas.tslib.Timestamp  

3. We can now use the TradeDate column as an index:

In [113]: #Convert DataFrame to TimeSeries
              #Resampling creates NaN rows for weekend dates, 
             hence use dropna
             ibmTS=ibmData.set_index('TradeDate').resample('D'                      ['closingPrice'].dropna()
        ibmTS
    Out[113]: TradeDate
             1959-06-29    445
             1959-06-30    448
             1959-07-01    450
             1959-07-02    447
      1959-07-06    451
      ...
      Name: closingPrice, Length: 255    

In the next section, we will learn how to assign a date column as an index and then perform subsetting based on the index. For this section, we will use the Object Occupancy dataset where some room parameters were observed every few minutes for several weeks and the corresponding room occupancy was observed. This dataset is present as three separate files.