Here, we introduce several important functionalities that we are going to use in the rest of the chapters. The Series() function included in the Pandas module would help us to generate time series. When dealing with time series, the most important variable is date. This is why we explain the date variable in more detail. Data.Frame is used intensively in Python and other languages, such as R.
We could easily use the pd.Series() function to generate our time series; refer to the following example:
>>>import pandas as pd >>>x = pd.date_range('1/1/2013', periods=252) >>>data = pd.Series(randn(len(x)), index=x) >>>data.head() 2013-01-01 0.776670 2013-01-02 0.128904 2013-01-03 -0.064601 2013-01-04 0.988347 2013-01-05 0.459587 Freq: D, dtype: float64 >>>data.tail() 2013-09-05 -0.167599 2013-09-06 0.530864 2013-09-07 1.378951 2013-09-08 ...