Let's do a quick recap of datetime slicing as we discuss some of the additional functionality that the pandas time series have. We can easily isolate data for the year by indexing on it: fb['2018']. In the case of our stock data, the full dataframe would be returned because we only have 2018 data; however, we can filter to a month (fb['2018-10']) or to a range of dates:
>>> fb['2018-10-11':'2018-10-15']
We only get three days back because the stock market is closed on the weekends:
| open | high | low | close | volume | trading_volume | |
|---|---|---|---|---|---|---|
| date | ||||||
| 2018-10-11 | 150.13 | 154.81 | 149.1600 | 153.35 | 35338901 | low |
| 2018-10-12 | 156.73 | 156.89 | 151.2998 | 153.74 | 25293492 | low |
| 2018-10-15 | 153.32 | 155.57 | 152.5500 | 153.52 | 15433521 ... |
