{"cells":[{"cell_type":"markdown","source":["# Time Series\n","\n","```{jupyter-info}\n","{rel-data-download}`bicycles.csv`\n","```\n","\n","We start by loading the biycycles dataset into a `DataFrame` using the same code as in the last slide to index the data by the date."],"metadata":{}},{"cell_type":"code","execution_count":1,"source":["# Some setup code to get the plotting library correct\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","\n","%matplotlib inline"],"outputs":[],"metadata":{}},{"cell_type":"code","execution_count":2,"source":["df = pd.read_csv('bicycles.csv', \n"," index_col='Date', parse_dates=True)\n","\n","# Sorts the rows so the index is sorted\n","df = df.sort_index() \n","\n","df # For display"],"outputs":[{"output_type":"execute_result","data":{"text/plain":[" East West\n","Date \n","2012-10-03 00:00:00 4.0 9.0\n","2012-10-03 01:00:00 4.0 6.0\n","2012-10-03 02:00:00 1.0 1.0\n","2012-10-03 03:00:00 2.0 3.0\n","2012-10-03 04:00:00 6.0 1.0\n","... ... ...\n","2019-03-31 19:00:00 30.0 58.0\n","2019-03-31 20:00:00 26.0 31.0\n","2019-03-31 21:00:00 18.0 15.0\n","2019-03-31 22:00:00 7.0 14.0\n","2019-03-31 23:00:00 6.0 10.0\n","\n","[56904 rows x 2 columns]"],"text/html":["
\n"," | East | \n","West | \n","
---|---|---|
Date | \n","\n"," | \n"," |
2012-10-03 00:00:00 | \n","4.0 | \n","9.0 | \n","
2012-10-03 01:00:00 | \n","4.0 | \n","6.0 | \n","
2012-10-03 02:00:00 | \n","1.0 | \n","1.0 | \n","
2012-10-03 03:00:00 | \n","2.0 | \n","3.0 | \n","
2012-10-03 04:00:00 | \n","6.0 | \n","1.0 | \n","
... | \n","... | \n","... | \n","
2019-03-31 19:00:00 | \n","30.0 | \n","58.0 | \n","
2019-03-31 20:00:00 | \n","26.0 | \n","31.0 | \n","
2019-03-31 21:00:00 | \n","18.0 | \n","15.0 | \n","
2019-03-31 22:00:00 | \n","7.0 | \n","14.0 | \n","
2019-03-31 23:00:00 | \n","6.0 | \n","10.0 | \n","
56904 rows × 2 columns
\n","\n"," | East | \n","West | \n","
---|---|---|
Date | \n","\n"," | \n"," |
2017-03-31 00:00:00 | \n","2.0 | \n","4.0 | \n","
2017-03-31 01:00:00 | \n","2.0 | \n","0.0 | \n","
2017-03-31 02:00:00 | \n","1.0 | \n","0.0 | \n","
2017-03-31 03:00:00 | \n","1.0 | \n","0.0 | \n","
2017-03-31 04:00:00 | \n","3.0 | \n","3.0 | \n","
2017-03-31 05:00:00 | \n","23.0 | \n","14.0 | \n","
2017-03-31 06:00:00 | \n","57.0 | \n","49.0 | \n","
2017-03-31 07:00:00 | \n","163.0 | \n","99.0 | \n","
2017-03-31 08:00:00 | \n","250.0 | \n","162.0 | \n","
2017-03-31 09:00:00 | \n","90.0 | \n","70.0 | \n","
2017-03-31 10:00:00 | \n","52.0 | \n","38.0 | \n","
2017-03-31 11:00:00 | \n","39.0 | \n","31.0 | \n","
2017-03-31 12:00:00 | \n","37.0 | \n","37.0 | \n","
2017-03-31 13:00:00 | \n","52.0 | \n","34.0 | \n","
2017-03-31 14:00:00 | \n","45.0 | \n","55.0 | \n","
2017-03-31 15:00:00 | \n","74.0 | \n","87.0 | \n","
2017-03-31 16:00:00 | \n","83.0 | \n","223.0 | \n","
2017-03-31 17:00:00 | \n","145.0 | \n","333.0 | \n","
2017-03-31 18:00:00 | \n","117.0 | \n","206.0 | \n","
2017-03-31 19:00:00 | \n","50.0 | \n","84.0 | \n","
2017-03-31 20:00:00 | \n","26.0 | \n","36.0 | \n","
2017-03-31 21:00:00 | \n","16.0 | \n","40.0 | \n","
2017-03-31 22:00:00 | \n","12.0 | \n","22.0 | \n","
2017-03-31 23:00:00 | \n","5.0 | \n","15.0 | \n","
\n"," | East | \n","West | \n","
---|---|---|
Date | \n","\n"," | \n"," |
2017-03-01 00:00:00 | \n","1.0 | \n","2.0 | \n","
2017-03-01 01:00:00 | \n","2.0 | \n","2.0 | \n","
2017-03-01 02:00:00 | \n","1.0 | \n","1.0 | \n","
2017-03-01 03:00:00 | \n","1.0 | \n","0.0 | \n","
2017-03-01 04:00:00 | \n","3.0 | \n","3.0 | \n","
... | \n","... | \n","... | \n","
2017-03-31 19:00:00 | \n","50.0 | \n","84.0 | \n","
2017-03-31 20:00:00 | \n","26.0 | \n","36.0 | \n","
2017-03-31 21:00:00 | \n","16.0 | \n","40.0 | \n","
2017-03-31 22:00:00 | \n","12.0 | \n","22.0 | \n","
2017-03-31 23:00:00 | \n","5.0 | \n","15.0 | \n","
744 rows × 2 columns
\n","\n"," | East | \n","West | \n","
---|---|---|
Date | \n","\n"," | \n"," |
2017-01-01 00:00:00 | \n","0.0 | \n","5.0 | \n","
2017-01-01 01:00:00 | \n","5.0 | \n","14.0 | \n","
2017-01-01 02:00:00 | \n","1.0 | \n","0.0 | \n","
2017-01-01 03:00:00 | \n","0.0 | \n","2.0 | \n","
2017-01-01 04:00:00 | \n","0.0 | \n","1.0 | \n","
... | \n","... | \n","... | \n","
2017-12-31 19:00:00 | \n","9.0 | \n","12.0 | \n","
2017-12-31 20:00:00 | \n","6.0 | \n","8.0 | \n","
2017-12-31 21:00:00 | \n","3.0 | \n","10.0 | \n","
2017-12-31 22:00:00 | \n","7.0 | \n","6.0 | \n","
2017-12-31 23:00:00 | \n","7.0 | \n","9.0 | \n","
8760 rows × 2 columns
\n","\n"," | East | \n","West | \n","
---|---|---|
Date | \n","\n"," | \n"," |
2017-01-01 00:00:00 | \n","0.0 | \n","5.0 | \n","
2017-01-01 01:00:00 | \n","5.0 | \n","14.0 | \n","
2017-01-01 02:00:00 | \n","1.0 | \n","0.0 | \n","
2017-01-01 03:00:00 | \n","0.0 | \n","2.0 | \n","
2017-01-01 04:00:00 | \n","0.0 | \n","1.0 | \n","
... | \n","... | \n","... | \n","
2018-12-31 19:00:00 | \n","9.0 | \n","5.0 | \n","
2018-12-31 20:00:00 | \n","12.0 | \n","14.0 | \n","
2018-12-31 21:00:00 | \n","7.0 | \n","7.0 | \n","
2018-12-31 22:00:00 | \n","3.0 | \n","4.0 | \n","
2018-12-31 23:00:00 | \n","7.0 | \n","6.0 | \n","
17520 rows × 2 columns
\n","