From charlesreid1

(Created page with "Using pandas to do lots of stuff: * load each csv file into a dataframe and create a master csv file * read master csv file and convert all the datetimes to datetime types *...")
 
 
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Using pandas to do lots of stuff:
= all the code=
* load each csv file into a dataframe and create a master csv file
 
* read master csv file and convert all the datetimes to datetime types
The wifi-data repository on Github has all of the code for the operations described here:
*
 
Link: https://github.com/charlesreid1/wifi-data
 
==timestamp extraction==
 
Timestamps.ipynb - ipython notebook that extracts "first time seen" and uses it as the time marker for each observation. This results in a new dataframe (to csv) that contains one item for each observation listed in a csv file.
 
Link: https://github.com/charlesreid1/wifi-data/blob/master/pandas/Timestamps.ipynb
 
==time series==
 
now that the time-indexed data is in pandas, we can more easily visualize it. The TimeSeriesPlots ipython notebook plots several quantities directly from the dataframe.
 
Link: https://github.com/charlesreid1/wifi-data/blob/master/pandas/TimeSeriesPlots.ipynb





Latest revision as of 04:16, 5 March 2016

all the code

The wifi-data repository on Github has all of the code for the operations described here:

Link: https://github.com/charlesreid1/wifi-data

timestamp extraction

Timestamps.ipynb - ipython notebook that extracts "first time seen" and uses it as the time marker for each observation. This results in a new dataframe (to csv) that contains one item for each observation listed in a csv file.

Link: https://github.com/charlesreid1/wifi-data/blob/master/pandas/Timestamps.ipynb

time series

now that the time-indexed data is in pandas, we can more easily visualize it. The TimeSeriesPlots ipython notebook plots several quantities directly from the dataframe.

Link: https://github.com/charlesreid1/wifi-data/blob/master/pandas/TimeSeriesPlots.ipynb