Reputation: 2510
As I am preparing to do some regressions on a rather big dataset I would like to visualize the data at first.
The data we are talking about is data about the New York subway (hourly entries, rain, weather and such) for May, 2011.
When creating the dataframe I converted hours and time to pandas datetime format.
Now I realize that what I want to do does not make much sense from a logical point of view for the example at hand. However, I would still like to plot the exact time of day against the hourly entries. Which, as I said, is not very meaningful since ENTRIESn_hourly is aggregated. But let's for the sake of the argument assume the ENTRIESn_hourly would be explicitly related to the exact timestamp.
Now how would I go about taking only the times and ignoring the dates and then plot that out?
Please find the jupyter notebook here: https://github.com/FBosler/Udacity/blob/master/Example.ipynb
Thx alot!
Upvotes: 3
Views: 5169
Reputation: 210982
IIUC you can do it this way:
In [9]: weather_turnstile.plot.line(x=weather_turnstile.Date_Time.dt.time, y='ENTRIESn_hourly', marker='o', alpha=0.3)
Out[9]: <matplotlib.axes._subplots.AxesSubplot at 0xc2a63c8>
.dt accessor gives you access to the following attributes:
In [10]: weather_turnstile.Date_Time.dt.
weather_turnstile.Date_Time.dt.ceil weather_turnstile.Date_Time.dt.is_quarter_end weather_turnstile.Date_Time.dt.strftime
weather_turnstile.Date_Time.dt.date weather_turnstile.Date_Time.dt.is_quarter_start weather_turnstile.Date_Time.dt.time
weather_turnstile.Date_Time.dt.day weather_turnstile.Date_Time.dt.is_year_end weather_turnstile.Date_Time.dt.to_period
weather_turnstile.Date_Time.dt.dayofweek weather_turnstile.Date_Time.dt.is_year_start weather_turnstile.Date_Time.dt.to_pydatetime
weather_turnstile.Date_Time.dt.dayofyear weather_turnstile.Date_Time.dt.microsecond weather_turnstile.Date_Time.dt.tz
weather_turnstile.Date_Time.dt.days_in_month weather_turnstile.Date_Time.dt.minute weather_turnstile.Date_Time.dt.tz_convert
weather_turnstile.Date_Time.dt.daysinmonth weather_turnstile.Date_Time.dt.month weather_turnstile.Date_Time.dt.tz_localize
weather_turnstile.Date_Time.dt.floor weather_turnstile.Date_Time.dt.nanosecond weather_turnstile.Date_Time.dt.week
weather_turnstile.Date_Time.dt.freq weather_turnstile.Date_Time.dt.normalize weather_turnstile.Date_Time.dt.weekday
weather_turnstile.Date_Time.dt.hour weather_turnstile.Date_Time.dt.quarter weather_turnstile.Date_Time.dt.weekday_name
weather_turnstile.Date_Time.dt.is_month_end weather_turnstile.Date_Time.dt.round weather_turnstile.Date_Time.dt.weekofyear
weather_turnstile.Date_Time.dt.is_month_start weather_turnstile.Date_Time.dt.second weather_turnstile.Date_Time.dt.year
Upvotes: 4