[melbourne-pug] Rounded-value ticks on X axis

Wed Sep 3 04:21:28 CEST 2014

Hi Kevin,

I have taken to using the Python port of ggplot, available at
http://ggplot.yhathq.com/ . I think it's on pip also. It takes pandas
dataframes just fine and provides a syntactically shorter route to graph
tweaking. It's not perfect, but it solved my problems more easily than
getting into the guts of matplotlib. (it is built on top of matplotlib so
you can provide mpl tweaks onto the graphs it produces if you really need
to). Pandas is not really a complete solution for graphing IMO.

Feel free to share some gists of what you are trying to to, I'm happy to
try and help out a little.

Cheers,
-T

On 31 August 2014 16:31, Kevin Shackleton <krshackleton at gmail.com> wrote:

> Unfortunately these matplotlib methods expect the data to be float, where
> using pandas I am able to parse and handle the time series data as
> datetime.  But graphing in pandas does not seem to inherit all the
> matplotlib methods (I suppose that would be a mammoth task, including all
> the method overlays).  I'm forming the opinion that I either do the job in
> numpy/matplotlib with extra code to sculpt the exact format I want or I go
> the concise and elegant pandas way and live with the loss of control of the
> x axis.
>
> Thanks,
>
> Kevin
>
>
> On 31 August 2014 12:01, Anthony Briggs <anthony.briggs at gmail.com> wrote:
>
>> Quick update: http://matplotlib.org/examples/api/date_demo.html and
>> http://stackoverflow.com/questions/3677368/matplotlib-format-axis-offset-values-to-whole-numbers-or-specific-number
>> mention something called a Formatter. DateFormatter from the first link
>> looks like it should do what you're after (convert to a date rather than a
>> time).
>>
>> Anthony
>>
>>
>> <http://stackoverflow.com/questions/3677368/matplotlib-format-axis-offset-values-to-whole-numbers-or-specific-number>
>>
>>
>> On 31 August 2014 13:57, Anthony Briggs <anthony.briggs at gmail.com> wrote:
>>
>>> Hi Kevin,
>>>
>>> Looks like it's just pulling those keys from the data, so something to
>>> convert those explicitly when you read it in would probably be the easiest
>>> way. There might be something in the read_csv function to convert data from
>>> a particular column, or you could try a dictionary comprehension per
>>> station (something along the lines of station_data = {reading['DT'][:10]:
>>> reading for reading in d[stn]}.
>>>
>>> You seem to have a lot of values for the same day though, so you'd
>>> either want to grab just the minimum, or do some sort of formatting in
>>> matplotlib.
>>>
>>> Another method would be to find the minimum value (or minimum 3) for a
>>> station and just report a warning if it's below a certain amount, rather
>>> than a graph which someone has to interpret.
>>>
>>> Final point - having a lot of one and two character variables makes it
>>> really hard to tell what your script is doing.
>>>
>>> Hope that helps,
>>>
>>> Anthony
>>>
>>>
>>>
>>>  On 31 August 2014 11:11, Kevin Shackleton <krshackleton at gmail.com>
>>> wrote:
>>>
>>>>  Hi,
>>>>
>>>> New to Melbourne PUG.  Fairly new to Python.
>>>>
>>>> I have a script that interprets power levels at several automated
>>>> surveying total stations working at a site.  The idea is to show that the
>>>> batteries have enough amp-hours to see the total station through a run of
>>>> cloudy days.  After 2 or 3 years of battery life the battery amp-hour
>>>> capacity will have reduced to a point where the installation will run often
>>>> out of power before the solar panels kick in each morning.  In practice
>>>> this is often affected by 3rd parties hanging extra electrical load on our
>>>> installation.
>>>>
>>>> The data looks like this:
>>>> 2014-06-16T18:40:20,HUT1,56
>>>> 2014-06-16T19:02:49,HUT2,15
>>>> 2014-06-16T20:16:12,HUT1,58
>>>> 2014-06-16T20:17:08,HUT2,11
>>>> 2014-06-16T20:51:17,HUT1,67
>>>> 2014-06-17T11:51:05,HUT1,100
>>>> 2014-06-17T11:51:07,HUT2,48
>>>> 2014-06-17T11:51:08,HUT3,57
>>>> where power level readings are coming from each of the huts (there are
>>>> actually 4 of them) at random times and with different data densities per
>>>> hut according to the cycle schedules.  The power levels are percent.  Some
>>>> total stations report in volts but that's another problem.
>>>>
>>>> My script solution is:
>>>> import pandas as pd
>>>> import matplotlib.pyplot as plt
>>>> df = pd.read_csv('Power_Log.csv',names=['DT','Station','Power'])
>>>> df2=df.groupby(['Station']) # set 'Station' as the data index
>>>> d = dict(iter(df2)) # make a dictionary including each station's data
>>>> for stn in d.keys():
>>>>     plt.figure() # creates a new plot canvas
>>>>     fig, ax = plt.subplots() # creates components of the plot
>>>>     ax.set_ylabel('Power (%)',fontsize=12)
>>>>     fig.subplots_adjust(bottom=0.15)
>>>>     d[stn].interpolate().plot(x='DT',y='Power',rot=15,title='Power
>>>> Level: ' + stn)
>>>>     ax.set_xlabel('Date-Time',fontsize=12)
>>>>     plt.savefig('Station_Power_' + stn + '.png')
>>>>
>>>> Possibly a bit wasteful going from dataframe to dataframe to dictionary.
>>>>
>>>> This code knocks out nice graphs, except for one thing - the x ticks
>>>> are at unrounded positions and therefore have long labels.
>>>>
>>>> I'm getting nowhere with set_major_formatter and autofmt_xdate methods
>>>> to try to set the ticks to rounded days.
>>>>
>>>> And brilliant ideas here?
>>>>
>>>> Thanks,
>>>>
>>>> Kevin.
>>>>
>>>> _______________________________________________
>>>> melbourne-pug mailing list
>>>> melbourne-pug at python.org
>>>> https://mail.python.org/mailman/listinfo/melbourne-pug
>>>>
>>>>
>>>
>>
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