Is there an easy way to draw a date/value chart in Python, if the "dates" axis had non-equidistant values?
For example, given these:
2009-02-01: 10
2009-02-02: 13
2009-02-07: 25
2009-03-01: 80
I'd like the chart to show that between the 2nd and 3nd value there's a longer gap than between the 1st and the 2nd.
I tried a few chart libraries but they all seem to assume that the X axis has non-scalar values..
(Side note: the chart should be exportable to PNG/GIF/whatever)
Thanks for your time!
you should be able to do this with matplotlib barchart. you can use xticks to give the x-axis date values, and the 'left' sizes don't have to be homogeneous. see the documentation for barchart for a full list of parameters.
If it is enough for you to get a PNG with the chart, you can use Google Chart API which will give you a PNG image you can save. You could also use your data to parse the URL using Python.
EDIT: Image URL for your example (very basic).
Related
I need to draw additional graphics on top of plotly go.Box traces, therefore I need to know X and Y coordinates for boxplot rectangle vertices. So far the only solution I came up with is basically recalculating everything (quartiles; X positions based on boxgap, boxgroupgap, etc.), then manually setting the y-axis range to know where everything will end up on the plot. This seems very cumbersome.
Is there a way in python to get the coordinates of go.Box boxplot elements, especially the grouped boxplots with categorical x-axis? As far as I understand these coordinates are calculated in JS frontend -- maybe there is some trick to get them back with Dash using callbacks?
I am trying to plot chart in my data science project. And I want to plot chart in this way
which is produced by Matplotlib library. And now I want to plot in the same way. But it gives me this way in bokeh.
My data for this in the form of python list and it is as below:
ages = [45.0,50.0,55.0,40.0,60.0,35.0,65.0,30.0,70.0,25.0,75.0,20.0,80.0,85.0,15.0,90.0,10.0,0.0]
ageCounter = [4466,4270,3824,3576,3240,2850,2527,2358,1968,1544,981,655,419,149,132,80,17,2]
Is there any thing I am missing.
Thanks in advance.
You need to sort the arrays together by age. Bokeh plots the points in the order you pass them in. The data above is out of order which explains the "zigzag".
This is not duplicate, because existing answers on similar questions don't describe exactly what I need.
Matplotlib has great formatters inside and I love to use them:
ax.xaxis.set_major_locator(matplotlib.dates.MonthLocator())
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%b%y'))
They let me plot such stock market charts:
This is what I need, but it has 1 issue: weekends. They are present on x axis and make my chart a little ugly.
Other questions about this issue give advice to create custom formatter. They show examples of such formatters. But no one of them do pretty formatting like matplotlib do:
May19, Jun19, Jul19...
I mean this line of code:
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%b%y'))
My question is: please help me to format x axis like matplotlib do: May19, Jun19, Jul19... and don't create weekends when stock market is closed.
What you could almost always do is something similar to what Nic Wanavit suggested.
Manually set your labels, depending on what you need on your axis.
Especially in this case the plot is looking a bit ugly because you have timespans in your data that are not provided with actual data (the weekends in this case) so pyplot will simply connect these points with the corresponding length from the x-axis.
What you can do then is just to plot your data equally distant - which is correct if the data is daily - otherwise consider to interpolate it using e.g. pandas bultin interpolation.
To avoid pyplot automatically detect the index I had to do this:
df['plotidx'] = [i for i in range(len(df['close'])):
Here all the closing values for the stock are stored in a column named 'close' obvsl.
You plot this correspondingly.
Then you can obtain all the ticks created via
labels = [item.get_text() for item in ax.get_xticklabels()]
Adjust them as desired with
labels[i] = string_for_the_label_no_i
Then get them back on the graph using
ax.xaxis.set_ticklabels(labels)
You need to somewhat "update" the plot then. Also keep in mind, that resizing a lot could end up with the labels being as also said in the documentation strange location.
It is some kind of a workaround but worked fine for me because it feels natural to plot data equally distant next to each other rather then making up some data for the weekends.
Greets
to set the x ticks
assuming that you have the dates variable in dataframe row df['dates']
ax.xaxis.set_ticks(df['dates'])
I currently annotate my charts with the last value of each series by adding a Label and supplying my the name of corresponding range it's plotted on:
Label(
...
x=data.index.max(),
y=data.loc[data.index.max(), 'my_col'],
y_range_name='my_range'
...
)
Which gives me:
How do I move the labels so they are positioned on their respective axis?
Example:
Please note that my labels' y-positioning is off, so I need some help with that aspect too. I've tried tweaking the y_offset but this has not yielded any consistently good results.
My data are always numerical time series.
As of Bokeh 1.2 there is no built-in annotation or glyph that will display outside the central plot area. There is an open issue on GitHub that this is similar to that you can follow or comment on. For the time being, something like this would require making a custom extension
I am trying to configure my Bokeh plots in Python such that they look a bit nicer. For example, is there a way to fix the maximum zoom out? Such that Bokeh cannot zoom out more than what is specified by the x-axis? E.g. look at bokeh example, and especially "Datetime axes". I would like to fix the axis size so that you cannot zoom out more than the initial x axis is wide.
Another question; is there a way to fill an area under a curve in a specified color? Like in the figure USDSEK. I can provide code, but I don't think it's necessary for the problem at hand.
UPDATED for 2019:
Bokeh now supports "directed areas" (which can also be stacked) see e.g.
https://docs.bokeh.org/en/latest/docs/gallery/stacked_area.html