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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:

I am new here and don't have privilege to post pictures. So please click on it

This is what I need, but it has 1 issue: weekends. They are present on x axis and make my chart a little ugly.

please click to see picture

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.

Quang Hoang
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Igor K.
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2 Answers2

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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

Nic
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to set the x ticks assuming that you have the dates variable in dataframe row df['dates']

ax.xaxis.set_ticks(df['dates'])
Nic Wanavit
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