Have a look at the graph below:
It’s a subplot of this larger figure:
I see two problems with it. First, the x-axis labels overlap with one another (this is my major issue). Second. the location of the x-axis minor gridlines seems a bit wonky. On the left of the graph, they look properly spaced. But on the right, they seem to be crowding the major gridlines…as if the major gridline locations aren’t proper multiples of the minor tick locations.
My setup is that I have a DataFrame called df which has a DatetimeIndex on the rows and a column called value which contains floats. I can provide an example of the df contents in a gist if necessary. A dozen or so lines of df are at the bottom of this post for reference.
Here’s the code that produces the figure:
now = dt.datetime.now()
fig, axes = plt.subplots(2, 2, figsize=(15, 8), dpi=200)
for i, d in enumerate([360, 30, 7, 1]):
ax = axes.flatten()[i]
earlycut = now - relativedelta(days=d)
data = df.loc[df.index>=earlycut, :]
ax.plot(data.index, data['value'])
ax.xaxis_date()
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', color='w', linewidth=1.5)
ax.grid(b=True, which='minor', color='w', linewidth=0.75)
What is my best option here to get the x-axis labels to stop overlapping each other (in each of the four subplots)? Also, separately (but less urgently), what’s up with the minor tick issue in the top-left subplot?
I am on Pandas 0.13.1, numpy 1.8.0, and matplotlib 1.4.x.
Here’s a small snippet of df for reference:
id scale tempseries_id value timestamp 2014-11-02 14:45:10.302204+00:00 7564 F 1 68.0000 2014-11-02 14:25:13.532391+00:00 7563 F 1 68.5616 2014-11-02 14:15:12.102229+00:00 7562 F 1 68.9000 2014-11-02 14:05:13.252371+00:00 7561 F 1 69.0116 2014-11-02 13:55:11.792191+00:00 7560 F 1 68.7866 2014-11-02 13:45:10.782227+00:00 7559 F 1 68.6750 2014-11-02 13:35:10.972248+00:00 7558 F 1 68.4500 2014-11-02 13:25:10.362213+00:00 7557 F 1 68.1116 2014-11-02 13:15:10.822247+00:00 7556 F 1 68.2250 2014-11-02 13:05:10.102200+00:00 7555 F 1 68.5616 2014-11-02 12:55:10.292217+00:00 7554 F 1 69.0116 2014-11-02 12:45:10.382226+00:00 7553 F 1 69.3500 2014-11-02 12:35:10.642245+00:00 7552 F 1 69.2366 2014-11-02 12:25:12.642255+00:00 7551 F 1 69.1250 2014-11-02 12:15:11.122382+00:00 7550 F 1 68.7866 2014-11-02 12:05:11.332224+00:00 7549 F 1 68.5616 2014-11-02 11:55:11.662311+00:00 7548 F 1 68.2250 2014-11-02 11:45:11.122193+00:00 7547 F 1 68.4500 2014-11-02 11:35:11.162271+00:00 7546 F 1 68.7866 2014-11-02 11:25:12.102211+00:00 7545 F 1 69.2366 2014-11-02 11:15:10.422226+00:00 7544 F 1 69.4616 2014-11-02 11:05:11.412216+00:00 7543 F 1 69.3500 2014-11-02 10:55:10.772212+00:00 7542 F 1 69.1250 2014-11-02 10:45:11.332220+00:00 7541 F 1 68.7866 2014-11-02 10:35:11.332232+00:00 7540 F 1 68.5616 2014-11-02 10:25:11.202411+00:00 7539 F 1 68.2250 2014-11-02 10:15:11.932326+00:00 7538 F 1 68.5616 2014-11-02 10:05:10.922229+00:00 7537 F 1 68.9000 2014-11-02 09:55:11.602357+00:00 7536 F 1 69.3500
Edit: Trying fig.autofmt_xdate():
I don’t think this going to do the trick. This seems to use the same x-tick labels for both graphs on the left and also for both graphs on the right. Which is not correct given my data. Please see the problematic output below:
Answers:
Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), leave a comment & I’ll get back to you as soon as possible.
Method 1
Ok, finally got it working. The trick was to use plt.setp to manually rotate the tick labels. Using fig.autofmt_xdate() did not work as it does some unexpected things when you have multiple subplots in your figure. Here’s the working code with its output:
for i, d in enumerate([360, 30, 7, 1]):
ax = axes.flatten()[i]
earlycut = now - relativedelta(days=d)
data = df.loc[df.index>=earlycut, :]
ax.plot(data.index, data['value'])
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', color='w', linewidth=1.5)
ax.grid(b=True, which='minor', color='w', linewidth=0.75)
plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')
fig.tight_layout()
By the way, the comment earlier about some matplotlib things taking forever is very interesting here. I’m using a raspberry pi to act as a weather station at a remote location. It’s collecting the data and serving the results via the web. And boy oh boy, it’s really wheezing trying to put out these graphics.
Method 2
Due to the way text rendering is handled in matplotlib, auto-detecting overlapping text really slows things down. (The space that text takes up can’t be accurately calculated until after it’s been drawn.) For that reason, matplotlib doesn’t try to do this automatically.
Therefore, it’s best to rotate long tick labels. Because dates most commonly have this problem, there’s a figure method fig.autofmt_xdate() that will (among other things) rotate the tick labels to make them a bit more readable. (Note: If you’re using a pandas plot method, it returns an axes object, so you’ll need to use ax.figure.autofmt_xdate().)
As a quick example:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
time = pd.date_range('01/01/2014', '4/01/2014', freq='H')
values = np.random.normal(0, 1, time.size).cumsum()
fig, ax = plt.subplots()
ax.plot_date(time, values, marker='', linestyle='-')
fig.autofmt_xdate()
plt.show()
If we were to leave fig.autofmt_xdate() out:
And if we use fig.autofmt_xdate():
Method 3
For the problems which don’t have date values in x axis, rather a string, you can insert n character in x axis values so they don’t overlap. Here is an example –
The data frame is
somecol value category 1 of column 16 category 2 of column 13 category 3 of column 21 category 4 of column 20 category 5 of column 11 category 6 of column 22 category 7 of column 19 category 8 of column 14 category 9 of column 18 category 10 of column 23 category 11 of column 10 category 12 of column 24 category 13 of column 17 category 14 of column 15 category 15 of column 12
I need to plot value on y axis and somecol on x axis, which will normally be plotted like this –
As you can see, there is a lot of overlap. Now introduce n character in somecol column.
somecol = df['somecol'].values.tolist()
for i in range(len(somecol)):
x = somecol[i].split(' ')
# insert n before 'of'
x.insert(x.index('of'),'n')
somecol[i] = ' '.join(x)
Now if you plot, it will look like this –
plt.plot(somecol, df['val'])
This method works well if you don’t want to rotate your labels.
The only con so far I found in this method is that you need to tweak your labels 3-4 times i.e., try with multiple formats to display the plot in best format.
All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0