Matplotlib显示重叠的x-tick标签 [英] Matplotlib showing x-tick labels overlapping

问题描述

看看下面的图:

这是这个较大数字的子图:

It's a subplot of this larger figure:

我看到了两个问题.首先，x轴标签相互重叠(这是我的主要问题).第二. x轴次方网格线的位置似乎有点奇怪.在图的左侧，它们看起来间隔适当.但是在右边，他们似乎拥挤了主要网格线……好像主要网格线的位置不是次要刻度线位置的适当倍数.

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.

我的设置是，我有一个名为df的DataFrame，它的行上有一个DatetimeIndex，还有一个名为value的列，其中包含浮点数.如有必要，我可以根据要点提供df内容的示例.这篇文章的底部有一打df行，以供参考.

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.

以下是生成图形的代码:

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)

在这里，使x轴标签停止相互重叠(在四个子图中的每一个中)，我的最佳选择是什么?另外，单独(但不那么紧急)在左上角子图中的次要滴答问题是怎么回事?

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?

我使用的是Pandas 0.13.1，numpy 1.8.0和matplotlib 1.4.x.

I am on Pandas 0.13.1, numpy 1.8.0, and matplotlib 1.4.x.

下面是一小段df供参考:

                                    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

尝试fig.autofmt_xdate(): 我认为这不会成功.这似乎在左侧的两个图和右侧的两个图都使用相同的x-tick标签.鉴于我的数据，这是不正确的.请查看以下有问题的输出:

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:

推荐答案

好，终于可以使用了.诀窍是使用plt.setp手动旋转刻度线标签.使用fig.autofmt_xdate()无效，因为当图形中有多个子图时，它会执行一些意外的操作.这是工作代码及其输出:

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()

顺便说一下，前面有关永久删除某些matplotlib东西的评论在这里非常有趣.我正在使用树莓派充当远程位置的气象站.它通过网络收集数据并提供结果.而且，天哪，尝试发布这些图形真是令人喘不过气.

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.

这篇关于Matplotlib显示重叠的x-tick标签的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！