blocks|key|4683639|text|您可以使用plt.xticks显式设置要勾选标记的位置|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4683640|plt.xticks(np.arange(min(x),+max(x)%2B1,+1.0))|code-block|syntax|javascript|4683641|4683642|例如,|4683643|import+numpy+as+np
import+matplotlib.pyplot+as+plt

x+=+[0,5,9,10,15]
y+=+[0,1,2,3,4]
plt.plot(x,y)
plt.xticks(np.arange(min(x),+max(x)%2B1,+1.0))
plt.show()|4683644|4683645|(使用np.arange而不是Python的range函数，只是为了防止min(x)和max(x)是浮点型而不是整型。)|4683646|4683647|plt.plot+(或ax.plot)函数将自动设置默认的x和y限制。如果您希望保留这些限制，并且只更改刻度线的步长，那么您可以使用ax.get_xlim()来发现Matplotlib已经设置了哪些限制。|4683648|start,+end+=+ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start,+end,+stepsize))|4683649|默认的刻度格式化程序应该很好地将刻度值四舍五入为合理的有效位数。但是，如果您希望对格式有更多的控制，您可以定义自己的格式化程序。例如,|4683650|ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%250.1f'))|4683651|下面是一个可运行的示例：|4683652|import+numpy+as+np
import+matplotlib.pyplot+as+plt
import+matplotlib.ticker+as+ticker

x+=+[0,5,9,10,15]
y+=+[0,1,2,3,4]
fig,+ax+=+plt.subplots()
ax.plot(x,y)
start,+end+=+ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start,+end,+0.712123))
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%250.1f'))
plt.show()|4683653|entityMap^0|5|A|0|0|0|0|0|0|3|9|M|5|10|6|17|6|0|0|0|8|B|7|T|1|V|1|1U|D|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|17|8|@$9|18|A|19|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1A|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|1B|8|@]|D|@]|E|$]]|$1|L|3|M|5|6|7|1C|8|@]|D|@]|E|$]]|$1|N|3|O|5|H|7|1D|8|@]|D|@]|E|$I|J]]|$1|P|3|-4|5|6|7|1E|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1F|8|@$9|1G|A|1H|B|C]|$9|1I|A|1J|B|C]|$9|1K|A|1L|B|C]|$9|1M|A|1N|B|C]]|D|@]|E|$]]|$1|S|3|-4|5|6|7|1O|8|@]|D|@]|E|$]]|$1|T|3|U|5|6|7|1P|8|@$9|1Q|A|1R|B|C]|$9|1S|A|1T|B|C]|$9|1U|A|1V|B|C]|$9|1W|A|1X|B|C]|$9|1Y|A|1Z|B|C]]|D|@]|E|$]]|$1|V|3|W|5|H|7|20|8|@]|D|@]|E|$I|J]]|$1|X|3|Y|5|6|7|21|8|@]|D|@]|E|$]]|$1|Z|3|10|5|H|7|22|8|@]|D|@]|E|$I|J]]|$1|11|3|12|5|6|7|23|8|@]|D|@]|E|$]]|$1|13|3|14|5|H|7|24|8|@]|D|@]|E|$I|J]]|$1|15|3|-4|5|6|7|25|8|@]|D|@]|E|$]]]|16|$]]

You could explicitly set where you want to tick marks with <code>plt.xticks</code>:

<pre><code>plt.xticks(np.arange(min(x), max(x)+1, 1.0))
</code></pre>

<hr>

For example,

<pre><code>import numpy as np
import matplotlib.pyplot as plt

x = [0,5,9,10,15]
y = [0,1,2,3,4]
plt.plot(x,y)
plt.xticks(np.arange(min(x), max(x)+1, 1.0))
plt.show()
</code></pre>

<hr>

(<code>np.arange</code> was used rather than Python's <code>range</code> function just in case <code>min(x)</code> and <code>max(x)</code> are floats instead of ints.) 

<hr>

The <code>plt.plot</code> (or <code>ax.plot</code>) function will automatically set default <code>x</code> and <code>y</code> limits. If you wish to keep those limits, and just change the stepsize of the tick marks, then you could use <code>ax.get_xlim()</code> to discover what limits Matplotlib has already set. 

<pre><code>start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, stepsize))
</code></pre>

The default tick formatter should do a decent job rounding the tick values to a sensible number of significant digits. However, if you wish to have more control over the format, you can define your own formatter. For example,

<pre><code>ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
</code></pre>

Here's a runnable example:

<pre><code>import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 0.712123))
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
plt.show()
</code></pre>

blocks|key|4683689|text|另一种方法是设置轴定位器：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4683690|import+matplotlib.ticker+as+plticker

loc+=+plticker.MultipleLocator(base=1.0)+#+this+locator+puts+ticks+at+regular+intervals
ax.xaxis.set_major_locator(loc)|code-block|syntax|javascript|4683691|根据您的需要，有几种不同类型的定位器。|4683692|下面是一个完整的示例：|4683693|import+matplotlib.pyplot+as+plt
import+matplotlib.ticker+as+plticker

x+=+[0,5,9,10,15]
y+=+[0,1,2,3,4]
fig,+ax+=+plt.subplots()
ax.plot(x,y)
loc+=+plticker.MultipleLocator(base=1.0)+#+this+locator+puts+ticks+at+regular+intervals
ax.xaxis.set_major_locator(loc)
plt.show()|4683694|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|R|8|@]|9|@]|A|$]]|$1|K|3|L|5|D|7|S|8|@]|9|@]|A|$E|F]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

Another approach is to set the axis locator:

<pre><code>import matplotlib.ticker as plticker

loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
</code></pre>

There are several different types of locator depending upon your needs.

Here is a full example:

<pre><code>import matplotlib.pyplot as plt
import matplotlib.ticker as plticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
plt.show()
</code></pre>

blocks|key|3042484|text|这是一个古老的话题，但我偶尔会遇到这个问题，于是我做了这个函数。它非常方便：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3042485|import+matplotlib.pyplot+as+pp
import+numpy+as+np

def+resadjust(ax,+xres=None,+yres=None):
++++"""
++++Send+in+an+axis+and+I+fix+the+resolution+as+desired.
++++"""

++++if+xres:
++++++++start,+stop+=+ax.get_xlim()
++++++++ticks+=+np.arange(start,+stop+%2B+xres,+xres)
++++++++ax.set_xticks(ticks)
++++if+yres:
++++++++start,+stop+=+ax.get_ylim()
++++++++ticks+=+np.arange(start,+stop+%2B+yres,+yres)
++++++++ax.set_yticks(ticks)|code-block|syntax|javascript|3042486|像这样控制刻度的一个警告是，在添加一行之后，不再享受最大比例的交互式自动更新。那就这么做|3042487|gca().set_ylim(top=new_top)+#+for+example|3042488|并再次运行重新调整功能。|3042489|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|S|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

This is an old topic, but I stumble over this every now and then and made this function. It's very convenient:

<pre><code>import matplotlib.pyplot as pp
import numpy as np

def resadjust(ax, xres=None, yres=None):
 """
 Send in an axis and I fix the resolution as desired.
 """

 if xres:
 start, stop = ax.get_xlim()
 ticks = np.arange(start, stop + xres, xres)
 ax.set_xticks(ticks)
 if yres:
 start, stop = ax.get_ylim()
 ticks = np.arange(start, stop + yres, yres)
 ax.set_yticks(ticks)
</code></pre>

One caveat of controlling the ticks like this is that one does no longer enjoy the interactive automagic updating of max scale after an added line. Then do

<pre><code>gca().set_ylim(top=new_top) # for example
</code></pre>

and run the resadjust function again.

blocks|key|4772870|text|我开发了一个不优雅的解决方案。考虑到我们有X轴和X中每个点的标签列表。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4772871|示例：|4772872|import+matplotlib.pyplot+as+plt

x+=+[0,1,2,3,4,5]
y+=+[10,20,15,18,7,19]
xlabels+=+['jan','feb','mar','apr','may','jun']|code-block|syntax|javascript|4772873|假设我只想显示'feb‘和'jun’的刻度标签|4772874|xlabelsnew+=+[]
for+i+in+xlabels:
++++if+i+not+in+['feb','jun']:
++++++++i+=+'+'
++++++++xlabelsnew.append(i)
++++else:
++++++++xlabelsnew.append(i)|4772875|很好，现在我们有了一个虚假的标签列表。首先，我们绘制了原始版本。|4772876|plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabels,rotation=45)
plt.show()|4772877|现在，修改后的版本。|4772878|plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabelsnew,rotation=45)
plt.show()|4772879|entityMap^0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|X|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|Y|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|Z|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|10|8|@]|9|@]|A|$G|H]]|$1|M|3|N|5|6|7|11|8|@]|9|@]|A|$]]|$1|O|3|P|5|F|7|12|8|@]|9|@]|A|$G|H]]|$1|Q|3|R|5|6|7|13|8|@]|9|@]|A|$]]|$1|S|3|T|5|F|7|14|8|@]|9|@]|A|$G|H]]|$1|U|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|V|$]]

I developed an inelegant solution. Consider that we have the X axis and also a list of labels for each point in X.

Example:

<pre><code>import matplotlib.pyplot as plt

x = [0,1,2,3,4,5]
y = [10,20,15,18,7,19]
xlabels = ['jan','feb','mar','apr','may','jun']
</code></pre>

Let's say that I want to show ticks labels only for 'feb' and 'jun'

<pre><code>xlabelsnew = []
for i in xlabels:
 if i not in ['feb','jun']:
 i = ' '
 xlabelsnew.append(i)
 else:
 xlabelsnew.append(i)
</code></pre>

Good, now we have a fake list of labels. First, we plotted the original version.

<pre><code>plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabels,rotation=45)
plt.show()
</code></pre>

Now, the modified version.

<pre><code>plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabelsnew,rotation=45)
plt.show()
</code></pre>

blocks|key|4684279|text|这有点老生常谈，但到目前为止，这是我找到的最干净/最容易理解的例子。它来自于SO+here上的答案：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4684280|Cleanest+way+to+hide+every+nth+tick+label+in+matplotlib+colorbar?|offset|length|4684281|for+label+in+ax.get_xticklabels()[::2]:
++++label.set_visible(False)|code-block|syntax|javascript|4684282|然后，您可以遍历标签，将其设置为可见或不可见，具体取决于所需的密度。|4684283|编辑:请注意，有时matplotlib设置标签==+''，因此它可能看起来不存在标签，而实际上它是并且只是不显示任何东西。为了确保循环遍历实际可见的标签，您可以尝试：|style|CODE|4684284|visible_labels+=+[lab+for+lab+in+ax.get_xticklabels()+if+lab.get_visible()+is+True+and+lab.get_text()+!=+'']
plt.setp(visible_labels[::2],+visible=False)|4684285|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/20337664/cleanest-way-to-hide-every-nth-tick-label-in-matplotlib-colorbar^0|0|0|1T|0|0|0|0|Q|2|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|11|8|@]|9|@$D|12|E|13|1|14]]|A|$]]|$1|F|3|G|5|H|7|15|8|@]|9|@]|A|$I|J]]|$1|K|3|L|5|6|7|16|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|17|8|@$D|18|E|19|O|P]]|9|@]|A|$]]|$1|Q|3|R|5|H|7|1A|8|@]|9|@]|A|$I|J]]|$1|S|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z]]]]

This is a bit hacky, but by far the cleanest/easiest to understand example that I've found to do this. It's from an answer on SO here:

<a href="https://stackoverflow.com/questions/20337664/cleanest-way-to-hide-every-nth-tick-label-in-matplotlib-colorbar">Cleanest way to hide every nth tick label in matplotlib colorbar?</a>

<pre><code>for label in ax.get_xticklabels()[::2]:
 label.set_visible(False)
</code></pre>

Then you can loop over the labels setting them to visible or not depending on the density you want.

edit: note that sometimes matplotlib sets labels == <code>''</code>, so it might look like a label is not present, when in fact it is and just isn't displaying anything. To make sure you're looping through actual visible labels, you could try:

<pre><code>visible_labels = [lab for lab in ax.get_xticklabels() if lab.get_visible() is True and lab.get_text() != '']
plt.setp(visible_labels[::2], visible=False)
</code></pre>

blocks|key|3043453|text|我喜欢这个解决方案(来自Matplotlib+Plotting+Cookbook)：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3043454|import+matplotlib.pyplot+as+plt
import+matplotlib.ticker+as+ticker

x+=+[0,5,9,10,15]
y+=+[0,1,2,3,4]

tick_spacing+=+1

fig,+ax+=+plt.subplots(1,1)
ax.plot(x,y)
ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
plt.show()|code-block|syntax|javascript|3043455|这个解决方案让您可以通过给ticker.MultipleLocater()的数字显式地控制刻度间距，允许自动确定限制，并且以后很容易阅读。|style|CODE|3043456|entityMap|0|LINK|mutability|MUTABLE|url|https://www.safaribooksonline.com/library/view/matplotlib-plotting-cookbook/9781849513265/ch03s11.html^0|C|S|0|0|0|D|O|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|F|7|Y|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|Z|8|@$A|10|B|11|K|L]]|9|@]|C|$]]|$1|M|3|-4|5|6|7|12|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]]]

I like this solution (from the <a href="https://www.safaribooksonline.com/library/view/matplotlib-plotting-cookbook/9781849513265/ch03s11.html">Matplotlib Plotting Cookbook</a>):

<pre><code>import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]

tick_spacing = 1

fig, ax = plt.subplots(1,1)
ax.plot(x,y)
ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
plt.show()
</code></pre>

This solution give you explicit control of the tick spacing via the number given to <code>ticker.MultipleLocater()</code>, allows automatic limit determination, and is easy to read later.

blocks|key|2580642|text|如果任何人对一般的一行程序感兴趣，只需获取当前的刻度，并使用它通过每隔一个刻度采样来设置新的刻度。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2580643|ax.set_xticks(ax.get_xticks()[::2])|code-block|syntax|javascript|2580644|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

In case anyone is interested in a general one-liner, simply get the current ticks and use it to set the new ticks by sampling every other tick.

<pre><code>ax.set_xticks(ax.get_xticks()[::2])
</code></pre>

blocks|key|4773927|text|xmarks=[i+for+i+in+range(1,length%2B1,1)]

plt.xticks(xmarks)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4773928|这对我很有效|unstyled|4773929|如果您希望在1,5之间使用记号，那么请替换|offset|length|4773930|length+=+5|4773931|entityMap|0|LINK|mutability|MUTABLE|url|1+and+5+inclusive^0|0|0|6|3|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|V|8|@]|9|@]|A|$]]|$1|G|3|H|5|F|7|W|8|@]|9|@$I|X|J|Y|1|Z]]|A|$]]|$1|K|3|L|5|6|7|10|8|@]|9|@]|A|$B|C]]|$1|M|3|-4|5|F|7|11|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|T]]]]

<pre><code>xmarks=[i for i in range(1,length+1,1)]

plt.xticks(xmarks)
</code></pre>

This worked for me

if you want ticks between [1,5] (1 and 5 inclusive) then replace 

<pre><code>length = 5
</code></pre>

blocks|key|2580721|text|纯Python实现|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2580722|下面是所需功能的纯python实现，它处理具有正值、负值或混合值的任何数值序列(int或float)，并允许用户指定所需的步长：|2580723|import+math

def+computeTicks+(x,+step+=+5):
++++"""
++++Computes+domain+with+given+step+encompassing+series+x
++++@+params
++++x++++-+Required+-+A+list-like+object+of+integers+or+floats
++++step+-+Optional+-+Tick+frequency
++++"""
++++xMax,+xMin+=+math.ceil(max(x)),+math.floor(min(x))
++++dMax,+dMin+=+xMax+%2B+abs((xMax+%25+step)+-+step)+%2B+(step+if+(xMax+%25+step+!=+0)+else+0),+xMin+-+abs((xMin+%25+step))
++++return+range(dMin,+dMax,+step)|code-block|syntax|javascript|2580724|样本输出|2580725|#+Negative+to+Positive
series+=+[-2,+18,+24,+29,+43]
print(list(computeTicks(series)))

[-5,+0,+5,+10,+15,+20,+25,+30,+35,+40,+45]

#+Negative+to+0
series+=+[-30,+-14,+-10,+-9,+-3,+0]
print(list(computeTicks(series)))

[-30,+-25,+-20,+-15,+-10,+-5,+0]

#+0+to+Positive
series+=+[19,+23,+24,+27]
print(list(computeTicks(series)))

[15,+20,+25,+30]

#+Floats
series+=+[1.8,+12.0,+21.2]
print(list(computeTicks(series)))

[0,+5,+10,+15,+20,+25]

#+Step+–+100
series+=+[118.3,+293.2,+768.1]
print(list(computeTicks(series,+step+=+100)))

[100,+200,+300,+400,+500,+600,+700,+800]|2580726|示例用法|2580727|import+matplotlib.pyplot+as+plt

x+=+[0,5,9,10,15]
y+=+[0,1,2,3,4]
plt.plot(x,y)
plt.xticks(computeTicks(x))
plt.show()|2580728|​|2580729|📷|atomic|offset|length|2580730|2580731|请注意，x轴的整数值都以5为间隔均匀分布，而y轴的间隔则不同(+matplotlib的默认行为，因为没有指定刻度)。|style|CODE|2580732|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/20fa27801df762dfccf2a5510c7a4a10.png|imageAlt^0|0|0|0|0|0|0|0|0|0|1|0|0|0|W|A|0^^$0|@$1|2|3|4|5|6|7|1B|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1C|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|1D|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|1E|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|1F|8|@]|9|@]|A|$G|H]]|$1|M|3|N|5|6|7|1G|8|@]|9|@]|A|$]]|$1|O|3|P|5|F|7|1H|8|@]|9|@]|A|$G|H]]|$1|Q|3|R|5|6|7|1I|8|@]|9|@]|A|$]]|$1|S|3|T|5|U|7|1J|8|@]|9|@$V|1K|W|1L|1|1M]]|A|$]]|$1|X|3|R|5|6|7|1N|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|6|7|1O|8|@$V|1P|W|1Q|10|11]]|9|@]|A|$]]|$1|12|3|-4|5|6|7|1R|8|@]|9|@]|A|$]]]|13|$14|$5|15|16|17|A|$18|19|1A|-4]]]]

<h1>Pure Python Implementation</h1>

Below's a pure python implementation of the desired functionality that handles any numeric series (int or float) with positive, negative, or mixed values and allows for the user to specify the desired step size:

<pre><code>import math

def computeTicks (x, step = 5):
 """
 Computes domain with given step encompassing series x
 @ params
 x - Required - A list-like object of integers or floats
 step - Optional - Tick frequency
 """
 xMax, xMin = math.ceil(max(x)), math.floor(min(x))
 dMax, dMin = xMax + abs((xMax % step) - step) + (step if (xMax % step != 0) else 0), xMin - abs((xMin % step))
 return range(dMin, dMax, step)
</code></pre>

<h1>Sample Output</h1>

<pre><code># Negative to Positive
series = [-2, 18, 24, 29, 43]
print(list(computeTicks(series)))

[-5, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45]

# Negative to 0
series = [-30, -14, -10, -9, -3, 0]
print(list(computeTicks(series)))

[-30, -25, -20, -15, -10, -5, 0]

# 0 to Positive
series = [19, 23, 24, 27]
print(list(computeTicks(series)))

[15, 20, 25, 30]

# Floats
series = [1.8, 12.0, 21.2]
print(list(computeTicks(series)))

[0, 5, 10, 15, 20, 25]

# Step – 100
series = [118.3, 293.2, 768.1]
print(list(computeTicks(series, step = 100)))

[100, 200, 300, 400, 500, 600, 700, 800]
</code></pre>

<h1>Sample Usage</h1>

<pre><code>import matplotlib.pyplot as plt

x = [0,5,9,10,15]
y = [0,1,2,3,4]
plt.plot(x,y)
plt.xticks(computeTicks(x))
plt.show()
</code></pre>

<a href="https://i.stack.imgur.com/imxIG.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/imxIG.png" alt="Plot of sample usage"></a>

Notice the x-axis has integer values all evenly spaced by 5, whereas the y-axis has a different interval (the <code>matplotlib</code> default behavior, because the ticks weren't specified).

blocks|key|3043650|text|如果你只想设置一个简单的带有最小样板的内衬的间距：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3043651|plt.gca().xaxis.set_major_locator(plt.MultipleLocator(1))|code-block|syntax|javascript|3043652|对于较小的刻度，也很容易工作：|3043653|plt.gca().xaxis.set_minor_locator(plt.MultipleLocator(1))|3043654|有点塞满了，但相当紧凑|3043655|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|S|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

if you just want to set the spacing a simple one liner with minimal boilerplate:

<pre><code>plt.gca().xaxis.set_major_locator(plt.MultipleLocator(1))
</code></pre>

also works easily for minor ticks:

<pre><code>plt.gca().xaxis.set_minor_locator(plt.MultipleLocator(1))
</code></pre>

a bit of a mouthfull, but pretty compact

blocks|key|4684969|text|由于以上解决方案都不适用于我的用例，因此我在这里提供一个使用None+(双关语！)的解决方案。它可以适应各种各样的场景。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4684970|这是一段在X和Y轴上生成杂乱的刻度的示例代码。|4684971|#+Note+the+super+cluttered+ticks+on+both+X+and+Y+axis.

#+inputs
x+=+np.arange(1,+101)
y+=+x+*+np.log(x)+

fig+=+plt.figure()+++++#+create+figure
ax+=+fig.add_subplot(111)
ax.plot(x,+y)
ax.set_xticks(x)++++++++#+set+xtick+values
ax.set_yticks(y)++++++++#+set+ytick+values

plt.show()|code-block|syntax|javascript|4684972|现在，我们使用一个新的图来清理混乱，该图仅将x和y轴上的稀疏值集显示为刻度。|4684973|#+inputs
x+=+np.arange(1,+101)
y+=+x+*+np.log(x)

fig+=+plt.figure()+++++++#+create+figure
ax+=+fig.add_subplot(111)
ax.plot(x,+y)

ax.set_xticks(x)
ax.set_yticks(y)

#+which+values+need+to+be+shown?
#+here,+we+show+every+third+value+from+`x`+and+`y`
show_every+=+3

sparse_xticks+=+[None]+*+x.shape[0]
sparse_xticks[::show_every]+=+x[::show_every]

sparse_yticks+=+[None]+*+y.shape[0]
sparse_yticks[::show_every]+=+y[::show_every]

ax.set_xticklabels(sparse_xticks,+fontsize=6)+++#+set+sparse+xtick+values
ax.set_yticklabels(sparse_yticks,+fontsize=6)+++#+set+sparse+ytick+values

plt.show()|4684974|根据用例的不同，只需更改show_every并将其用于采样X或Y轴或两个轴的滴答值，就可以调整上面的代码。|4684975|如果这种基于步长的解决方案不适合，那么如果需要的话，还可以以不规则的间隔填充sparse_xticks或sparse_yticks的值。|4684976|entityMap^0|U|4|0|5|1|7|1|0|0|0|0|C|A|0|12|D|1G|D|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|Z|8|@$9|10|A|11|B|C]|$9|12|A|13|B|C]]|D|@]|E|$]]|$1|H|3|I|5|J|7|14|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|15|8|@]|D|@]|E|$]]|$1|O|3|P|5|J|7|16|8|@]|D|@]|E|$K|L]]|$1|Q|3|R|5|6|7|17|8|@$9|18|A|19|B|C]]|D|@]|E|$]]|$1|S|3|T|5|6|7|1A|8|@$9|1B|A|1C|B|C]|$9|1D|A|1E|B|C]]|D|@]|E|$]]|$1|U|3|-4|5|6|7|1F|8|@]|D|@]|E|$]]]|V|$]]

Since None of the above solutions worked for my usecase, here I provide a solution using <code>None</code> (pun!) which can be adapted to a wide variety of scenarios.
Here is a sample piece of code that produces cluttered ticks on both <code>X</code> and <code>Y</code> axes.
<pre><code># Note the super cluttered ticks on both X and Y axis.

# inputs
x = np.arange(1, 101)
y = x * np.log(x) 

fig = plt.figure() # create figure
ax = fig.add_subplot(111)
ax.plot(x, y)
ax.set_xticks(x) # set xtick values
ax.set_yticks(y) # set ytick values

plt.show()
</code></pre>
Now, we clean up the clutter with a new plot that shows only a sparse set of values on both x and y axes as ticks.
<pre><code># inputs
x = np.arange(1, 101)
y = x * np.log(x)

fig = plt.figure() # create figure
ax = fig.add_subplot(111)
ax.plot(x, y)

ax.set_xticks(x)
ax.set_yticks(y)

# which values need to be shown?
# here, we show every third value from `x` and `y`
show_every = 3

sparse_xticks = [None] * x.shape[0]
sparse_xticks[::show_every] = x[::show_every]

sparse_yticks = [None] * y.shape[0]
sparse_yticks[::show_every] = y[::show_every]

ax.set_xticklabels(sparse_xticks, fontsize=6) # set sparse xtick values
ax.set_yticklabels(sparse_yticks, fontsize=6) # set sparse ytick values

plt.show()
</code></pre>
Depending on the usecase, one can adapt the above code simply by changing <code>show_every</code> and using that for sampling tick values for X or Y or both the axes.
If this stepsize based solution doesn't fit, then one can also populate the values of <code>sparse_xticks</code> or <code>sparse_yticks</code> at irregular intervals, if that is what is desired.

blocks|key|4684982|text|通用化的一个线性，只有Numpy导入：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4684983|import+numpy+as+np
ax.set_xticks(np.arange(min(ax.get_xticks()),max(ax.get_xticks()),1));|code-block|syntax|javascript|4684984|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Generalisable one liner, with only Numpy imported:
<pre><code>import numpy as np
ax.set_xticks(np.arange(min(ax.get_xticks()),max(ax.get_xticks()),1));
</code></pre>

blocks|key|2582286|text|您可以遍历标签并显示或隐藏所需的标签：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2582287|+++for+i,+label+in+enumerate(ax.get_xticklabels()):
++++++++if+i+%25+interval+!=+0:
++++++++++++label.set_visible(False)|code-block|syntax|javascript|2582288|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

You can loop through labels and show or hide those you want:
<pre><code> for i, label in enumerate(ax.get_xticklabels()):
 if i % interval != 0:
 label.set_visible(False)
</code></pre>

I am trying to fix how python plots my data.

Say 

<pre><code>x = [0,5,9,10,15]
</code></pre>

and 

<pre><code>y = [0,1,2,3,4]
</code></pre>

Then I would do:

<pre><code>matplotlib.pyplot.plot(x,y)
matplotlib.pyplot.show()
</code></pre>

and the x axis' ticks are plotted in intervals of 5. Is there a way to make it show intervals of 1?

Changing the "tick frequency" on x or y axis in matplotlib?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我正在尝试修复python绘制数据的方式。说x = [0,5,9,10,15]和y = [0,1,2,3,4]然后我会这样做：matplotlib.pyplot.plot(x,y)matplotlib.pyplot.show()X轴的刻度是以5为间隔绘制的，有没有办法让它显示间隔为1呢？

问在matplotlib中更改x或y轴上的“滴答频率”？
EN

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问在matplotlib中更改x或y轴上的“滴答频率”？EN