blocks|key|2672982|text|链接的解决方案有什么问题？这看起来很简单。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2672983|允许我重新排列您的数据集，以便更容易加载到Pandas+DataFrame中|offset|length|style|CODE|2672984|+++Time++Measurement++Mean+++Std
0+++++0++++++++++++1++++17++1.10
1+++++1++++++++++++1++++16++1.08
2+++++2++++++++++++1++++14++0.87
3+++++3++++++++++++1++++11++0.86
4+++++0++++++++++++2++++21++1.33
5+++++1++++++++++++2++++21++1.34
6+++++2++++++++++++2++++21++1.35
7+++++3++++++++++++2++++21++1.33|code-block|syntax|javascript|2672985|for+i,+m+in+df.groupby("Measurement"):
++++ax.plot(m.Time,+m.Mean)
++++ax.fill_between(m.Time,+m.Mean+-+m.Std,+m.Mean+%2B+m.Std,+alpha=0.35)|2672986|​|2672987|📷|atomic|2672988|2672989|这是一些随机生成的数据的结果：|2672990|2672991|2672992|2672993|编辑|BOLD|2672994|由于这个问题显然是在迭代您的特定数据帧格式，让我向您展示如何做到这一点(我是pandas新手，所以可能有更好的方法)。如果我没理解错，你的截图应该是这样的：|2672995|Measurement++++1++++++++++2++++++
++++++++++++Mean+++Std+Mean+++Std
Time+++++++++++++++++++++++++++++
0+++++++++++++17++1.10+++21++1.33
1+++++++++++++16++1.08+++21++1.34
2+++++++++++++14++0.87+++21++1.35
3+++++++++++++11++0.86+++21++1.33

df.info()
<class+'pandas.core.frame.DataFrame'>
Int64Index:+4+entries,+0+to+3
Data+columns+(total+4+columns):
(1,+Mean)++++4+non-null+int64
(1,+Std)+++++4+non-null+float64
(2,+Mean)++++4+non-null+int64
(2,+Std)+++++4+non-null+float64
dtypes:+float64(2),+int64(2)
memory+usage:+160.0+bytes

df.columns
MultiIndex(levels=[[1,+2],+[u'Mean',+u'Std']],
+++++++++++labels=[[0,+0,+1,+1],+[0,+1,+0,+1]],
+++++++++++names=[u'Measurement',+None])|2672996|并且您应该能够使用迭代它并获得相同的图：|2672997|for+i,+m+in+df.groupby("Measurement"):
++++ax.plot(m["Time"],+m['Mean'])
++++ax.fill_between(m["Time"],
++++++++++++++++++++m['Mean']+-+m['Std'],
++++++++++++++++++++m['Mean']+%2B+m['Std'],+alpha=0.35)|2672998|或者您可以使用以下命令将其重新堆叠为上面的格式|2672999|(df.stack("Measurement")++++++#+stack+"Measurement"+columns+row+by+row
+.reset_index()+++++++++++++++#+make+"Time"+a+normal+column,+add+a+new+index
+.sort_values("Measurement")++#+group+values+from+the+same+Measurement
+.reset_index(drop=True))+++++#+drop+sorted+index+and+make+a+new+one|2673000|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/e995d550dbec744d0e90668d956b104a.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/e21c3ddfd19880254d39e800b2dacb82.png^0|0|S|9|0|0|0|0|0|1|0|0|0|0|0|0|1|1|0|0|0|2|0|12|6|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1P|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1Q|8|@$D|1R|E|1S|F|G]]|9|@]|A|$]]|$1|H|3|I|5|J|7|1T|8|@]|9|@]|A|$K|L]]|$1|M|3|N|5|J|7|1U|8|@]|9|@]|A|$K|L]]|$1|O|3|P|5|6|7|1V|8|@]|9|@]|A|$]]|$1|Q|3|R|5|S|7|1W|8|@]|9|@$D|1X|E|1Y|1|1Z]]|A|$]]|$1|T|3|P|5|6|7|20|8|@]|9|@]|A|$]]|$1|U|3|V|5|6|7|21|8|@]|9|@]|A|$]]|$1|W|3|P|5|6|7|22|8|@]|9|@]|A|$]]|$1|X|3|R|5|S|7|23|8|@]|9|@$D|24|E|25|1|26]]|A|$]]|$1|Y|3|P|5|6|7|27|8|@]|9|@]|A|$]]|$1|Z|3|10|5|6|7|28|8|@$D|29|E|2A|F|11]]|9|@]|A|$]]|$1|12|3|13|5|6|7|2B|8|@$D|2C|E|2D|F|G]]|9|@]|A|$]]|$1|14|3|15|5|J|7|2E|8|@]|9|@]|A|$K|L]]|$1|16|3|17|5|6|7|2F|8|@]|9|@]|A|$]]|$1|18|3|19|5|J|7|2G|8|@]|9|@]|A|$K|L]]|$1|1A|3|1B|5|6|7|2H|8|@]|9|@]|A|$]]|$1|1C|3|1D|5|J|7|2I|8|@]|9|@]|A|$K|L]]|$1|1E|3|-4|5|6|7|2J|8|@]|9|@]|A|$]]]|1F|$1G|$5|1H|1I|1J|A|$1K|1L|1M|-4]]|1N|$5|1H|1I|1J|A|$1K|1O|1M|-4]]]]

What's wrong with the linked solution? It seems pretty straightforward.

Allow me to rearrange your dataset so it's easier to load in a Pandas <code>DataFrame</code>

<pre><code> Time Measurement Mean Std
0 0 1 17 1.10
1 1 1 16 1.08
2 2 1 14 0.87
3 3 1 11 0.86
4 0 2 21 1.33
5 1 2 21 1.34
6 2 2 21 1.35
7 3 2 21 1.33
</code></pre>

 

<pre><code>for i, m in df.groupby("Measurement"):
 ax.plot(m.Time, m.Mean)
 ax.fill_between(m.Time, m.Mean - m.Std, m.Mean + m.Std, alpha=0.35)
</code></pre>

<a href="https://i.stack.imgur.com/Xnkmz.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Xnkmz.png" alt="enter image description here"></a>

And here's the result with some random generated data:

<a href="https://i.stack.imgur.com/8ORlU.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/8ORlU.png" alt="enter image description here"></a>

EDIT

Since the issue is apparently iterating over your particular dataframe format let me show how you could do it (I'm new to <code>pandas</code> so there may be better ways). If I understood correctly your screenshot you should have something like:

<pre><code>Measurement 1 2 
 Mean Std Mean Std
Time 
0 17 1.10 21 1.33
1 16 1.08 21 1.34
2 14 0.87 21 1.35
3 11 0.86 21 1.33

df.info()
&lt;class 'pandas.core.frame.DataFrame'&gt;
Int64Index: 4 entries, 0 to 3
Data columns (total 4 columns):
(1, Mean) 4 non-null int64
(1, Std) 4 non-null float64
(2, Mean) 4 non-null int64
(2, Std) 4 non-null float64
dtypes: float64(2), int64(2)
memory usage: 160.0 bytes

df.columns
MultiIndex(levels=[[1, 2], [u'Mean', u'Std']],
 labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
 names=[u'Measurement', None])
</code></pre>

And you should be able to iterate over it with and obtain the same plot:

<pre><code>for i, m in df.groupby("Measurement"):
 ax.plot(m["Time"], m['Mean'])
 ax.fill_between(m["Time"],
 m['Mean'] - m['Std'],
 m['Mean'] + m['Std'], alpha=0.35)
</code></pre>

Or you could restack it to the format above with

<pre><code>(df.stack("Measurement") # stack "Measurement" columns row by row
 .reset_index() # make "Time" a normal column, add a new index
 .sort_values("Measurement") # group values from the same Measurement
 .reset_index(drop=True)) # drop sorted index and make a new one
</code></pre>

I have data in the following format:

<pre><code>| | Measurement 1 | | Measurement 2 | |
|------|---------------|------|---------------|------|
| | Mean | Std | Mean | Std |
| Time | | | | |
| 0 | 17 | 1.10 | 21 | 1.33 |
| 1 | 16 | 1.08 | 21 | 1.34 |
| 2 | 14 | 0.87 | 21 | 1.35 |
| 3 | 11 | 0.86 | 21 | 1.33 |
</code></pre>

I am using the following code to generate a matplotlib line graph from this data, which shows the standard deviation as a filled in area, see below:

<pre><code>def seconds_to_minutes(x, pos):
 minutes = f'{round(x/60, 0)}'
 return minutes

fig, ax = plt.subplots()
mean_temperature_over_time['Measurement 1']['mean'].plot(kind='line', yerr=mean_temperature_over_time['Measurement 1']['std'], alpha=0.15, ax=ax)
mean_temperature_over_time['Measurement 2']['mean'].plot(kind='line', yerr=mean_temperature_over_time['Measurement 2']['std'], alpha=0.15, ax=ax)

ax.set(title="A Line Graph with Shaded Error Regions", xlabel="x", ylabel="y")
formatter = FuncFormatter(seconds_to_minutes)
ax.xaxis.set_major_formatter(formatter)
ax.grid()
ax.legend(['Mean 1', 'Mean 2'])
</code></pre>

Output:

<a href="https://i.stack.imgur.com/WMh2n.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/WMh2n.png" alt="Output Graph"></a>
This seems like a very messy solution, and only actually produces shaded output because I have so much data. What is the correct way to produce a line graph from the dataframe I have with shaded error regions? I've looked at <a href="https://stackoverflow.com/questions/12957582/plot-yerr-xerr-as-shaded-region-rather-than-error-bars">Plot yerr/xerr as shaded region rather than error bars</a>, but am unable to adapt it for my case.

Plot Shaded Error Bars from Pandas Agg

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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我有以下格式的数据：|      | Measurement 1 |      | Measurement 2 |      ||------|---------------|------|---------------|------||      | Mean          | Std  | Mean      ...

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