blocks|key|639720|text|我会同时做两件事:旋转你的x标签，只使用日期：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|639721|import+seaborn+as+sns
import+matplotlib.pyplot+as+plt

#+dummy+data:
df+=+pd.DataFrame({'Date':pd.to_datetime(['1999-12-12',+'2000-12-12',+'2001-12-12']),'Amount':[1,2,3]})

sns.barplot(x="Date",+y="Amount",+data=df)
#+use+the+original+locations+of+your+xticks,+and+only+the+date+for+your+label
#+rotate+the+labels+90+degrees+using+the+rotation+argument
plt.xticks(plt.xticks()[0],+df.Date.dt.date,+rotation=90)
plt.tight_layout()
plt.show()|code-block|syntax|javascript|639722|​|639723|📷|atomic|offset|length|639724|639725|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/48495837c82c383672a7e749990ece42.png|imageAlt^0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Z|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|10|8|@]|9|@$L|11|M|12|1|13]]|A|$]]|$1|N|3|H|5|6|7|14|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V|W|-4]]]]

I would do both: rotate your xlabels and use only the dates:

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

# dummy data:
df = pd.DataFrame({'Date':pd.to_datetime(['1999-12-12', '2000-12-12', '2001-12-12']),'Amount':[1,2,3]})

sns.barplot(x="Date", y="Amount", data=df)
# use the original locations of your xticks, and only the date for your label
# rotate the labels 90 degrees using the rotation argument
plt.xticks(plt.xticks()[0], df.Date.dt.date, rotation=90)
plt.tight_layout()
plt.show()
</code></pre>

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

blocks|key|2497903|text|另一种解决方案是，如果你有大量的日期，并且喜欢在一个更稀疏的间隔上给它们贴上标签；|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2497904|import+pandas+as+pd
import+seaborn+as+sns
import+matplotlib.pyplot+as+plt
import+matplotlib.dates+as+mdates

#+dummy+data:
df+=+pd.DataFrame({'Date':pd.to_datetime(['1999-12-12',+'2000-12-12',+'2001-12-12',
++++++++++++++++++++++++++++++++++++++++++'2002-12-12',+'2003-12-12',+'2004-12-12',
++++++++++++++++++++++++++++++++++++++++++'2005-12-12','2006-12-12',+'2007-12-12',+'2008-12-12']),
++++++++++++++++++++++++++++++++++++++++++'Amount':[1,2,3,4,5,6,7,8,9,10]})

fig,+ax+=+plt.subplots()
sns.barplot(x="Date",+y="Amount",+data=df,+ax=ax)

#+set+the+frequency+for+labelling+the+xaxis
freq+=+int(2)

#+set+the+xlabels+as+the+datetime+data+for+the+given+labelling+frequency,
#+also+use+only+the+date+for+the+label
ax.set_xticklabels(df.iloc[::freq].Date.dt.date)
#+set+the+xticks+at+the+same+frequency+as+the+xlabels
xtix+=+ax.get_xticks()
ax.set_xticks(xtix[::freq])
#+nicer+label+format+for+dates
fig.autofmt_xdate()

plt.tight_layout()
plt.show()|code-block|syntax|javascript|2497905|点击查看情节|offset|length|2497906|也值得考虑使用海运地块默认值，并将日期放在yaxis上，以便于阅读，但这是更多的个人偏好。|2497907|import+pandas+as+pd
import+seaborn+as+sns
import+matplotlib.pyplot+as+plt
import+matplotlib.dates+as+mdates

#+set+the+seaborn+asthetics
sns.set()

#+dummy+data:
df+=+pd.DataFrame({'Date':pd.to_datetime(['1999-12-12',+'2000-12-12',+'2001-12-12',
++++++++++++++++++++++++++++++++++++++++++'2002-12-12',+'2003-12-12',+'2004-12-12',
++++++++++++++++++++++++++++++++++++++++++'2005-12-12','2006-12-12',+'2007-12-12',+'2008-12-12']),
++++++++++++++++++++++++++++++++++++++++++'Amount':[1,2,3,4,5,6,7,8,9,10]})

fig,+ax+=+plt.subplots()
#+plot+with+a+horizontal+orientation
sns.barplot(y="Date",+x="Amount",+data=df,+ax=ax,+orient='h')

#+set+the+frequency+for+labelling+the+yaxis
freq+=+int(2)

#+set+the+ylabels+as+the+datetime+data+for+the+given+labelling+frequency,
#+also+use+only+the+date+for+the+label
ax.set_yticklabels(df.iloc[::freq].Date.dt.date)
#+set+the+yticks+at+the+same+frequency+as+the+ylabels
ytix+=+ax.get_yticks()
ax.set_yticks(ytix[::freq])

plt.tight_layout()
plt.show()|2497908|点击查看更好的情节|2497909|entityMap|0|LINK|mutability|MUTABLE|url|https://i.stack.imgur.com/xOTO5.png|1|https://i.stack.imgur.com/6wFKo.png^0|0|0|0|6|0|0|0|0|0|9|1|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|12|8|@]|9|@$I|13|J|14|1|15]]|A|$]]|$1|K|3|L|5|6|7|16|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|17|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|18|8|@]|9|@$I|19|J|1A|1|1B]]|A|$]]|$1|Q|3|-4|5|6|7|1C|8|@]|9|@]|A|$]]]|R|$S|$5|T|U|V|A|$W|X]]|Y|$5|T|U|V|A|$W|Z]]]]

Another solution, if you have a large number of dates and would prefer to label them at a more sparse interval;

<pre><code>import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

# dummy data:
df = pd.DataFrame({'Date':pd.to_datetime(['1999-12-12', '2000-12-12', '2001-12-12',
 '2002-12-12', '2003-12-12', '2004-12-12',
 '2005-12-12','2006-12-12', '2007-12-12', '2008-12-12']),
 'Amount':[1,2,3,4,5,6,7,8,9,10]})

fig, ax = plt.subplots()
sns.barplot(x="Date", y="Amount", data=df, ax=ax)

# set the frequency for labelling the xaxis
freq = int(2)

# set the xlabels as the datetime data for the given labelling frequency,
# also use only the date for the label
ax.set_xticklabels(df.iloc[::freq].Date.dt.date)
# set the xticks at the same frequency as the xlabels
xtix = ax.get_xticks()
ax.set_xticks(xtix[::freq])
# nicer label format for dates
fig.autofmt_xdate()

plt.tight_layout()
plt.show()
</code></pre>

<a href="https://i.stack.imgur.com/xOTO5.png" rel="nofollow noreferrer">Click to see plot</a>

It's also worth considering using the seaborn plot defaults, and placing the dates on the yaxis for ease of reading, but that's moreso personal preference.

<pre><code>import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

# set the seaborn asthetics
sns.set()

# dummy data:
df = pd.DataFrame({'Date':pd.to_datetime(['1999-12-12', '2000-12-12', '2001-12-12',
 '2002-12-12', '2003-12-12', '2004-12-12',
 '2005-12-12','2006-12-12', '2007-12-12', '2008-12-12']),
 'Amount':[1,2,3,4,5,6,7,8,9,10]})

fig, ax = plt.subplots()
# plot with a horizontal orientation
sns.barplot(y="Date", x="Amount", data=df, ax=ax, orient='h')

# set the frequency for labelling the yaxis
freq = int(2)

# set the ylabels as the datetime data for the given labelling frequency,
# also use only the date for the label
ax.set_yticklabels(df.iloc[::freq].Date.dt.date)
# set the yticks at the same frequency as the ylabels
ytix = ax.get_yticks()
ax.set_yticks(ytix[::freq])

plt.tight_layout()
plt.show()
</code></pre>

<a href="https://i.stack.imgur.com/6wFKo.png" rel="nofollow noreferrer">Click to see nicer plot</a>

blocks|key|2497963|text|这会自动调整SNS绘图的日期x轴，因此在大多数情况下不必手动完成此操作：sns_plot.get_figure().autofmt_xdate()|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2497964|entityMap^0|10|11|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

This automatically adjusts SNS plot's date x axis so you don't have to manually do this in most cases:
<code>sns_plot.get_figure().autofmt_xdate()</code>

df (Pandas DataFrame) has two columns: Date (as datetime64) and Amount (as float).

I plot the values from Amount column against time, using barplot:

<pre><code>sns.barplot(x="Date", y="Amount", data=df)
plt.show()
</code></pre>

However, the date labels are a terrible mess (see picture). What would be an elegant way of dealing with this in Pandas? I'm considering removing month and year from the label, or rotating the labels 90 degrees. How would these be done, or is there a better option? Thank you.

<a href="https://i.stack.imgur.com/sJ26x.png" rel="noreferrer"><img src="https://i.stack.imgur.com/sJ26x.png" alt="Overlapping date labels on x axis"></a>

How to modify messy and overlapping date labels below x axis

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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df (Pandas DataFrame)有两列:日期(作为datetime64)和数量(作为浮点数)。我根据时间绘制“金额”列中的值，使用barplot：sns.barplot(x="Date", y="Amount", data=df)plt.show()然而，日期标签是一个可怕的混乱(见图)。在潘达斯，什么是处理...

问如何修改x轴以下凌乱和重叠的日期标签
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问如何修改x轴以下凌乱和重叠的日期标签EN