blocks|key|2649648|text|数据生成：我使用以下代码随机生成了您的数据：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|2649649|import+numpy+as+np
import+pandas+as+pd

#+The+number+of+demo+data+count
demo_num+=+20

#+Regions
regions+=+['central',+'east',+'west']
np.random.seed(9)
regions_r+=+np.random.choice(regions,+demo_num)

#+Tiers
tiers+=+['hi',+'lo',+'mid']
np.random.seed(99)
tiers_r+=+np.random.choice(tiers,+demo_num)

#+Sales
sales2015+=+np.array(range(demo_num))+*+100+
sales2016+=+np.array(range(demo_num))+*+200

#+Dataframe+`df`+to+store+all+above
df+=+pd.DataFrame({'Region':+regions_r,+'Tier':+tiers_r,+'Sales2015':+sales2015,+'Sales2016':+sales2016})|code-block|syntax|javascript|2649650|2649651|数据：现在输入的数据如下所示|2649652|++++Region++Sales2015+++Sales2016+++Tier
0+++west++++0+++++++++++0+++++++++++lo
1+++central+100+++++++++200+++++++++lo
2+++west++++200+++++++++400+++++++++hi
3+++east++++300+++++++++600+++++++++lo
4+++west++++400+++++++++800+++++++++hi
5+++central+500+++++++++1000++++++++mid
6+++west++++600+++++++++1200++++++++hi
7+++east++++700+++++++++1400++++++++lo
8+++east++++800+++++++++1600++++++++hi
9+++west++++900+++++++++1800++++++++lo
10++central+1000++++++++2000++++++++mid
11++central+1100++++++++2200++++++++lo
12++west++++1200++++++++2400++++++++lo
13++east++++1300++++++++2600++++++++hi
14++central+1400++++++++2800++++++++lo
15++east++++1500++++++++3000++++++++mid
16++east++++1600++++++++3200++++++++hi
17++east++++1700++++++++3400++++++++mid
18++central+1800++++++++3600++++++++hi
19++central+1900++++++++3800++++++++hi|2649653|2649654|可视化的代码：|2649655|import+matplotlib.pyplot+as+plt
import+pandas+as+pd

#+Summary+statistics++++++++
df+=+df.groupby(['Tier',+'Region'],+sort=True).sum()[['Sales2015',+'Sales2016']].reset_index(level=1,+drop=False)

#+Loop+over+Regions+and+visualize+graphs+side+by+side
regions+=+df.Region.unique().tolist()
fig,+axes+=+plt.subplots(ncols=len(regions),+nrows=1,+figsize=(10,+5),+sharex=False,+sharey=True)

for+region,+ax+in+zip(regions,+axes.ravel()):
++++df.loc[df['Region']+==+region].plot(ax=ax,+kind='bar',+title=region)
plt.tight_layout()
plt.show()|2649656|2649657|结果：+Now图形如下所示。我没有优化字体大小等。|2649658|📷|atomic|2649659|​|2649660|希望这能有所帮助。|2649661|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/4fd62decb6d055bbda8dc88a991a3fd7.png|imageAlt^0|0|5|0|0|0|0|3|0|0|0|4|3|0|0|0|0|3|0|0|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1D|8|@$9|1E|A|1F|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1G|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|1H|8|@]|D|@]|E|$]]|$1|L|3|M|5|6|7|1I|8|@$9|1J|A|1K|B|C]]|D|@]|E|$]]|$1|N|3|O|5|H|7|1L|8|@]|D|@]|E|$I|J]]|$1|P|3|-4|5|6|7|1M|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1N|8|@$9|1O|A|1P|B|C]]|D|@]|E|$]]|$1|S|3|T|5|H|7|1Q|8|@]|D|@]|E|$I|J]]|$1|U|3|-4|5|6|7|1R|8|@]|D|@]|E|$]]|$1|V|3|W|5|6|7|1S|8|@$9|1T|A|1U|B|C]]|D|@]|E|$]]|$1|X|3|Y|5|Z|7|1V|8|@]|D|@$9|1W|A|1X|1|1Y]]|E|$]]|$1|10|3|11|5|6|7|1Z|8|@]|D|@]|E|$]]|$1|12|3|13|5|6|7|20|8|@]|D|@]|E|$]]|$1|14|3|-4|5|6|7|21|8|@]|D|@]|E|$]]]|15|$16|$5|17|18|19|E|$1A|1B|1C|-4]]]]

Data generation: I randomly generated your data using below code:

<pre><code>import numpy as np
import pandas as pd

# The number of demo data count
demo_num = 20

# Regions
regions = ['central', 'east', 'west']
np.random.seed(9)
regions_r = np.random.choice(regions, demo_num)

# Tiers
tiers = ['hi', 'lo', 'mid']
np.random.seed(99)
tiers_r = np.random.choice(tiers, demo_num)

# Sales
sales2015 = np.array(range(demo_num)) * 100 
sales2016 = np.array(range(demo_num)) * 200

# Dataframe `df` to store all above
df = pd.DataFrame({'Region': regions_r, 'Tier': tiers_r, 'Sales2015': sales2015, 'Sales2016': sales2016})
</code></pre>

<hr>

Data: Now input data looks like this

<pre><code> Region Sales2015 Sales2016 Tier
0 west 0 0 lo
1 central 100 200 lo
2 west 200 400 hi
3 east 300 600 lo
4 west 400 800 hi
5 central 500 1000 mid
6 west 600 1200 hi
7 east 700 1400 lo
8 east 800 1600 hi
9 west 900 1800 lo
10 central 1000 2000 mid
11 central 1100 2200 lo
12 west 1200 2400 lo
13 east 1300 2600 hi
14 central 1400 2800 lo
15 east 1500 3000 mid
16 east 1600 3200 hi
17 east 1700 3400 mid
18 central 1800 3600 hi
19 central 1900 3800 hi
</code></pre>

<hr>

Code for visualization:

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

# Summary statistics 
df = df.groupby(['Tier', 'Region'], sort=True).sum()[['Sales2015', 'Sales2016']].reset_index(level=1, drop=False)

# Loop over Regions and visualize graphs side by side
regions = df.Region.unique().tolist()
fig, axes = plt.subplots(ncols=len(regions), nrows=1, figsize=(10, 5), sharex=False, sharey=True)

for region, ax in zip(regions, axes.ravel()):
 df.loc[df['Region'] == region].plot(ax=ax, kind='bar', title=region)
plt.tight_layout()
plt.show()
</code></pre>

<hr>

Result: Now graphs look like this. I haven't optimize font size etc..
<a href="https://i.stack.imgur.com/CelkR.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/CelkR.png" alt="Output"></a>

Hope this helps.

I want to plot a bar chart where I need to compare sales of two regions with respect to <code>Region</code> and <code>Tier</code>.

I implemented below code:

<pre><code>df.groupby(['Region','Tier'],sort=True).sum()[['Sales2015','Sales2016']].unstack().plot(kind="bar",width = .8)
</code></pre>

<img src="https://i.stack.imgur.com/a3uuN.png" alt="enter image description here">

But I want to implement sales of <code>Tier</code> 2015 and 2016 side by side,
e.g., on the x-axis the <code>xticks</code> should look like High Sales of 2015 and 2016 etc.
<img src="https://i.stack.imgur.com/wJjTd.png" alt="enter image description here">

Bar plot by grouping values in python

我想绘制一个条形图，我需要在其中比较两个地区相对于Region和Tier的销售额。我实现了以下代码：df.groupby(['Region','Tier'],sort=True).sum()[['Sales2015','Sales2016']].unstack().plot(kind="bar",width = .8)...

问在python中通过分组值绘制条形图
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