如何按降序对堆积条形图进行排序？

Question

我正在尝试创建由以下代码创建的堆叠水平条形图，但是我想按频率降序对其进行排序。我尝试在 groupby 行中添加 .sort_values() 但它不会影响图表。

此谷歌驱动器上提供的数据：https://drive.google.com/file/d/1PcfYsyD6T1_EHz3FZvdWN2DewzI64_zq/view?usp=sharing

df = pd.read_csv('data.csv')

     Company                                                   Location                    DateTime                                       Details  Outcome
0     SpaceX                 LC-39A, Kennedy Space Center, Florida, USA  Fri Aug 07, 2020 05:12 UTC  Falcon 9 Block 5 | Starlink V1 L9 & BlackSky  Success
1       CASC  Site 9401 (SLS-2), Jiuquan Satellite Launch Center, China  Thu Aug 06, 2020 04:01 UTC           Long March 2D | Gaofen-9 04 & Q-SAT  Success
2     SpaceX                              Pad A, Boca Chica, Texas, USA  Tue Aug 04, 2020 23:57 UTC            Starship Prototype | 150 Meter Hop  Success
3  Roscosmos               Site 200/39, Baikonur Cosmodrome, Kazakhstan  Thu Jul 30, 2020 21:25 UTC  Proton-M/Briz-M | Ekspress-80 & Ekspress-103  Success
4        ULA                   SLC-41, Cape Canaveral AFS, Florida, USA  Thu Jul 30, 2020 11:50 UTC                    Atlas V 541 | Perseverance  Success

#add a success column
values = ['Success']
conditions = list(map(df['Outcome'].str.contains, values))
df['outcome'] = np.select(conditions, values, 'Failure')
s = df.groupby(['Company','outcome']).size().sort_values()
s = s.unstack()

outcome          Failure  Success
Company                          
AEB                  3.0      NaN
AMBA                 4.0      4.0
ASI                  NaN      9.0
Arianespace         10.0    269.0


s.plot(
    kind = 'barh', # Horizontal bars
    stacked = True,
    color = ['Red', 'Green'],
    figsize = [15, 8],
)
plt.savefig('hbar.png')

这是我的输出

Answer 1

• 将每个公司的'Success' 和'Failure' 相加，然后按总数排序。

import pandas as pd

df = pd.read_csv('data.csv')
#add a success column
values = ['Success']
conditions = list(map(df['Outcome'].str.contains, values))
df['outcome'] = np.select(conditions, values, 'Failure')

s = df.groupby(['Company','outcome']).size().unstack()

# add the row wise sum for each company
s['total'] = s.sum(axis=1)

# sort
s.sort_values('total', inplace=True)

# plot
ax = s[['Failure', 'Success']].plot(kind='barh', stacked=True, color=['Red', 'Green'], figsize=[15, 8])

# set the xscale for better visibility of data
ax.set_xscale('log')  # use 'symlog' if there are negative values

plt.savefig('hbar.png')

• 如果 OP 中的数据链接消失，则数据的子集可重现

data = {'Company': ['SpaceX', 'CASC', 'SpaceX', 'Roscosmos', 'ULA', 'CASC', 'Roscosmos', 'CASC', 'SpaceX', 'JAXA', 'Northrop', 'ExPace', 'CASC', 'IAI', 'CASC', 'Rocket Lab', 'CASC', 'SpaceX', 'CASC', 'CASC', 'SpaceX', 'Rocket Lab', 'CASC', 'SpaceX', 'CASC', 'SpaceX', 'CASC', 'Virgin Orbit', 'VKS RF', 'MHI', 'ULA', 'ExPace', 'CASC', 'Roscosmos', 'SpaceX', 'IRGC', 'CASC', 'Roscosmos', 'ULA', 'CASC', 'Arianespace', 'SpaceX', 'VKS RF', 'CASC', 'CASC', 'SpaceX', 'VKS RF', 'CASC', 'Arianespace', 'SpaceX', 'Northrop', 'ULA', 'ISA', 'MHI', 'Arianespace', 'Rocket Lab', 'SpaceX', 'SpaceX', 'Arianespace', 'ExPace', 'CASC', 'CASC', 'SpaceX', 'CASC', 'VKS RF', 'Roscosmos', 'ULA', 'CASC', 'Arianespace', 'SpaceX', 'CASC', 'Blue Origin', 'ISRO', 'VKS RF', 'ExPace', 'ExPace', 'Roscosmos', 'Rocket Lab', 'SpaceX', 'CASC', 'ISRO', 'Arianespace', 'VKS RF', 'CASC', 'ExPace', 'CASC', 'ExPace', 'SpaceX', 'CASC', 'CASC', 'Northrop', 'Exos', 'CASC', 'Rocket Lab', 'Northrop', 'ILS', 'CASC', 'VKS RF', 'Roscosmos', 'CASC', 'MHI', 'CASC', 'CASC', 'CASC', 'ExPace', 'VKS RF', 'ISA', 'ULA', 'Roscosmos', 'Rocket Lab', 'CASC', 'CASC', 'ULA', 'SpaceX', 'Arianespace', 'Roscosmos', 'Roscosmos', 'VKS RF', 'CASC', 'SpaceX', 'i-Space', 'ISRO', 'Roscosmos', 'Roscosmos', 'Arianespace', 'VKS RF', 'Roscosmos', 'Exos', 'Rocket Lab', 'SpaceX', 'CASC', 'Arianespace', 'SpaceX', 'CASC', 'Roscosmos', 'Roscosmos', 'SpaceX', 'CASC', 'ISRO', 'CASC', 'Rocket Lab', 'SpaceX', 'Blue Origin', 'CASC', 'CASC', 'Northrop', 'SpaceX', 'Arianespace', 'Roscosmos', 'ISRO', 'CASC', 'Rocket Lab', 'OneSpace', 'Arianespace', 'ULA', 'Roscosmos', 'CASC', 'Exos', 'SpaceX', 'Arianespace', 'SpaceX', 'VKS RF', 'Arianespace', 'ISA', 'ISRO', 'Blue Origin', 'CASC', 'ULA', 'JAXA', 'ISA', 'SpaceX', 'CASC', 'CASC', 'Roscosmos', 'CASC', 'SpaceX', 'CASC', 'VKS RF', 'Arianespace', 'ISRO', 'Rocket Lab', 'CASC', 'CASC', 'SpaceX', 'Arianespace', 'SpaceX', 'Roscosmos', 'VKS RF', 'ISRO', 'Arianespace', 'CASC', 'CASC', 'Northrop', 'Roscosmos', 'SpaceX', 'ISRO', 'Rocket Lab', 'Arianespace', 'VKS RF', 'CASC', 'MHI', 'CASC', 'Landspace', 'Roscosmos', 'CASC', 'Arianespace', 'ULA', 'CASC', 'Roscosmos', 'CASC', 'SpaceX', 'ExPace', 'Arianespace', 'MHI', 'CASC', 'ISRO', 'ULA', 'SpaceX', 'CASC', 'Exos', 'CASC', 'Arianespace', 'ULA', 'SpaceX', 'CASC', 'CASC', 'SpaceX', 'Arianespace', 'SpaceX', 'Blue Origin', 'Roscosmos', 'CASC', 'CASC', 'SpaceX', 'CASC', 'VKS RF', 'MHI', 'Roscosmos', 'CASC', 'SpaceX', 'CASC', 'SpaceX', 'Northrop', 'CASC', 'SpaceX', 'CASC', 'ULA', 'CASC', 'Blue Origin', 'CASC', 'Eurockot', 'SpaceX', 'VKS RF', 'ULA', 'ISRO', 'CASC', 'Arianespace', 'SpaceX', 'CASC', 'SpaceX', 'CASC', 'Roscosmos', 'ISRO', 'Roscosmos', 'CASC', 'Arianespace', 'SpaceX', 'ULA', 'MHI', 'SpaceX', 'Roscosmos', 'CASC', 'SpaceX', 'JAXA', 'CASC', 'Roscosmos', 'SpaceX', 'Arianespace', 'CASC', 'Rocket Lab', 'ULA', 'CASC', 'JAXA', 'CASC', 'ULA', 'ISRO', 'CASC', 'CASC', 'SpaceX', 'Land Launch', 'CASC', 'CASC', 'SpaceX', 'MHI', 'Roscosmos', 'Blue Origin', 'SpaceX', 'Arianespace', 'CASC', 'CASC'], 'Outcome': ['Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Failure', 'Success', 'Success', 'Success', 'Failure', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Failure', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Failure', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Failure', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Failure', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 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df = pd.DataFrame(data)