matplotlib 条形图显示一个额外的值

Question

我正在从一个非常小的数据框生成一个排序的条形图。 54 行。

除了我注意到我的 xtick 标签没有在图表中的条形下方居中之外，一切正常。

经过进一步检查，我的条形图生成了 55 列。

只有 54 个值。

最低值为：IDN 为 11720.226825。

最左边的那一列是从哪里来的？

          count         mean          std          min          25%          50%          75%           max
TIME  54.000000  2016.000000     0.000000  2016.000000  2016.000000  2016.000000  2016.000000   2016.000000
Value 54.000000 36964.931286 16417.626336 11720.226825 25605.549959 36949.610508 46129.782953 102019.458319 

33      EST       GDP     TOT  USD_CAP         A  2016  29740.839398        NaN
34      IDN       GDP     TOT  USD_CAP         A  2016  11720.226825        NaN
35      ISR       GDP     TOT  USD_CAP         A  2016  37269.565838        NaN

生成条形图的完整代码：

x = df1.sort_values('Value', ascending=True)
names = x['LOCATION'].to_list()
fig, axe = plt.subplots(figsize=(16, 8), facecolor='0.9')
axe.set_facecolor("lightgrey")
x.plot(kind='bar', width=2, color='darkblue', edgecolor='white', ax=axe, legend=False)
plt.title("Gross Domestic Product (GDP), Total, US Dollars/Capita, 2016", size=16, color='black', loc='left')
plt.yticks([0, 5000, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000, 50000,
            55000, 60000, 65000, 70000, 75000, 80000, 85000, 90000, 95000, 100000],
           ["0", "", "10K", "", "20K", "", "30K", "", "40K", "", "50K",
            "", "60K", "", "70K", "", "80K", "", "90K", "", "100K"])
plt.xticks([i for i in range(len(names))], names, rotation=90)
axe.set_axisbelow(True)
plt.grid(color='white')
plt.show()

数据：

   LOCATION INDICATOR SUBJECT  MEASURE FREQUENCY  TIME         Value Flag Codes
0       AUS       GDP     TOT  USD_CAP         A  2016  48021.877366        NaN
1       AUT       GDP     TOT  USD_CAP         A  2016  50503.328406        NaN
2       BEL       GDP     TOT  USD_CAP         A  2016  46566.544596        NaN
3       CAN       GDP     TOT  USD_CAP         A  2016  44819.498023        NaN
4       CZE       GDP     TOT  USD_CAP         A  2016  34714.434668        NaN
5       DNK       GDP     TOT  USD_CAP         A  2016  49020.543891        NaN
6       FIN       GDP     TOT  USD_CAP         A  2016  43446.124099        NaN
7       FRA       GDP     TOT  USD_CAP         A  2016  41357.835264          P
8       DEU       GDP     TOT  USD_CAP         A  2016  48942.907149        NaN
9       GRC       GDP     TOT  USD_CAP         A  2016  26764.908341          P
10      HUN       GDP     TOT  USD_CAP         A  2016  26700.756023        NaN
11      ISL       GDP     TOT  USD_CAP         A  2016  50751.583533        NaN
12      IRL       GDP     TOT  USD_CAP         A  2016  70879.955583        NaN
13      ITA       GDP     TOT  USD_CAP         A  2016  38380.162379        NaN
14      JPN       GDP     TOT  USD_CAP         A  2016  42292.681306        NaN
15      KOR       GDP     TOT  USD_CAP         A  2016  36629.655177        NaN
16      LUX       GDP     TOT  USD_CAP         A  2016 102019.458319        NaN
17      MEX       GDP     TOT  USD_CAP         A  2016  18535.166890          E
18      NLD       GDP     TOT  USD_CAP         A  2016  50961.235694          P
19      NZL       GDP     TOT  USD_CAP         A  2016  39241.025516          E
20      NOR       GDP     TOT  USD_CAP         A  2016  58791.758233        NaN
21      POL       GDP     TOT  USD_CAP         A  2016  27055.192120        NaN
22      PRT       GDP     TOT  USD_CAP         A  2016  30658.466294          P
23      SVK       GDP     TOT  USD_CAP         A  2016  30460.320190        NaN
24      ESP       GDP     TOT  USD_CAP         A  2016  36331.525570          P
25      SWE       GDP     TOT  USD_CAP         A  2016  48853.279783        NaN
26      CHE       GDP     TOT  USD_CAP         A  2016  63888.708682          P
27      TUR       GDP     TOT  USD_CAP         A  2016  25655.471775          E
28      GBR       GDP     TOT  USD_CAP         A  2016  42757.048917        NaN
29      USA       GDP     TOT  USD_CAP         A  2016  57591.197652        NaN
30      CHL       GDP     TOT  USD_CAP         A  2016  23003.911170        NaN
31      CHN       GDP     TOT  USD_CAP         A  2016  15477.980833        NaN
32      COL       GDP     TOT  USD_CAP         A  2016  14011.612739        NaN
33      EST       GDP     TOT  USD_CAP         A  2016  29740.839398        NaN
34      IDN       GDP     TOT  USD_CAP         A  2016  11720.226825        NaN
35      ISR       GDP     TOT  USD_CAP         A  2016  37269.565838        NaN
36      RUS       GDP     TOT  USD_CAP         A  2016  24788.679645        NaN
37      SVN       GDP     TOT  USD_CAP         A  2016  32729.606427        NaN
38      ZAF       GDP     TOT  USD_CAP         A  2016  13334.987018        NaN
39     EU28       GDP     TOT  USD_CAP         A  2016  39652.936403        NaN
40     OECD       GDP     TOT  USD_CAP         A  2016  42151.375096          E
41    OECDE       GDP     TOT  USD_CAP         A  2016  39216.988089          E
42      LVA       GDP     TOT  USD_CAP         A  2016  25588.909354        NaN
43      BRA       GDP     TOT  USD_CAP         A  2016  15224.547440          E
44      LTU       GDP     TOT  USD_CAP         A  2016  29862.344559        NaN
45      SAU       GDP     TOT  USD_CAP         A  2016  54304.320339        NaN
46     EA19       GDP     TOT  USD_CAP         A  2016  42151.339298        NaN
47      ARG       GDP     TOT  USD_CAP         A  2016  20382.379902        NaN
48      CRI       GDP     TOT  USD_CAP         A  2016  16381.382700        NaN
49      BGR       GDP     TOT  USD_CAP         A  2016  19242.642129        NaN
50      HRV       GDP     TOT  USD_CAP         A  2016  23859.838656        NaN
51      CYP       GDP     TOT  USD_CAP         A  2016  32882.262558          P
52      MLT       GDP     TOT  USD_CAP         A  2016  37337.621659        NaN
53      ROU       GDP     TOT  USD_CAP         A  2016  23197.339903          P 

Answer 1

• width 似乎太大了。不要越过width=1
• 要更简洁地格式化 y 轴主要刻度，请使用 answer，并使用 answer 添加次要刻度。

import matplotlib.ticker as tkr
import pandas as pd

data = {'INDICATOR': ['AUS', 'AUT', 'BEL', 'CAN', 'CZE', 'DNK', 'FIN', 'FRA', 'DEU', 'GRC', 'HUN', 'ISL', 'IRL', 'ITA', 'JPN', 'KOR', 'LUX', 'MEX', 'NLD', 'NZL', 'NOR', 'POL', 'PRT', 'SVK', 'ESP', 'SWE', 'CHE', 'TUR', 'GBR', 'USA', 'CHL', 'CHN', 'COL', 'EST', 'IDN', 'ISR', 'RUS', 'SVN', 'ZAF', 'EU28', 'OECD', 'OECDE', 'LVA', 'BRA', 'LTU', 'SAU', 'EA19', 'ARG', 'CRI', 'BGR', 'HRV', 'CYP', 'MLT', 'ROU'], 'Flag': [48021.877366, 50503.328406, 46566.544596, 44819.498023, 34714.434668, 49020.543891, 43446.124099, 41357.835264, 48942.907149, 26764.908341, 26700.756023, 50751.583533, 70879.955583, 38380.162379, 42292.681306, 36629.655177, 102019.458319, 18535.16689, 50961.235694, 39241.025516, 58791.758233, 27055.19212, 30658.466294, 30460.32019, 36331.52557, 48853.279783, 63888.708682, 25655.471775, 42757.048917, 57591.197652, 23003.91117, 15477.980833, 14011.612739, 29740.839398, 11720.226825, 37269.565838, 24788.679645, 32729.606427, 13334.987018, 39652.936403, 42151.375096, 39216.988089, 25588.909354, 15224.54744, 29862.344559, 54304.320339, 42151.339298, 20382.379902, 16381.3827, 19242.642129, 23859.838656, 32882.262558, 37337.621659, 23197.339903]}
df = pd.DataFrame(data)

x = df.sort_values('Flag', ascending=True)

ax = x.plot(x='INDICATOR', y='Flag', kind='bar', color='darkblue', width=1, edgecolor='white', legend=False, figsize=(16, 8))
ax.set_title("Gross Domestic Product (GDP), Total, US Dollars/Capita, 2016", size=16, color='black', loc='left')

# format the y-axis major ticks
ax.set_yticks(range(0, 100001, 10000))
ax.yaxis.set_major_formatter(tkr.EngFormatter())

# add a y-axis minor ticks
ax.yaxis.set_minor_locator(tkr.AutoMinorLocator(n=2))

# format the plot face
ax.set_facecolor("lightgrey")
ax.set_axisbelow(True)

ax.grid(which='both', color='white')