Python中DF的水平条形图

Question

所以我一直在尝试用 Python 中的 DF 制作水平条形图。我成功了，但是 X 轴的值顺序不对。

import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)


plt.rcdefaults()
fig, ax = plt.subplots()

plt.xlim(0, 14)

# data
people = (a.Ethnicity)
y_pos = np.arange(len(people))
performance = a.Value

ax.barh(y_pos, performance, align='center')
ax.set_yticks(y_pos)
ax.set_yticklabels(people)
ax.invert_yaxis()  # labels read top-to-bottom
ax.set_xlabel('Value')
ax.set_title('Unemployment between different ethnic groups in 2004')

plt.show()

这段代码给了我下图： Graph from Python

但我希望它是这样的：This graph 或者是这样的：Graph from Excel

这是变量a：

a = df.loc[(df.Time == 2018) & (df.Region == "All") & (df.Age == "All") & (df.Sex == "All"), ["Ethnicity","Value"]]
print(a)
                      Ethnicity Value
32772                        All   4.2
32952                      Asian   6.2
33132                Asian Other   6.1
33312                      Black   8.8
33492                     Indian   4.3
33672                      Mixed     7
33852                      Other   7.5
34032           Other than White   7.1
34212  Pakistani and Bangladeshi   8.4
34392                    Unknown   4.1
34572                      White   3.7
34752              White British   3.8
34932                White Other   3.4

Answer 1

我在编辑器中复制并粘贴了您的代码，得到了您想要的图表。 X 轴中的值的顺序正确。在我看来，可能在您的代码的另一部分中发生了某种不正确的排序。我将您的数据复制为字典来创建数据框。

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

fig, ax = plt.subplots()
plt.xlim(0, 100)

data = {'All': 4.2,
        'Asian': 6.2,
        'Asian Other': 6.1,
        'Black': 8.8,
        'Indian': 4.3,
        'Mixed': 7,
        'Other': 7.5,
        'Other than White': 7.1,
        'Pakistani and Bangladeshi': 8.4,
        'Unknown': 4.1,
        'White': 3.7,
        'White British': 3.8,
        'White Other': 3.4}

a = pd.DataFrame(data.items(), columns=["Ethnicity", "Value"])
people = (a.Ethnicity)
y_pos = np.arange(len(people))
performance = a.Value

ax.barh(y_pos, performance, align='center')
ax.set_yticks(y_pos)
ax.set_yticklabels(people)
ax.invert_yaxis()  # labels read top-to-bottom
ax.set_xlabel('Value')

plt.tight_layout()
plt.show()

结果：