英文:
How do i get pie chart labels to line up with the correct values from dataframe?
问题
我的饼图看起来还可以，各个部分的大小是正确的，但标签位置不正确。
# 声明爆炸式饼图
explode = [0, 0.1, 0, 0, 0, 0]
# 定义要使用的Seaborn颜色调色板
palette_color = sns.color_palette('pastel')
# 在图表上绘制数据
fig=plt.pie(combined_df.groupby(['Continent'])['total_consumption'].sum(), colors=palette_color, labels=combined_df['Continent'].unique(),
explode=explode, autopct='%.0f%%', labeldistance=0.9)
plt.show
应该绘制的值：
- 非洲 227.0
- 亚洲 128.7
- 欧洲 431.9
- 北美洲 147.5
- 大洋洲 42.5
- 南美洲 83.2
英文:
My pie chart is coming out okay, the segments are the correct sizes, but the labels aren't on the correct places.
# declaring exploding pie
explode = [0, 0.1, 0, 0, 0,0]
# define Seaborn color palette to use
palette_color = sns.color_palette('pastel')
# plotting data on chart
fig=plt.pie(combined_df.groupby(['Continent'])['total_consumption'].sum(), colors=palette_color,labels=combined_df['Continent'].unique(),
explode=explode, autopct='%.0f%%', labeldistance=0.9,)
plt.show
the values that should be plotted
Continent
Africa 227.0
Asia 128.7
Europe 431.9
North America 147.5
Oceania 42.5
South America 83.2
答案1
得分: 3
我怀疑unique()和value_counts()返回结果的顺序不一致。
value_counts()返回结果按计数排序，而unique()按在数组中的出现顺序排序。
你可以尝试这样做：
value_cnts = combined_df['Continent'].value_counts()
fig = plt.pie(value_cnts, colors=palette_color, labels=value_cnts.index,
explode=explode, autopct='%.0f%%', labeldistance=0.9)
英文:
My suspicion is that the order of the returned results of unique() and value_counts() is not consistent.
value_counts() returned is order by count, and unique() is order by appearance in the array.
You can try this:
value_cnts = combined_df['Continent'].value_counts()
fig = plt.pie(value_cnts, colors=palette_color, labels=value_cnts.index,
explode=explode, autopct='%.0f%%', labeldistance=0.9)
答案2
得分: 1
尝试不对 value_counts 的值进行排序：
fig = plt.pie(combined_df['Continent'].value_counts(sort=False), # <- 不排序
colors=palette_color,
labels=combined_df['Continent'].unique(), # 因为 unique 不排序
explode=explode, autopct='%.0f%%', labeldistance=0.9,)
演示：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
palette_color = sns.color_palette('pastel')
explode = [0, 0.1, 0, 0, 0,0]
np.random.seed(2023)
data = np.random.choice(
['Africa', 'Asia', 'Europe', 'North America', 'Oceania', 'South America'],
p=(0.213989, 0.121324, 0.407146, 0.139046, 0.040064, 0.078431),
size=1000
)
combined_df = pd.DataFrame({'Continent': data})
fig = plt.pie(combined_df['Continent'].value_counts(sort=False),
autopct='%.0f%%', labeldistance=0.9,
labels=combined_df['Continent'].unique(),
colors=palette_color, explode=explode)
plt.show()
英文:
Try to not sort values on value_counts:
fig=plt.pie(combined_df['Continent'].value_counts(sort=False), # <- do not sort
colors=palette_color,
labels=combined_df['Continent'].unique(), # because unique is not sorted
explode=explode, autopct='%.0f%%', labeldistance=0.9,)
Demo:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
palette_color = sns.color_palette('pastel')
explode = [0, 0.1, 0, 0, 0,0]
np.random.seed(2023)
data = data = np.random.choice(
['Africa', 'Asia', 'Europe', 'North America', 'Oceania', 'South America'],
p=(0.213989, 0.121324, 0.407146, 0.139046, 0.040064, 0.078431),
size=1000
)
combined_df = pd.DataFrame({'Continent': data})
fig = plt.pie(combined_df['Continent'].value_counts(sort=False),
autopct='%.0f%%', labeldistance=0.9,
labels=combined_df['Continent'].unique(),
colors=palette_color, explode=explode)
plt.show()
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