如何為多個組繪制帶有注釋的堆疊條形圖

Question

在直方圖中，2 條柱之間出現了一個缺口。有人知道為什么嗎？

我收到此錯誤：

FixedLocator 位置的數量 (11)，通常來自對 set_ticks 的調用，與刻度標簽的數量 (10) 不匹配。

csv 文件只有 2 列，一列是國家名稱，另一列是所獲得的獎牌類型，每一行都有一個帶有類型和國家的獎牌。

該文件的鏈接是： https : //github.com/jpiedehierroa/files/blob/main/Libro1.csv

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

from pathlib import Path
my_csv = Path("C:/Usersjosep/Desktop/Libro1.csv")
df = pd.read_csv("Libro1.csv", sep=',')

# or load from github repo link
url = 'https://raw.githubusercontent.com/jpiedehierroa/files/main/Libro1.csv'
df = pd.read_csv(url)    

# Prepare data
x_var = 'countries'
groupby_var = 'type'
df_agg = df.loc[:,[x_var, groupby_var]].groupby(groupby_var)
vals = [df[x_var].values.tolist() for i, df in df_agg]

# Draw
plt.figure(figsize=(10,10), dpi= 100)
colors= ("#CD7F32","silver","gold")
n, bins, patches = plt.hist(vals, df[x_var].unique().__len__(), stacked=True, density=False, color=colors[:len(vals)])

# Decoration
plt.legend(["bronze", "silver","gold"], loc="upper right")
plt.title(f"Histogram of medals achieved by ${x_var}$ colored by ${groupby_var}$ in Tokyo 2020", fontsize=18)
plt.text(2,80,"138")
plt.xlabel(x_var)
plt.ylabel("amount of medals by type")
plt.ylim(0, 130)
plt.xticks(ticks=bins, labels=np.unique(df[x_var]).tolist(), rotation=90, horizontalalignment='left')
plt.show()

測試數據

• 如果鏈接失效

countries,type
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,gold
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,silver
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
USA,bronze
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,gold
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,silver
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
China,bronze
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,gold
Japan,silver
Japan,silver
Japan,silver
Japan,silver
Japan,silver
Japan,silver
Japan,silver
Japan,silver
Japan,silver
Japan,silver
Japan,silver
Japan,silver
Japan,silver
Japan,silver
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
Japan,bronze
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,gold
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,silver
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
GB,bronze
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,gold
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,silver
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
ROC,bronze
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,gold
Australia,silver
Australia,silver
Australia,silver
Australia,silver
Australia,silver
Australia,silver
Australia,silver
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Australia,bronze
Netherlands,gold
Netherlands,gold
Netherlands,gold
Netherlands,gold
Netherlands,gold
Netherlands,gold
Netherlands,gold
Netherlands,gold
Netherlands,gold
Netherlands,gold
Netherlands,silver
Netherlands,silver
Netherlands,silver
Netherlands,silver
Netherlands,silver
Netherlands,silver
Netherlands,silver
Netherlands,silver
Netherlands,silver
Netherlands,silver
Netherlands,silver
Netherlands,silver
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
Netherlands,bronze
France,gold
France,gold
France,gold
France,gold
France,gold
France,gold
France,gold
France,gold
France,gold
France,gold
France,silver
France,silver
France,silver
France,silver
France,silver
France,silver
France,silver
France,silver
France,silver
France,silver
France,silver
France,silver
France,bronze
France,bronze
France,bronze
France,bronze
France,bronze
France,bronze
France,bronze
France,bronze
France,bronze
France,bronze
France,bronze
Germany,gold
Germany,gold
Germany,gold
Germany,gold
Germany,gold
Germany,gold
Germany,gold
Germany,gold
Germany,gold
Germany,gold
Germany,silver
Germany,silver
Germany,silver
Germany,silver
Germany,silver
Germany,silver
Germany,silver
Germany,silver
Germany,silver
Germany,silver
Germany,silver
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Germany,bronze
Italy,gold
Italy,gold
Italy,gold
Italy,gold
Italy,gold
Italy,gold
Italy,gold
Italy,gold
Italy,gold
Italy,gold
Italy,silver
Italy,silver
Italy,silver
Italy,silver
Italy,silver
Italy,silver
Italy,silver
Italy,silver
Italy,silver
Italy,silver
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze
Italy,bronze

Answer 1

• 這更容易實現為堆積條形圖，因此，使用pandas.DataFrame.plot重塑數據pandas.crosstab並使用帶有kind='bar'和stacked=True pandas.crosstab繪圖
  • 這不應該用plt.hist來實現，因為它更復雜，而且更容易直接使用 pandas plot 方法。
  • 當 x 值是連續范圍的數字而不是離散的分類值時，直方圖也更合適。
• 使用matplotlib.pyplot.bar_label添加注釋
  • 有關其他詳細信息和示例，請參閱此答案
  • 這個答案顯示了如何在沒有.bar_label情況下以舊方式進行注釋。 我不推薦它。
• 在python 3.8.12 、 pandas 1.3.4 、 matplotlib 3.4.3

加載和塑造 DataFrame

import pandas as pd

# load from github repo link
url = 'https://raw.githubusercontent.com/jpiedehierroa/files/main/Libro1.csv'
df = pd.read_csv(url) 

# reshape the dataframe
ct = pd.crosstab(df.countries, df.type)

# total medals per country
ct['tot'] = ct.sum(axis=1)

# sort
ct = ct.sort_values(by='tot', ascending=False)

# display(ct)
type         bronze  gold  silver  tot
countries                             
USA              33    39      41  113
China            18    38      32   88
ROC              23    20      28   71
GB               22    22      21   65
Japan            17    27      14   58
Australia        22    17       7   46
Italy            20    10      10   40
Germany          16    10      11   37
Netherlands      14    10      12   36
France           11    10      12   33

陰謀

colors = ("#CD7F32", "silver", "gold")
cd = dict(zip(ct.columns, colors))

# plot the medals columns
title = 'Country Medal Count for Tokyo 2020'
ax = ct.iloc[:, :-1].plot(kind='bar', stacked=True, color=cd, title=title,
                          figsize=(12, 5), rot=0, width=1, ec='k' )

# annotate each container with individual values
for c in ax.containers:
    ax.bar_label(c, label_type='center')
    
# annotate the top containers with the sum of all the containers for the stack
labels = ct.tot.tolist()
ax.bar_label(ax.containers[2], labels=labels, padding=3)

# pad the spacing between the number and the edge of the figure
ax.margins(y=0.1)