MatPlotLibのグループ化された棒グラフにデータラベルを追加

Question

いくつかのmatplotlibコードを見つけてカスタマイズして、グループ化された棒グラフを作成することができました。ただし、コードの上部にはラベルがありません。私はいくつかのアプローチを試みましたが、私はそれを正しくしていません。

私の最終目標は、次のようになります。

1. どれ外側の周りに黒い枠を取り除くと、y軸のラベル

取得各バー

の先頭にデータラベルを追加します。ヘルプ（特に＃1）は大いに感謝しています！

コード：

#Code adapted from: 
#https://chrisalbon.com/python/matplotlib_grouped_bar_plot.html 
#matplotlib online 

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


raw_data = {'plan_type': ['A1', 'A2', 'A3', 'A4', 'A5', 'A6'], 
     'Group A':  [100, 0, 0, 0, 0, 0], 
     'Group B':  [48, 16, 9, 22, 5, 0], 
     'Group C':  [18, 28, 84, 34, 11, 0], 
     'Group D': [49, 13, 7, 23, 6, 0], 
     'Group E':   [57, 16, 9, 26, 3, 0] 

    } 
df = pd.DataFrame(raw_data, columns = ['plan_type', 'Group B', 'Group C', 'Group D', 'Group E']) 


df2 =pd.DataFrame(raw_data, columns = ['plan_type', 'Group A']) 



# Setting the positions and width for the bars 
pos = list(range(len(df['Group B']))) 
width = 0.2 

# Plotting the bars 
fig, ax = plt.subplots(figsize=(7, 2)) 


#This creates another y-axis that shares the same x-axis 


# Create a bar with Group A data, 
# in position pos + some width buffer, 
plt.bar(pos, 
    #using df['Group E'] data, 
    df2['Group A'], 
    # of width 
    width*8, 
    # with alpha 0.5 
    alpha=1, 
    # with color 
    color='#E6E9ED', 
    # with label the fourth value in plan_type 
    label=df2['plan_type'][0]) 


# Create a bar with Group B data, 
# in position pos, 
plt.bar(pos, 
    #using df['Group B'] data, 
    df['Group B'], 
    # of width 
    width, 
    # with alpha 1 
    alpha=1, 
    # with color 
    color='#900C3F', 
    # with label the first value in plan_type 
    label=df['plan_type'][0]) 

# Create a bar with Group C data, 
# in position pos + some width buffer, 
plt.bar([p + width for p in pos], 
    #using df['Group C'] data, 
    df['Group C'], 
    # of width 
    width, 
    # with alpha 1 
    alpha=1.0, 
    # with color 
    color='#C70039', 
    # with label the second value in plan_type 
    label=df['plan_type'][1]) 

# Create a bar with Group D data, 
# in position pos + some width buffer, 
plt.bar([p + width*2 for p in pos], 
    #using df['Group D'] data, 
    df['Group D'], 
    # of width 
    width, 
    # with alpha 1 
    alpha=1, 
    # with color 
    color='#FF5733', 
    # with label the third value in plan_type 
    label=df['plan_type'][2]) 

# Create a bar with Group E data, 
# in position pos + some width buffer, 
plt.bar([p + width*3 for p in pos], 
    #using df['Group E'] data, 
    df['Group E'], 
    # of width 
    width, 
    # with alpha 1 
    alpha=1, 
    # with color 
    color='#FFC300', 
    # with label the fourth value in plan_type 
    label=df['plan_type'][3]) 


# Set the y axis label 
ax.set_ylabel('Percent') 

# Set the chart's title 
ax.set_title('A GRAPH - YAY!', fontweight = "bold") 

# Set the position of the x ticks 
ax.set_xticks([p + 1.5 * width for p in pos]) 

# Set the labels for the x ticks 
ax.set_xticklabels(df['plan_type']) 

# Setting the x-axis and y-axis limits 
plt.xlim(min(pos)-width, max(pos)+width*5) 
plt.ylim([0, 100]) 
#plt.ylim([0, max(df['Group B'] + df['Group C'] + df['Group D'] + df['Group E'])]) 

# Adding the legend and showing the plot. Upper center location, 5 columns, 
Expanded to fit on one line. 
plt.legend(['Group A','Group B', 'Group C', 'Group D', 'Group E'], loc='upper center', ncol=5, mode='expand', fontsize ='x-small') 

#plt.grid() --> This would add a Grid, but I don't want that. 

plt.show() 
plt.savefig("PlanOffered.jpg") 

Answer 1

ソリューションは、この問題のものと類似している： Adding value labels on a matplotlib bar chart

は、しかし、私は、プロットの独自の型を使用する例を提供し、したがって、それを作ります理解しやすい

棒の上にラベルを得るための一般的な考え方は、軸内のパッチを反復し、それらの高さの高さで注釈を付けることです。

Iコードを少し簡略。

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

raw_data = {'plan_type': ['A1', 'A2', 'A3', 'A4', 'A5', 'A6'], 
     'Group A':  [100, 0, 0, 0, 0, 0], 
     'Group B':  [48, 16, 9, 22, 5, 0], 
     'Group C':  [18, 28, 84, 34, 11, 0], 
     'Group D': [49, 13, 7, 23, 6, 0], 
     'Group E':   [57, 16, 9, 26, 3, 0] 

    } 
df2 =pd.DataFrame(raw_data, columns = ['plan_type', 'Group A']) 
df = pd.DataFrame(raw_data, 
        columns = ['plan_type', 'Group B', 'Group C', 'Group D', 'Group E']) 

ax = df2.plot.bar(rot=0,color='#E6E9ED',width=1) 
ax = df.plot.bar(rot=0, ax=ax, color=["#900C3F", '#C70039', '#FF5733', '#FFC300'], 
       width = 0.8) 

for p in ax.patches[1:]: 
    h = p.get_height() 
    x = p.get_x()+p.get_width()/2. 
    if h != 0: 
     ax.annotate("%g" % p.get_height(), xy=(x,h), xytext=(0,4), rotation=90, 
        textcoords="offset points", ha="center", va="bottom") 

ax.set_xlim(-0.5, None) 
ax.margins(y=0) 
ax.legend(ncol=len(df.columns), loc="lower left", bbox_to_anchor=(0,1.02,1,0.08), 
      borderaxespad=0, mode="expand") 
ax.set_xticklabels(df["plan_type"]) 
plt.show()