如何以正确的顺序绘制分组的条形图
How to plot grouped bars in the correct order
我正在制作标准化测试的熟练程度分组条形图。这是我的代码：
bush_prof_boy = bush.groupby(['BOY Prof'])['BOY Prof'].count()
bush_prof_pct_boy = bush_prof_boy/bush['BOY Prof'].count() * 100
bush_prof_eoy = bush.groupby(['EOY Prof'])['EOY Prof'].count()
bush_prof_pct_eoy = bush_prof_eoy/bush['EOY Prof'].count() * 100
labels = ['Remedial', 'Below Proficient', 'Proficient', 'Advanced']
fig, ax = plt.subplots()
rects1 = ax.bar(x - width/2, bush_prof_pct_boy, width, label='BOY',
color='mediumorchid')
rects2 = ax.bar(x + width/2, bush_prof_pct_eoy, width, label='EOY', color='teal')
ax.set_ylabel('% of Students at Proficiency Level', fontsize=18)
ax.set_title('Bushwick Middle Change in Proficiency Levels', fontsize=25)
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize=25)
ax.legend(fontsize=25)
plt.yticks(fontsize=15)
plt.figure(figsize=(5,15))
plt.show()
“BOY”代表“年初”，“EOY”代表“年末”，因此条形图旨在显示年初和年底落入每个熟练程度级别的学生百分比。该图看起来不错，但当我深入研究这些数字时，我可以看到 EOY 的标签不正确。这是我的图表：
BOY 的百分比绘制正确，但 EOY 的标签错误。以下是实际百分比，我确信这是正确的：
BOY %
Advanced 14.0
Below Proficient 38.0
Proficient 34.0
Remedial 14.0
EOY %
Advanced 39.0
Below Proficient 18.0
Proficient 32.0
Remedial 11.0
- 使用来自 Kaggle: Brooklyn NY Schools
的数据
- 单独计算条形组可能会出现问题。 最好在一个数据帧内进行计算，塑造数据帧，然后绘图，因为这将确保条形图绘制在正确的组中。
- 由于没有提供数据，这从宽格式数字数据开始，然后清理和调整数据框。
- 使用
.cut 将数值转换为分类值
- Dataframe 使用
.melt, and then use .groupby 转换为长格式以计算 'x of Year' 内的百分比
- 重塑
.pivot, and plot with pandas.DataFrame.plot
- 在
python 3.8、pandas 1.3.1 和 matplotlib 3.4.2 中测试
导入、加载和清理 DataFrame
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
# data
data = {'BOY': [11.0, 11.0, 11.0, 11.0, 11.0, 8.0, 11.0, 14.0, 12.0, 13.0, 11.0, 14.0, 10.0, 9.0, 10.0, 10.0, 10.0, 12.0, 12.0, 13.0, 12.0, 11.0, 9.0, 12.0, 16.0, 12.0, 12.0, 12.0, 15.0, 10.0, 10.0, 10.0, 8.0, 11.0, 12.0, 14.0, 10.0, 8.0, 11.0, 12.0, 14.0, 12.0, 13.0, 15.0, 13.0, 8.0, 8.0, 11.0, 10.0, 11.0, 13.0, 11.0, 13.0, 15.0, 10.0, 8.0, 10.0, 9.0, 8.0, 11.0, 13.0, 11.0, 8.0, 11.0, 15.0, 11.0, 12.0, 17.0, 12.0, 11.0, 18.0, 14.0, 15.0, 16.0, 7.0, 11.0, 15.0, 16.0, 13.0, 13.0, 13.0, 0.0, 11.0, 15.0, 14.0, 11.0, 13.0, 16.0, 14.0, 12.0, 8.0, 13.0, 13.0, 14.0, 7.0, 10.0, 16.0, 10.0, 13.0, 10.0, 14.0, 8.0, 16.0, 13.0, 12.0, 14.0, 12.0, 14.0, 16.0, 15.0, 13.0, 13.0, 10.0, 14.0, 8.0, 10.0, 10.0, 11.0, 12.0, 10.0, 12.0, 14.0, 17.0, 13.0, 14.0, 16.0, 15.0, 13.0, 16.0, 9.0, 16.0, 15.0, 11.0, 11.0, 15.0, 14.0, 12.0, 15.0, 11.0, 16.0, 14.0, 14.0, 15.0, 14.0, 14.0, 14.0, 16.0, 15.0, 12.0, 12.0, 14.0, 15.0, 13.0, 14.0, 13.0, 17.0, 14.0, 13.0, 14.0, 13.0, 13.0, 12.0, 10.0, 15.0, 14.0, 12.0, 12.0, 14.0, 12.0, 14.0, 13.0, 15.0, 13.0, 14.0, 14.0, 12.0, 11.0, 15.0, 14.0, 14.0, 10.0], 'EOY': [16.0, 16.0, 16.0, 14.0, 10.0, 14.0, 16.0, 14.0, 15.0, 15.0, 15.0, 11.0, 11.0, 15.0, 10.0, 14.0, 17.0, 14.0, 9.0, 15.0, 14.0, 16.0, 14.0, 13.0, 11.0, 13.0, 12.0, 14.0, 15.0, 13.0, 14.0, 15.0, 12.0, 19.0, 9.0, 13.0, 11.0, 14.0, 17.0, 17.0, 14.0, 13.0, 14.0, 10.0, 16.0, 15.0, 12.0, 11.0, 12.0, 14.0, 15.0, 10.0, 15.0, 14.0, 14.0, 15.0, 18.0, 15.0, 10.0, 10.0, 15.0, 15.0, 13.0, 15.0, 19.0, 13.0, 18.0, 20.0, 21.0, 17.0, 18.0, 17.0, 18.0, 17.0, 12.0, 16.0, 15.0, 18.0, 19.0, 17.0, 20.0, 11.0, 18.0, 19.0, 11.0, 12.0, 17.0, 20.0, 17.0, 15.0, 13.0, 18.0, 14.0, 17.0, 12.0, 12.0, 16.0, 12.0, 14.0, 15.0, 14.0, 10.0, 20.0, 13.0, 18.0, 20.0, 11.0, 20.0, 17.0, 20.0, 13.0, 17.0, 15.0, 18.0, 14.0, 13.0, 13.0, 18.0, 10.0, 13.0, 12.0, 18.0, 20.0, 20.0, 16.0, 18.0, 15.0, 20.0, 22.0, 18.0, 21.0, 18.0, 18.0, 18.0, 17.0, 16.0, 19.0, 16.0, 20.0, 19.0, 19.0, 20.0, 20.0, 14.0, 18.0, 20.0, 20.0, 18.0, 16.0, 21.0, 20.0, 18.0, 15.0, 14.0, 17.0, 19.0, 21.0, 14.0, 18.0, 15.0, 18.0, 21.0, 19.0, 17.0, 16.0, 16.0, 15.0, 20.0, 19.0, 16.0, 21.0, 17.0, 19.0, 15.0, 18.0, 20.0, 18.0, 20.0, 18.0, 16.0, 16.0]}
df = pd.DataFrame(data)
# replace numbers with categorical labels; could also create new columns
labels = ['Remedial', 'Below Proficient', 'Proficient', 'Advanced']
bins = [1, 11, 13, 15, np.inf]
df['BOY'] = pd.cut(x=df.BOY, labels=labels, bins=bins, right=True)
df['EOY'] = pd.cut(x=df.EOY, labels=labels, bins=bins, right=True)
# melt the relevant columns into a long form
dfm = df.melt(var_name='Tested', value_name='Proficiency')
# set the categorical label order, which makes the xaxis labels print in the specific order
dfm['Proficiency'] = pd.Categorical(dfm['Proficiency'], labels, ordered=True)
Groupby、百分比计算和绘图形状
# groupby and get the value counts
dfg = dfm.groupby('Tested')['Proficiency'].value_counts().reset_index(level=1, name='Size').rename({'level_1': 'Proficiency'}, axis=1)
# divide by the Tested value counts to get the percent
dfg['percent'] = dfg['Size'].div(dfm.Tested.value_counts()).mul(100).round(1)
# reshape to plot
dfp = dfg.reset_index().pivot(index='Proficiency', columns='Tested', values='percent')
# display(dfp)
Tested BOY EOY
Proficiency
Remedial 34.8 9.9
Below Proficient 28.7 12.7
Proficient 27.1 25.4
Advanced 8.8 51.9
情节
ax = dfp.plot(kind='bar', figsize=(15, 5), rot=0, color=['orchid', 'teal'])
# formatting
ax.yaxis.set_major_formatter(mtick.PercentFormatter())
ax.set_ylabel('Students at Proficiency Level', fontsize=18)
ax.set_xlabel('')
ax.set_title('Bushwick Middle Change in Proficiency Levels', fontsize=25)
ax.set_xticklabels(ax.get_xticklabels(), fontsize=25)
ax.legend(fontsize=25)
_ = plt.yticks(fontsize=15)
# add bar labels
for p in ax.containers:
ax.bar_label(p, fmt='%.1f%%', label_type='edge', fontsize=12)
# pad the spacing between the number and the edge of the figure
ax.margins(y=0.2)
- 查看条形标签匹配
dfp
我正在制作标准化测试的熟练程度分组条形图。这是我的代码：
bush_prof_boy = bush.groupby(['BOY Prof'])['BOY Prof'].count()
bush_prof_pct_boy = bush_prof_boy/bush['BOY Prof'].count() * 100
bush_prof_eoy = bush.groupby(['EOY Prof'])['EOY Prof'].count()
bush_prof_pct_eoy = bush_prof_eoy/bush['EOY Prof'].count() * 100
labels = ['Remedial', 'Below Proficient', 'Proficient', 'Advanced']
fig, ax = plt.subplots()
rects1 = ax.bar(x - width/2, bush_prof_pct_boy, width, label='BOY',
color='mediumorchid')
rects2 = ax.bar(x + width/2, bush_prof_pct_eoy, width, label='EOY', color='teal')
ax.set_ylabel('% of Students at Proficiency Level', fontsize=18)
ax.set_title('Bushwick Middle Change in Proficiency Levels', fontsize=25)
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize=25)
ax.legend(fontsize=25)
plt.yticks(fontsize=15)
plt.figure(figsize=(5,15))
plt.show()
“BOY”代表“年初”，“EOY”代表“年末”，因此条形图旨在显示年初和年底落入每个熟练程度级别的学生百分比。该图看起来不错，但当我深入研究这些数字时，我可以看到 EOY 的标签不正确。这是我的图表：
BOY 的百分比绘制正确，但 EOY 的标签错误。以下是实际百分比，我确信这是正确的：
BOY %
Advanced 14.0
Below Proficient 38.0
Proficient 34.0
Remedial 14.0
EOY %
Advanced 39.0
Below Proficient 18.0
Proficient 32.0
Remedial 11.0
- 使用来自 Kaggle: Brooklyn NY Schools 的数据
- 单独计算条形组可能会出现问题。 最好在一个数据帧内进行计算，塑造数据帧，然后绘图，因为这将确保条形图绘制在正确的组中。
- 由于没有提供数据，这从宽格式数字数据开始，然后清理和调整数据框。
- 使用
.cut 将数值转换为分类值
- Dataframe 使用
.melt, and then use.groupby转换为长格式以计算'x of Year' 内的百分比
- 重塑
.pivot, and plot withpandas.DataFrame.plot
- 使用
- 在
python 3.8、pandas 1.3.1和matplotlib 3.4.2 中测试
导入、加载和清理 DataFrame
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
# data
data = {'BOY': [11.0, 11.0, 11.0, 11.0, 11.0, 8.0, 11.0, 14.0, 12.0, 13.0, 11.0, 14.0, 10.0, 9.0, 10.0, 10.0, 10.0, 12.0, 12.0, 13.0, 12.0, 11.0, 9.0, 12.0, 16.0, 12.0, 12.0, 12.0, 15.0, 10.0, 10.0, 10.0, 8.0, 11.0, 12.0, 14.0, 10.0, 8.0, 11.0, 12.0, 14.0, 12.0, 13.0, 15.0, 13.0, 8.0, 8.0, 11.0, 10.0, 11.0, 13.0, 11.0, 13.0, 15.0, 10.0, 8.0, 10.0, 9.0, 8.0, 11.0, 13.0, 11.0, 8.0, 11.0, 15.0, 11.0, 12.0, 17.0, 12.0, 11.0, 18.0, 14.0, 15.0, 16.0, 7.0, 11.0, 15.0, 16.0, 13.0, 13.0, 13.0, 0.0, 11.0, 15.0, 14.0, 11.0, 13.0, 16.0, 14.0, 12.0, 8.0, 13.0, 13.0, 14.0, 7.0, 10.0, 16.0, 10.0, 13.0, 10.0, 14.0, 8.0, 16.0, 13.0, 12.0, 14.0, 12.0, 14.0, 16.0, 15.0, 13.0, 13.0, 10.0, 14.0, 8.0, 10.0, 10.0, 11.0, 12.0, 10.0, 12.0, 14.0, 17.0, 13.0, 14.0, 16.0, 15.0, 13.0, 16.0, 9.0, 16.0, 15.0, 11.0, 11.0, 15.0, 14.0, 12.0, 15.0, 11.0, 16.0, 14.0, 14.0, 15.0, 14.0, 14.0, 14.0, 16.0, 15.0, 12.0, 12.0, 14.0, 15.0, 13.0, 14.0, 13.0, 17.0, 14.0, 13.0, 14.0, 13.0, 13.0, 12.0, 10.0, 15.0, 14.0, 12.0, 12.0, 14.0, 12.0, 14.0, 13.0, 15.0, 13.0, 14.0, 14.0, 12.0, 11.0, 15.0, 14.0, 14.0, 10.0], 'EOY': [16.0, 16.0, 16.0, 14.0, 10.0, 14.0, 16.0, 14.0, 15.0, 15.0, 15.0, 11.0, 11.0, 15.0, 10.0, 14.0, 17.0, 14.0, 9.0, 15.0, 14.0, 16.0, 14.0, 13.0, 11.0, 13.0, 12.0, 14.0, 15.0, 13.0, 14.0, 15.0, 12.0, 19.0, 9.0, 13.0, 11.0, 14.0, 17.0, 17.0, 14.0, 13.0, 14.0, 10.0, 16.0, 15.0, 12.0, 11.0, 12.0, 14.0, 15.0, 10.0, 15.0, 14.0, 14.0, 15.0, 18.0, 15.0, 10.0, 10.0, 15.0, 15.0, 13.0, 15.0, 19.0, 13.0, 18.0, 20.0, 21.0, 17.0, 18.0, 17.0, 18.0, 17.0, 12.0, 16.0, 15.0, 18.0, 19.0, 17.0, 20.0, 11.0, 18.0, 19.0, 11.0, 12.0, 17.0, 20.0, 17.0, 15.0, 13.0, 18.0, 14.0, 17.0, 12.0, 12.0, 16.0, 12.0, 14.0, 15.0, 14.0, 10.0, 20.0, 13.0, 18.0, 20.0, 11.0, 20.0, 17.0, 20.0, 13.0, 17.0, 15.0, 18.0, 14.0, 13.0, 13.0, 18.0, 10.0, 13.0, 12.0, 18.0, 20.0, 20.0, 16.0, 18.0, 15.0, 20.0, 22.0, 18.0, 21.0, 18.0, 18.0, 18.0, 17.0, 16.0, 19.0, 16.0, 20.0, 19.0, 19.0, 20.0, 20.0, 14.0, 18.0, 20.0, 20.0, 18.0, 16.0, 21.0, 20.0, 18.0, 15.0, 14.0, 17.0, 19.0, 21.0, 14.0, 18.0, 15.0, 18.0, 21.0, 19.0, 17.0, 16.0, 16.0, 15.0, 20.0, 19.0, 16.0, 21.0, 17.0, 19.0, 15.0, 18.0, 20.0, 18.0, 20.0, 18.0, 16.0, 16.0]}
df = pd.DataFrame(data)
# replace numbers with categorical labels; could also create new columns
labels = ['Remedial', 'Below Proficient', 'Proficient', 'Advanced']
bins = [1, 11, 13, 15, np.inf]
df['BOY'] = pd.cut(x=df.BOY, labels=labels, bins=bins, right=True)
df['EOY'] = pd.cut(x=df.EOY, labels=labels, bins=bins, right=True)
# melt the relevant columns into a long form
dfm = df.melt(var_name='Tested', value_name='Proficiency')
# set the categorical label order, which makes the xaxis labels print in the specific order
dfm['Proficiency'] = pd.Categorical(dfm['Proficiency'], labels, ordered=True)
Groupby、百分比计算和绘图形状
# groupby and get the value counts
dfg = dfm.groupby('Tested')['Proficiency'].value_counts().reset_index(level=1, name='Size').rename({'level_1': 'Proficiency'}, axis=1)
# divide by the Tested value counts to get the percent
dfg['percent'] = dfg['Size'].div(dfm.Tested.value_counts()).mul(100).round(1)
# reshape to plot
dfp = dfg.reset_index().pivot(index='Proficiency', columns='Tested', values='percent')
# display(dfp)
Tested BOY EOY
Proficiency
Remedial 34.8 9.9
Below Proficient 28.7 12.7
Proficient 27.1 25.4
Advanced 8.8 51.9
情节
ax = dfp.plot(kind='bar', figsize=(15, 5), rot=0, color=['orchid', 'teal'])
# formatting
ax.yaxis.set_major_formatter(mtick.PercentFormatter())
ax.set_ylabel('Students at Proficiency Level', fontsize=18)
ax.set_xlabel('')
ax.set_title('Bushwick Middle Change in Proficiency Levels', fontsize=25)
ax.set_xticklabels(ax.get_xticklabels(), fontsize=25)
ax.legend(fontsize=25)
_ = plt.yticks(fontsize=15)
# add bar labels
for p in ax.containers:
ax.bar_label(p, fmt='%.1f%%', label_type='edge', fontsize=12)
# pad the spacing between the number and the edge of the figure
ax.margins(y=0.2)
- 查看条形标签匹配
dfp