matplotlib 如何获取每行的百分比并可视化分类数据

7bsow1i6 于 2023-04-06 发布在其他

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我正在对Loan Prediction数据集（Pandas dataframe）进行探索性数据分析。该dataframe有两列：Property_Area的值有三种类型- Rural，Urban，Semiurban。另一列是Loan_Status明智的值有两种类型- Y，N。我想绘制一个像这样的图表：沿着X轴应该有Property_Area，并且，对于每种类型的3个区域，我想显示沿着Y轴接受或拒绝贷款的百分比。如何做到这一点？
以下是我的数据示例：

data = pd.DataFrame({'Loan_Status':['N','Y','Y','Y','Y','N','N','Y','N','Y','N'], 
       'Property_Area': ['Rural', 'Urban','Urban','Urban','Urban','Urban',
       'Semiurban','Urban','Semiurban','Rural','Semiurban']})

我试着这样做：

status = data['Loan_Status']
index = data['Property_Area']
df = pd.DataFrame({'Loan Status' : status}, index=index)
ax = df.plot.bar(rot=0)

data is the dataframe for the original dataset

输出：x1c 0d1x

**编辑：**我可以做我想做的事情，但是，为此，我不得不写一段很长的代码：

new_data = data[['Property_Area', 'Loan_Status']].copy()
count_rural_y = new_data[(new_data.Property_Area == 'Rural') & (data.Loan_Status == 'Y') ].count()
count_rural = new_data[(new_data.Property_Area == 'Rural')].count()
#print(count_rural[0])
#print(count_rural_y[0])
rural_y_percent = (count_rural_y[0]/count_rural[0])*100
#print(rural_y_percent)

#print("-"*50)

count_urban_y = new_data[(new_data.Property_Area == 'Urban') & (data.Loan_Status == 'Y') ].count()
count_urban = new_data[(new_data.Property_Area == 'Urban')].count()
#print(count_urban[0])
#print(count_urban_y[0])
urban_y_percent = (count_urban_y[0]/count_urban[0])*100
#print(urban_y_percent)

#print("-"*50)

count_semiurban_y = new_data[(new_data.Property_Area == 'Semiurban') & (data.Loan_Status == 'Y') ].count()
count_semiurban = new_data[(new_data.Property_Area == 'Semiurban')].count()
#print(count_semiurban[0])
#print(count_semiurban_y[0])
semiurban_y_percent = (count_semiurban_y[0]/count_semiurban[0])*100
#print(semiurban_y_percent)

#print("-"*50)

objects = ('Rural', 'Urban', 'Semiurban')
y_pos = np.arange(len(objects))
performance = [rural_y_percent,urban_y_percent,semiurban_y_percent]
plt.bar(y_pos, performance, align='center', alpha=0.5)
plt.xticks(y_pos, objects)
plt.ylabel('Loan Approval Percentage')
plt.title('Area Wise Loan Approval Percentage')

plt.show()

输出：

如果可能的话，你能给我一个更简单的方法吗？

matplotlib

来源：https://stackoverflow.com/questions/53108063/how-to-get-percentages-for-each-row-and-visualize-categorical-data

1条答案

按热度按时间

b4qexyjb1#

Pandas `Crosstabs` with `normalize`会让这个过程变得简单

在pandas Dataframe 中获取2+列并获取 * 每行 * 的百分比的一种简单方法是使用pandascrosstab函数和normalize = 'index'

以下是交叉表函数将如何查找它：

# Crosstab with "normalize = 'index'". 
df_percent = pd.crosstab(data.Property_Area,data.Loan_Status,
                         normalize = 'index').rename_axis(None)

# Multiply all percentages by 100 for graphing. 
df_percent *= 100

这将输出df_percent，如下所示：*

Loan_Status          N          Y
Rural        50.000000  50.000000
Semiurban    66.666667  33.333333
Urban        16.666667  83.333333

然后你可以很容易地将其绘制到条形图中：

# Plot only approvals as bar graph. 
plt.bar(df_percent.index, df_percent.Y, align='center', alpha=0.5)
plt.ylabel('Loan Approval Percentage')
plt.title('Area Wise Loan Approval Percentage')

plt.show()

并获取结果图表：*

Here you can see the code working in google colab
下面是我为这个答案生成的示例 Dataframe ：

data = pd.DataFrame({'Loan_Status':['N','Y','Y','Y','Y','N','N','Y','N','Y','Y'
   ], 'Property_Area': ['Rural', 'Urban','Urban','Urban','Urban','Urban',
   'Semiurban','Urban','Semiurban','Rural','Semiurban']})

创建以下示例数据框：

Loan_Status Property_Area
0            N         Rural
1            Y         Urban
2            Y         Urban
3            Y         Urban
4            Y         Urban
5            N         Urban
6            N     Semiurban
7            Y         Urban
8            N     Semiurban
9            Y         Rural
10           Y     Semiurban

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matplotlib 如何获取每行的百分比并可视化分类数据

1条答案

Pandas `Crosstabs` with `normalize`会让这个过程变得简单

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matplotlib 如何获取每行的百分比并可视化分类数据

1条答案

Pandas Crosstabs with normalize会让这个过程变得简单

相关问题

热门标签

最新问答

Pandas `Crosstabs` with `normalize`会让这个过程变得简单