ML之FE：对人类性别相关属性数据集进行数据特征分布可视化分析与挖掘

傷城~ 2023-01-19 08:27 15阅读 0赞

**目录**

对人类性别相关属性数据集进行数据特征分布可视化分析与挖掘

输出结果

实现代码

--------------------

# 对人类性别相关属性数据集进行数据特征分布可视化分析与挖掘 #

## 输出结果 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 1][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 2][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 3][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 4][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 5][]

## 实现代码 ##

# coding: utf8
    import pandas as pd
    import matplotlib.pyplot as plt
    
    
    # ML之FE：对人类性别相关属性数据集进行数据特征分布可视化分析与挖掘
    
    
    #1、定义数据集
    # 头发(长发/短发)、身高、下巴(棱角/圆滑)、胡长(mm)、皮肤、体重
    contents={"name": ['Mary', 'Bob',  'Lisa', 'Tom',  'Alan', 'Jason','Sophia', 'Aiden',  'Sarah', 'Miqi', 'Temp01', 'Temp02'],
              "age":  [ 16,     24,      19,     20,      33,     23,     29,      31,        34,      24,       27,        30],
              "Hair": ['长发', '短发',  '长发', '短发', '长发', '短发', '长发',    '长发',   '长发',  '长发',    '短发',   '长发'],
            "Height": [158,     175,     162,    170,    175,    168,    166,     169,       164,     157,      182,        161],
             "Jaw":   ['圆滑', '棱角',  '圆滑', '棱角', '圆滑', '圆滑', '圆滑',    '棱角',  '圆滑',    '圆滑',    '棱角',   '圆滑'], 
            "Beard":  [2,       7,       3,       5,      2,      3,      5,        6,         3,      4,         5,         3],
             "Skin":  ['细腻', '粗糙',  '细腻', '粗糙', '细腻', '粗糙', '细腻',   '粗糙',   '细腻',   '细腻',    '粗糙',    '粗糙'], 
            "Weight": [99,     143,      105,     135,    120,    160,    95,      145,      125,     112,       155,        100],
             "Sex":   ['女性', '男性', '女性',  '男性', '男性', '男性',  '女性',   '男性',  '女性',   '女性',    '男性',    '女性'], 
              }
    data_frame = pd.DataFrame(contents)
    print(type(data_frame))
    
    data_name = 'HumanGender_RelatedAttributes'
    col_cat='Jaw'
    label_name='Sex'
    
    
    for col in data_frame.columns[1:-2]:
        if data_frame[col].dtypes in ['object']:
            print(col)
            # T1、采用函数
            col_cats=[col,label_name]
    #         SNCountPlot(col_cats,data_frame,imgName='')
            
            # T2、自定义函数？？？
            x_subname = list(data_frame[col].value_counts().to_dict().keys())
            label_y1 = list(data_frame[label_name].value_counts().to_dict().keys())[0]
            label_y2 = list(data_frame[label_name].value_counts().to_dict().keys())[1]
            y1=list(data_frame[data_frame[label_name]==label_y1][col].value_counts().to_dict().values())
            y2=list(data_frame[data_frame[label_name]==label_y2][col].value_counts().to_dict().values())
            print(x_subname)
            print(label_y1,label_y2)
            print(y1,y2)
            
    #       # T2、自定义函数？？？
    #         y01Lists,y02Lists=[],[]
    #         for x in x_subname:
    #             if x not in data_frame[data_frame[label_name]==label_y2][col].value_counts(dropna=False).to_dict().keys():
    #                 pass
    #             else:
    #                 
    #                 y01=data_frame[data_frame[label_name]==label_y1][col].value_counts(dropna=False).to_dict()[x]
    #                 y02=data_frame[data_frame[label_name]==label_y2][col].value_counts(dropna=False).to_dict()[x]
    #                 y01Lists.append(y01)
    #                 y02Lists.append(y02)
    #         print(y01Lists,y02Lists)
            
            
            DoubleBarAddText(y1,y2, col,label_name, x_subname,label_y1,label_y2,data_name)
        else:
            Num_col_Plot2_ByLabels(data_name,data_frame,label_name,col)

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70]: /images/20221021/dce198f5ec71437ba8edbd11c00bfbd7.png
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 2]: /images/20221021/efc2922d2ace4a818f10e8d57cd13e95.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 3]: /images/20221021/c889d9c022724f82a42cea1887204bf8.png
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