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

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

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

# 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)
赞 (0)

相关推荐