基於客戶提取爲所屬客戶經理的信息

本地處理

#!/usr/bin/python
# -*- coding: utf-8 -*-

# UnicodeDecodeError: 'utf8' codec can't decode byte 0x9a in position 12的暫時解決方法——修改默認encoding
import sys
reload(sys)
sys.setdefaultencoding('utf-8')

from pyspark.sql import SparkSession
from pyspark import SparkContext
from pyspark.sql.functions import *  # 爲使用dataframe的方法
import re
from pyspark.sql.types import *
import datetime
import pandas as pd


conf = SparkConf().setAppName("miniProject").setMaster("local")
sc1 = SparkContext.getOrCreate(conf)
spark = SparkSession(sc1)

1. 客戶經理信息簡單查詢

# 客戶經理數據——已經將代碼的csv傳到本地,讀取
cm_df = pd.read_csv('file:///home/hadoop/xxx/project_0509_khjl/secret.csv', sep=',', encoding='utf-8')
cm_df = cm_df.ix[:,1:-1]
cm_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 104467 entries, 0 to 104466
Data columns (total 12 columns):
staff_id             104467 non-null int64
staff_name           104467 non-null object
user_id_card_edit    104467 non-null object
id_county            104431 non-null object
id_province          104431 non-null object
id_city              104431 non-null object
id                   104467 non-null int64
role_id              104467 non-null int64
presona_id           104467 non-null int64
status               104467 non-null int64
city_number          104467 non-null int64
num                  104467 non-null int64
dtypes: int64(7), object(5)
memory usage: 9.6+ MB
cm_df.describe()
staff_id id role_id presona_id status city_number num
count 104467.000000 104467.000000 104467.000000 104467.000000 104467.000000 104467.0 104467.000000
mean 191295.747298 191295.747298 58.994228 3135.484143 1.857151 0.0 -0.000029
std 78064.637688 78064.637688 0.400920 2029.885308 0.349920 0.0 0.031703
min 218.000000 218.000000 0.000000 43.000000 1.000000 0.0 -4.000000
25% 162123.500000 162123.500000 59.000000 1603.000000 2.000000 0.0 0.000000
50% 206533.000000 206533.000000 59.000000 2374.000000 2.000000 0.0 0.000000
75% 247621.500000 247621.500000 59.000000 4828.000000 2.000000 0.0 0.000000
max 294921.000000 294921.000000 155.000000 9002.000000 2.000000 0.0 4.000000
# 城市數據
# 已經將代碼的csv傳到cluster,放在hdfs根目錄
ctcode_df = pd.read_csv('file:///home/hadoop/xxx/project_0509_khjl/xz.csv', sep=',', encoding='utf-8')
ctcode_df.head()
ctcode_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3219 entries, 0 to 3218
Data columns (total 2 columns):
xzqhdm         3219 non-null int64
xzqhdm_name    3219 non-null object
dtypes: int64(1), object(1)
memory usage: 50.4+ KB
ctcode_df['xzqhdm'] = ctcode_df['xzqhdm'].astype(str)
ctcode_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3219 entries, 0 to 3218
Data columns (total 2 columns):
xzqhdm         3219 non-null object
xzqhdm_name    3219 non-null object
dtypes: object(2)
memory usage: 50.4+ KB
# 鏈接
tmp_county = pd.merge(left=cm_df, right=ctcode_df, left_on='id_county', right_on='xzqhdm', how='left')


tmp_province = pd.merge(left=tmp_county, right=ctcode_df, left_on='id_province', right_on='xzqhdm', how='left')

tmp_city = pd.merge(left=tmp_province, right=ctcode_df, left_on='id_city', right_on='xzqhdm', how='left')

tmp_city.drop(['xzqhdm_x','xzqhdm_y','xzqhdm'], axis=1, inplace=True)
tmp_city.head()
tmp_city.count()
staff_id             104467
staff_name           104467
user_id_card_edit    104467
id_county            104431
id_province          104431
id_city              104431
id                   104467
role_id              104467
presona_id           104467
status               104467
city_number          104467
num                  104467
xzqhdm_name_x         45367
xzqhdm_name_y         65507
xzqhdm_name           51701
dtype: int64
# 不同省份的人數
tmp_city.groupby(['xzqhdm_name_y'])['staff_id'].count()
xzqhdm_name_y
上海市          240
雲南省         1699
內蒙古自治區      1731
北京市           79
吉林省         2200
四川省         4520
天津市          274
寧夏回族自治區      369
安徽省         3906
山東省         5584
山西省         1285
廣東省         3889
廣西壯族自治區     1823
新疆維吾爾自治區      55
江蘇省         4799
江西省         2386
河北省         3110
河南省         4545
浙江省         2202
海南省          337
湖北省         3596
湖南省         2706
甘肅省         1031
福建省         3204
西藏自治區          2
貴州省         1230
遼寧省         2562
重慶市         1347
陝西省         1929
青海省          132
黑龍江省        2735
Name: staff_id, dtype: int64
dis_ct_prov = tmp_city.groupby(['xzqhdm_name_y'])['staff_id'].count().sort_values(ascending=False) 
dis_ct_city = tmp_city.groupby(['xzqhdm_name_x'])['staff_id'].count().sort_values(ascending=False) 
dis_ct_county = tmp_city.groupby(['xzqhdm_name'])['staff_id'].count().sort_values(ascending=False) 
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib notebook
from pylab import *  
mpl.rcParams['font.sans-serif'] = ['SimHei']  
mpl.rcParams['axes.unicode_minus'] = False  

fig = plt.figure()
dis_ct_prov.plot(kind='bar', )
plt.title('province')
plt.show()
<IPython.core.display.Javascript object>
dis_ct_prov[:10]
xzqhdm_name_y
山東省     5584
江蘇省     4799
河南省     4545
四川省     4520
安徽省     3906
廣東省     3889
湖北省     3596
福建省     3204
河北省     3110
黑龍江省    2735
Name: staff_id, dtype: int64
fig2 = plt.figure()
dis_ct_city.plot(kind='bar', )
plt.title('city')
plt.show()
<IPython.core.display.Javascript object>
dis_ct_city[:10]
xzqhdm_name_x
市中區    241
惠安縣    187
睢寧縣    177
寧海縣    164
沭陽縣    161
灌雲縣    159
興化市    148
仙遊縣    142
臨泉縣    135
沛縣     134
Name: staff_id, dtype: int64
fig3 = plt.figure()
dis_ct_county.plot(kind='bar', )
plt.title('city')
plt.show()
<IPython.core.display.Javascript object>
dis_ct_county[:10]
xzqhdm_name
徐州市    892
阜陽市    618
泉州市    604
漳州市    566
濰坊市    556
淮安市    526
鹽城市    521
湛江市    509
南充市    508
宿遷市    505
Name: staff_id, dtype: int64
# 查看客戶經理的在職狀態,1-在職
tmp_city.groupby('status').count(staff_id)

2、客戶信息處理

#!/usr/bin/python
# -*- coding: utf-8 -*-

import pandas as pd
import numpy as np

# from pyspark.sql import functions as F

def fraud_normal(already_num,cd):
    if cd>=2:
        flag='loan_fraud'
    else:
        if already_num>4:
            flag='loan_normal'
        else:
            flag='drop'
    return flag
# Fraud_normal=F.udf(fraud_normal)

# 讀入整理好的客戶信息,已經將代碼的csv傳到本地,讀取
cl_df = pd.read_csv('file:///home/hadoop/xxx/project_0509_khjl/datasource/client.csv', sep=',', encoding='utf-8')
cl_df = cl_df.ix[:,1:-1]
cl_df[['cd', 'already_num']].sort_values(['cd', 'already_num']).head()
cd already_num
160569 -8.0 17
114941 -3.0 10
1041 0.0 0
1176 0.0 0
2354 0.0 0
cl_df[cl_df['loan_type']==2].head()
cl_df['flag'] = cl_df[cl_df['loan_type']==2].apply(lambda x: fraud_normal(x['already_num'],x['cd']), axis=1)
cl_df_flaged=cl_df[cl_df['flag']!='drop']
cl_df_flaged.columns, cl_df_flaged.count()
(Index([             u'id',          u'id_num',      u'account_id',
          u'loan_staff_id',       u'education',       u'child_sum',
                 u'is_car',  u'account_number',        u'zs_money',
                u'zipCode',          u'status',             u'amt',
              u'loan_type',              u'cd',            u'year',
        u'intopieces_date',           u'count',   u'total_account',
        u'avg_month_men_a',       u'repay_num',     u'already_num',
             u'user_phone',     u'presona_pid',            u'flag'],
       dtype='object'), id                 771956
 id_num             771956
 account_id         771956
 loan_staff_id      771956
 education          771956
 child_sum          771806
 is_car             771956
 account_number     771956
 zs_money           771956
 zipCode            254976
 status             771956
 amt                771956
 loan_type          771956
 cd                 768909
 year               771956
 intopieces_date    771956
 count              771956
 total_account      771956
 avg_month_men_a    771956
 repay_num          771956
 already_num        771956
 user_phone         752550
 presona_pid        751350
 flag               271373
 dtype: int64)

3. 鏈接經理和客戶

def et_id_sex(x):
    
    if len(x)==18:
        if float(x[16])%2 == 0:
            sex = 0 # 'female'
        else:
            sex = 1 # 'male'
        return sex

    elif len(x)==15:
        if float(x[-1])%2 == 0:
            sex = 0 # 'female'
        else:
            sex = 1 # 'male'
        return sex
    else:
        return None
import datetime

def et_id_age(x):
            
    if len(x)==18:
        y_m_d = x[6:15]
        age = calculate_age(y_m_d)
        return age

    elif len(x)==15:
        y_m_d = x[6:12]
        age = calculate_age(y_m_d)
        return age
    else:
        return None

def calculate_age(input_born, today=[2018,5,10]):
        '''
        : input_born: string, len=8
        '''

        y_born = input_born[0:4]
        m_born = input_born[4:6]
        d_born = input_born[6:8]
        
        if (int(m_born)  in range(1, 13, 1) and int(d_born) in range(1, 32, 1)):
            born = datetime.date(int(y_born), int(m_born), int(d_born))
            today = datetime.date(today[0],today[1],today[2])

            born_days = born - datetime.date(born.year-1, 12, 31)  #減去上一年最後一天,可得解
            target_days = today - datetime.date(today.year-1, 12, 31)
            sub_days = target_days - born_days
            sub_days = sub_days.days

            if today > born:
                years = today.year-born.year
                if sub_days >= 0:
                    if sub_days>=0 and sub_days<183:
                        return years
                    else:
                        # sub_days in range(183,366,1):
                        return years+1

                else:
                    sub_days = sub_days*(-1)
                    if sub_days>=0 and sub_days<183:
                        return years
                    else:
                        return years-1
            else:
                print('error_date')
                return None
            
        else:
            return None

3.1 客戶經理數據讀入與處理

# 城市區劃數據,已經將代碼的csv傳到cluster,放在hdfs根目錄
ctcode_df = pd.read_csv('/home/hadoop/xxx/project_0509_khjl/datasource/xz.csv', sep=',', encoding='utf-8')
ctcode_df['xzqhdm'] = ctcode_df['xzqhdm'].astype(str)

# 客戶經理數據——已經將代碼的csv傳到本地,讀取
cm_df = pd.read_csv('/home/hadoop/xxx/project_0509_khjl/datasource/manager.csv', sep=',', encoding='utf-8')
cm_df = cm_df.ix[:,1:-1]

# 城市地點
tmp_county = pd.merge(left=cm_df, right=ctcode_df, left_on='id_county', right_on='xzqhdm', how='left')

tmp_province = pd.merge(left=tmp_county, right=ctcode_df, left_on='id_province', right_on='xzqhdm', how='left')

tmp_city = pd.merge(left=tmp_province, right=ctcode_df, left_on='id_city', right_on='xzqhdm', how='left')

# 性別
tmp_city['sex'] = tmp_city['user_id_card_edit'].apply(et_id_sex)

# 年齡
tmp_city['age'] = tmp_city['user_id_card_edit'].apply(et_id_age)

# 重命名客戶經理的在職狀態字段,status
tmp_city.rename(columns={u'status':'status_m'}, inplace=True)

cm_df_location = tmp_city.drop(['xzqhdm_x','xzqhdm_y','xzqhdm'], axis=1, inplace=False)
/home/hadoop/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (4,5,6) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)


error_date

3.1鏈接經理與客戶信息

joint_cm_cl = pd.merge(left=cm_df_location, right=cl_df_flaged, left_on='staff_id', right_on='loan_staff_id', how='left')
joint_cm_cl.tail()
cl_df_flaged['loan_staff_id'].dtypes
dtype('int64')
cm_df_location['staff_id'].dtypes
dtype('int64')
cm_df_location.sort_values('staff_id').tail()
cl_df_flaged.sort_values('loan_staff_id').tail()
joint_cm_cl.count()
staff_id             740250
staff_name           740250
user_id_card_edit    740250
id_county            740214
id_province          740214
id_city              740214
id_x                 740250
role_id              740250
presona_id           740250
status_m             740250
city_number          740250
num                  740250
xzqhdm_name_x        314714
xzqhdm_name_y        461990
xzqhdm_name          362371
sex                  740214
age                  740122
id_y                 690472
id_num               690472
account_id           690472
loan_staff_id        690472
education            690472
child_sum            690359
is_car               690472
account_number       690472
zs_money             690472
zipCode              221233
status               690472
amt                  690472
loan_type            690472
cd                   687819
year                 690472
intopieces_date      690472
count                690472
total_account        690472
avg_month_men_a      690472
repay_num            690472
already_num          690472
user_phone           690472
presona_pid          689456
flag                 236757
dtype: int64
cl_df_flaged.count()
id                 771956
id_num             771956
account_id         771956
loan_staff_id      771956
education          771956
child_sum          771806
is_car             771956
account_number     771956
zs_money           771956
zipCode            254976
status             771956
amt                771956
loan_type          771956
cd                 768909
year               771956
intopieces_date    771956
count              771956
total_account      771956
avg_month_men_a    771956
repay_num          771956
already_num        771956
user_phone         752550
presona_pid        751350
flag               271373
dtype: int64
joint_cm_cl.groupby('flag').agg('count')
staff_id staff_name user_id_card_edit id_county id_province id_city id_x role_id presona_id status_m ... cd year intopieces_date count total_account avg_month_men_a repay_num already_num user_phone presona_pid
flag
loan_fraud 64186 64186 64186 64186 64186 64186 64186 64186 64186 64186 ... 64186 64186 64186 64186 64186 64186 64186 64186 64186 64104
loan_normal 172571 172571 172571 172571 172571 172571 172571 172571 172571 172571 ... 172569 172571 172571 172571 172571 172571 172571 172571 172571 172352

2 rows × 40 columns

joint_cm_cl.ix[584100:584129,'flag'].notnull()
584100     True
584101     True
584102    False
584103    False
584104     True
584105    False
584106     True
584107    False
584108    False
584109    False
584110     True
584111    False
584112    False
584113     True
584114    False
584115    False
584116    False
584117    False
584118    False
584119    False
584120    False
584121    False
584122     True
584123    False
584124     True
584125    False
584126     True
584127    False
584128     True
584129     True
Name: flag, dtype: bool

3.3 選取感興趣數據一

feature_use1 = ['staff_id','id_province', 'sex', 'status_m', 'age', 'num', 'presona_id', 'city_number','flag']
dataset_use1 = joint_cm_cl.loc[:, feature_use1]
dataset_use1.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 740250 entries, 0 to 740249
Data columns (total 9 columns):
staff_id       740250 non-null int64
id_province    740214 non-null object
sex            740214 non-null float64
status_m       740250 non-null int64
age            740122 non-null float64
num            740250 non-null int64
presona_id     740250 non-null int64
city_number    740250 non-null int64
flag           236757 non-null object
dtypes: float64(2), int64(5), object(2)
memory usage: 56.5+ MB
dataset_use1.describe()
/home/hadoop/anaconda2/lib/python2.7/site-packages/numpy/lib/function_base.py:4291: RuntimeWarning: Invalid value encountered in percentile
  interpolation=interpolation)
staff_id sex status_m age num presona_id city_number
count 740250.000000 740214.000000 740250.000000 740122.000000 740250.000000 740250.000000 740250.0
mean 206828.474852 0.578088 1.715630 28.350092 -0.000009 3265.831038 0.0
std 49171.210610 0.493865 0.451114 4.882471 0.018777 2031.866055 0.0
min 218.000000 0.000000 1.000000 18.000000 -4.000000 43.000000 0.0
25% 184968.000000 NaN 1.000000 NaN 0.000000 1676.000000 0.0
50% 214036.000000 NaN 2.000000 NaN 0.000000 2457.000000 0.0
75% 238753.000000 NaN 2.000000 NaN 0.000000 5110.000000 0.0
max 294921.000000 1.000000 2.000000 1716.000000 4.000000 9002.000000 0.0

經驗證,其中sex=NaN的客戶經理,溯源其身份證,通過函數驗證身份證信息屬假的,故考慮去除sex=NaN的客戶經理信息(相應的flag也是NaN)

# 去除sex=NaN的觀測
dataset_use1 = dataset_use1[dataset_use1['sex'].notnull()]
# 去除flag==NaN的觀測。這部分值可能是由於在生成flag的過程中,cd=None或者already_num=None所帶來的。
dataset_use1 = dataset_use1[dataset_use1['flag'].notnull()]
# 去除sage=NaN的觀測
dataset_use1[dataset_use1['age'].isnull()]
dataset_use1 = dataset_use1[dataset_use1['age'].notnull()]
dataset_use1[dataset_use1['flag'].isnull()].groupby('staff_id').count()['sex']
Series([], Name: sex, dtype: int64)
# 計算客戶經理對應的客戶的違約情況,0-1,無違約,100%違約。獲取客戶經理對應的客戶數量
def groupby_calcu(data_df, goal_id='staff_id', flag_id='flag'):
    whole_num = data_df.groupby(goal_id).count()['sex']
    whole_num.rename('whole_count', inplace=True)
    
    loan_fraud_num = data_df[data_df[flag_id]=='loan_fraud'].groupby(goal_id).count()['sex']
    loan_fraud_num.rename('fraud_count', inplace=True)
    
    tmp_df = pd.concat([whole_num, loan_fraud_num], axis=1, join='outer')
    tmp_df['fraud_count'] = tmp_df['fraud_count'].fillna(value=0, inplace=False)
    
    # 類型轉換,減少內存
    tmp_df['whole_count'] = tmp_df['whole_count'].astype('int32')
    tmp_df['fraud_count'] = tmp_df['fraud_count'].astype('float32')
    
    flag_new_name = '%s_perc' %(flag_id)
    
    # 方式1:突破內存限制
    for i in tmp_df.index.values:
        tmp_df.ix[i, flag_new_name] = (tmp_df.ix[i, 'fraud_count']/tmp_df.ix[i, 'whole_count']).astype('float32')

    # 方式2:受到內存限制,報錯!!!死機!!!——不推薦!!!
    # np.float32(wf_df['f_count'][:100000]/wf_df['whole_count'][:100000])
    
    df = pd.DataFrame({goal_id:tmp_df.index, flag_new_name:tmp_df[flag_new_name], 'num_client':tmp_df['whole_count']}, columns=[goal_id, flag_new_name, 'num_client'])

    unique_data_df = data_df.drop_duplicates(goal_id).sort_values(goal_id)
    unique_data_df_final = pd.merge(left=unique_data_df, right=df, left_on=goal_id, right_on=goal_id, how='left')
    
    whole_num, loan_fraud_num, tmp_df, df, unique_data_df = None, None, None, None, None
    del whole_num, loan_fraud_num, tmp_df, df, unique_data_df
    
    return unique_data_df_final
 
dataset_use2 = groupby_calcu(dataset_use1.ix[:,])
dataset_use2.head()
staff_id id_province sex status_m age num presona_id city_number flag flag_perc num_client
0 4735 350000 0.0 2 28.0 0 112 0 loan_fraud 1.0 1
1 4857 620000 0.0 2 29.0 0 43 0 loan_fraud 1.0 2
2 5365 340000 1.0 2 32.0 0 141 0 loan_normal 0.0 2
3 5373 410000 1.0 2 28.0 0 142 0 loan_normal 0.0 2
4 5910 410000 0.0 2 25.0 0 1110 0 loan_normal 0.5 4
# w_num1 = dataset_use1.groupby('staff_id').count()['sex']
# w_num1 = w_num1.rename('whole_count')
# f_num1 = dataset_use1[dataset_use1['flag']=='fraud_count'].groupby('staff_id').count()['sex']
# f_num1 = f_num1.rename('f_count')

# wf_df = pd.concat([w_num1, f_num1], axis=1, join='outer')
# wf_df['f_count'] = wf_df['f_count'].fillna(value=int(0), inplace=False)

# wf_df['whole_count'] = wf_df['whole_count'].astype('int32')
# wf_df['f_count'] = wf_df['f_count'].astype('float32')
# wf_df.dtypes
dataset_use2.describe()
staff_id sex status_m age num presona_id city_number flag_perc num_client
count 39374.000000 39374.000000 39374.000000 39374.000000 39374.000000 39374.000000 39374.0 39374.000000 39374.000000
mean 219081.539468 0.616727 1.799055 27.900797 0.000000 3240.945421 0.0 0.268053 6.012851
std 42961.619859 0.486190 0.400712 4.126361 0.014254 2052.084694 0.0 0.313801 7.872544
min 4735.000000 0.000000 1.000000 19.000000 -1.000000 43.000000 0.0 0.000000 1.000000
25% 196255.250000 0.000000 2.000000 25.000000 0.000000 1676.000000 0.0 0.000000 1.000000
50% 226812.500000 1.000000 2.000000 28.000000 0.000000 2449.000000 0.0 0.181818 3.000000
75% 249347.750000 1.000000 2.000000 30.000000 0.000000 4961.000000 0.0 0.444444 7.000000
max 286704.000000 1.000000 2.000000 52.000000 2.000000 9002.000000 0.0 1.000000 103.000000
# flagd的處理
def flag_pd(x):
    if x=='loan_normal':
        label = int(1)
        
    else:
        label = int(0)
    return label

dataset_use2['flag_01'] = dataset_use2['flag'].apply(flag_pd)

3.3選取感興趣的字段二——爲建模準備

dataset_use3 = dataset_use2[['sex','age','num_client', 'status_m', 'presona_id', 'id_province', 'flag_perc']].reset_index()
dataset_use3[dataset_use3["flag_perc"] <= 0.5]['flag_perc'] = 0
dataset_use3[dataset_use3["flag_perc"] > 0.5]['flag_perc'] = 1
/home/hadoop/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
/home/hadoop/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  from ipykernel import kernelapp as app
dataset_use3.dtypes, dataset_use3.head()
(index            int64
 sex            float64
 age            float64
 num_client       int32
 status_m         int64
 presona_id       int64
 id_province     object
 flag_perc      float32
 dtype: object,
    index  sex   age  num_client  status_m  presona_id id_province  flag_perc
 0      0  0.0  28.0           1         2         112      350000        1.0
 1      1  0.0  29.0           2         2          43      620000        1.0
 2      2  1.0  32.0           2         2         141      340000        0.0
 3      3  1.0  28.0           2         2         142      410000        0.0
 4      4  0.0  25.0           4         2        1110      410000        0.5)
dataset_use3['sex'] = dataset_use3['sex'].astype(int).astype(str)
dataset_use3['age'] = dataset_use3['age'].astype(int)
dataset_use3['status_m'] = dataset_use3['status_m'].astype(int).astype(str)
dataset_use3['presona_id'] = dataset_use3['presona_id'].astype(str)
dataset_use3['id_province'] = dataset_use3['id_province'].astype(int).astype(str)
dataset_use3['flag_perc'] = dataset_use3['flag_perc'].astype(int)

data_tmp_onehot = pd.get_dummies(dataset_use3[['sex','age', 'num_client', 'status_m', 'id_province']])
data_tmp_onehot['flag_perc'] = dataset_use3['flag_perc']
data_tmp_onehot.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 39374 entries, 0 to 39373
Data columns (total 38 columns):
age                   39374 non-null int64
num_client            39374 non-null int32
sex_0                 39374 non-null float64
sex_1                 39374 non-null float64
status_m_1            39374 non-null float64
status_m_2            39374 non-null float64
id_province_110000    39374 non-null float64
id_province_120000    39374 non-null float64
id_province_130000    39374 non-null float64
id_province_140000    39374 non-null float64
id_province_150000    39374 non-null float64
id_province_210000    39374 non-null float64
id_province_220000    39374 non-null float64
id_province_230000    39374 non-null float64
id_province_310000    39374 non-null float64
id_province_320000    39374 non-null float64
id_province_330000    39374 non-null float64
id_province_340000    39374 non-null float64
id_province_350000    39374 non-null float64
id_province_360000    39374 non-null float64
id_province_370000    39374 non-null float64
id_province_400000    39374 non-null float64
id_province_410000    39374 non-null float64
id_province_420000    39374 non-null float64
id_province_430000    39374 non-null float64
id_province_440000    39374 non-null float64
id_province_450000    39374 non-null float64
id_province_460000    39374 non-null float64
id_province_500000    39374 non-null float64
id_province_510000    39374 non-null float64
id_province_520000    39374 non-null float64
id_province_530000    39374 non-null float64
id_province_610000    39374 non-null float64
id_province_620000    39374 non-null float64
id_province_630000    39374 non-null float64
id_province_640000    39374 non-null float64
id_province_650000    39374 non-null float64
flag_perc             39374 non-null int64
dtypes: float64(35), int32(1), int64(2)
memory usage: 11.3 MB

3.4繪圖與描述統計分析

import matplotlib.pyplot as plt
import seaborn as sns
% matplotlib notebook

3.4.1單變量的分佈情況

flg = plt.figure()
<IPython.core.display.Javascript object>

<img src="data:image/png;base64,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

發表評論
所有評論
還沒有人評論,想成為第一個評論的人麼? 請在上方評論欄輸入並且點擊發布.
相關文章