本文主要是介绍分析Airbnb新用户订房地点，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
首先先导入包
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn as sk
import datetime
import os
import seaborn as sns-----------------------------------------------------------------------------------
其次导入数据
train = pd.read_csv('/Users/qinpeng/Documents/airbnb/train_users_2.csv',sep=',')
test = pd.read_csv('/Users/qinpeng/Documents/airbnb/test_users.csv',sep=',')-----------------------------------------------------------------------------------
观测数据
train.head()
train.shape
test.shape
print(train.shape)
print(test.shape)
train.info()
test.info()(213451, 16)
(62096, 15)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 213451 entries, 0 to 213450
Data columns (total 16 columns):
id 213451 non-null object
date_account_created 213451 non-null object
timestamp_first_active 213451 non-null int64
date_first_booking 88908 non-null object
gender 213451 non-null object
age 125461 non-null float64
signup_method 213451 non-null object
signup_flow 213451 non-null int64
language 213451 non-null object
affiliate_channel 213451 non-null object
affiliate_provider 213451 non-null object
first_affiliate_tracked 207386 non-null object
signup_app 213451 non-null object
first_device_type 213451 non-null object
first_browser 213451 non-null object
country_destination 213451 non-null object
dtypes: float64(1), int64(2), object(13)
memory usage: 26.1+ MB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 62096 entries, 0 to 62095
Data columns (total 15 columns):
id 62096 non-null object
date_account_created 62096 non-null object
timestamp_first_active 62096 non-null int64
date_first_booking 0 non-null float64
gender 62096 non-null object
age 33220 non-null float64
signup_method 62096 non-null object
signup_flow 62096 non-null int64
language 62096 non-null object
affiliate_channel 62096 non-null object
affiliate_provider 62096 non-null object
first_affiliate_tracked 62076 non-null object
signup_app 62096 non-null object
first_device_type 62096 non-null object
first_browser 62096 non-null object
dtypes: float64(2), int64(2), object(11)
memory usage: 7.1+ MB可以看出train共有观测213451条，16个变量，test共有62096条，15个变量
可以看到里面缺失的列的数量
date_first_booking 0 non-null float64为什么全是空置，因为需要预测住房地点，所以此列为全空
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train_features = train.columns
test_features = test.columns
np.setdiff1d(train_features,test_features)
dac_train = train.date_account_created.value_counts()
dac_test = test.date_account_created.value_counts()print('training dataset:\n')
print(dac_train.describe())
print('\n' + '***' * 15 + '\n')
print('test dataset:\n')
print(dac_test.describe())
print('training dateset:')
print(dac_train.head())
print(dac_train.tail())training dataset:count 1634.000000
mean 130.630967
std 139.327895
min 1.000000
25% 15.000000
50% 79.000000
75% 201.000000
max 674.000000
Name: date_account_created, dtype: float64*********************************************test dataset:count 92.000000
mean 674.956522
std 122.568116
min 401.000000
25% 606.750000
50% 662.000000
75% 739.000000
max 1105.000000
Name: date_account_created, dtype: float64
training dateset:
2014-05-13 674
2014-06-24 670
2014-06-25 636
2014-05-20 632
2014-05-14 622
Name: date_account_created, dtype: int64
2010-03-09 1
2010-04-24 1
2010-01-23 1
2010-02-14 1
2010-04-11 1
Name: date_account_created, dtype: int64上面图说明train注册的不同天数有1634个不同的天数，test有92个不同的天数，train最小值是1，最大值674
trainng中2014-5-13有674个
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数据集train显示2010-1-1到2014-6-30的数据
数据集test显示2014-6-30到2014-9-30的数据
dac_train_date = pd.to_datetime(dac_train.index)
print('the start date of training dataset is :{}'.format(dac_train_date.min()))
print('the end date of training dataset is :{}'.format(dac_train_date.max()))dac_test_date = pd.to_datetime(dac_test.index)
print('the start date of test dataset is :{}'.format(dac_test_date.min()))
print('the end date of test dataset is :{}'.format(dac_test_date.max()))the start date of training dataset is :2010-01-01 00:00:00
the end date of training dataset is :2014-06-30 00:00:00
the start date of test dataset is :2014-07-01 00:00:00
the end date of test dataset is :2014-09-30 00:00:00-----------------------------------------------------------------------------------
dac_train_day = dac_train_date - dac_train_date.min()
dac_test_day = dac_test_date - dac_test_date.min()
print(dac_train_day)
print(dac_test_day)TimedeltaIndex(['1593 days', '1635 days', '1636 days', '1600 days','1594 days', '1614 days', '1601 days', '1627 days','1622 days', '1641 days',...'18 days', '27 days', '2 days', '0 days','1 days', '67 days', '113 days', '22 days','44 days', '100 days'],dtype='timedelta64[ns]', length=1634, freq=None)
TimedeltaIndex(['22 days', '21 days', '16 days', '23 days', '17 days','20 days', '57 days', '56 days', '28 days', '36 days','29 days', '42 days', '41 days', '58 days', '35 days','27 days', '15 days', '24 days', '77 days', '30 days','44 days', '49 days', '71 days', '37 days', '19 days','38 days', '43 days', '64 days', '34 days', '55 days','85 days', '18 days', '26 days', '84 days', '91 days','31 days', '63 days', '90 days', '51 days', '25 days','69 days', '65 days', '39 days', '48 days', '52 days','7 days', '46 days', '14 days', '50 days', '70 days','83 days', '79 days', '45 days', '60 days', '1 days','76 days', '72 days', '33 days', '13 days', '2 days','6 days', '59 days', '61 days', '66 days', '78 days','53 days', '8 days', '47 days', '9 days', '54 days','80 days', '40 days', '73 days', '0 days', '74 days','62 days', '32 days', '68 days', '88 days', '67 days','82 days', '86 days', '75 days', '81 days', '87 days','10 days', '89 days', '5 days', '11 days', '3 days','4 days', '12 days'],dtype='timedelta64[ns]', freq=None)-----------------------------------------------------------------------------------
plt.scatter(dac_train_day.days,dac_train.values,color = 'r',label= 'train dataset')
plt.scatter(dac_test_day.days,dac_test.values,color = 'b',label = 'test dataset')plt.title('Accounts created vs day')
plt.xlabel('Days')
plt.ylabel('Accounts created')
plt.legend(loc = 'upper left')-----------------------------------------------------------------------------------
tra_train_df = train.timestamp_first_active.astype(str).apply(lambda x:datetime.datetime(int(x[:4]),int(x[4:6]),int(x[6:8]),int(x[8:10]),int(x[10:12]),int(x[12:])))
tra_test_df = test.timestamp_first_active.astype(str).apply(lambda x:datetime.datetime(int(x[:4]),int(x[4:6]),int(x[6:8]),int(x[8:10]),int(x[10:12]),int(x[12:])))20090319043255把这样的时间转换成标准的时间格式2009-03-19 04:32:55
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print(train[train.age<15].age.shape)
print(train[train.age>80].age.shape)
print(test[test.age<15].age.shape)
print(test[test.age>80].age.shape)(57,)
(2771,)
(2,)
(417,)可以看到有train里面有57个小于15岁的，大于80岁的有2771个，test小于15岁的有2个，大于80岁的有417个，属于极值吧
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plt.scatter(train.age.value_counts().index.values,train.age.value_counts().values,color='r',label='training')
plt.scatter(test.age.value_counts().index.values,test.age.value_counts().values,color='b',label='test')plt.title('Counts at different ages')
plt.xlabel('Age')
plt.ylabel('Counts od id')
plt.legend(loc = 'upper right',fontsize = 15)-----------------------------------------------------------------------------------
age_train = [train[train.age.isnull()].age.shape[0],train.query('age<15').age.shape[0],train.query('age>=15 & age<=80').age.shape[0],train.query('age>80').age.shape[0]]age_test = [test[test.age.isnull()].age.shape[0],test.query('age<15').age.shape[0],test.query('age>=15 & age<=80').age.shape[0],test.query('age>80').age.shape[0]]
columns = ['Null','age<15','age','age>80']fig, (ax1,ax2) = plt.subplots(1,2,sharex=True,sharey=True,figsize=(10,5))sns.barplot(columns,age_train,ax=ax1)
sns.barplot(columns,age_test,ax=ax2)ax1.set_title('training dataset')
ax2.set_title('test dataset')
ax1.set_ylabel('counts')我用柱状图把年龄分成几段，空值分一段，小于15岁分成一段，大于80岁分成一段，绿色属于正常值范围，蓝色属于空值，红色属于千年老妖
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ohe_feats = ['gender','signup_method','signup_flow','language','affiliate_channel','affiliate_provider','first_affiliate_tracked','signup_app','first_device_type','first_browser']def feature_barplot(feature, df_train = train, df_test=test, figsize=(10,5),rot=90,saveimg = False):feat_train = df_train[feature].value_counts()feat_test = df_test[feature].value_counts()fig_feature, (axis1,axis2) = plt.subplots(1,2,sharex=True,sharey=True,figsize=figsize)sns.barplot(feat_train.index.values, feat_train.values, ax= axis1)sns.barplot(feat_test.index.values, feat_test.values, ax= axis2)axis1.set_xticklabels(axis1.xaxis.get_majorticklabels(), rotation = rot)axis2.set_xticklabels(axis1.xaxis.get_majorticklabels(), rotation = rot)axis1.set_title(feature + ' of training dataset')axis2.set_title(feature + ' of test dataset')axis1.set_ylabel('Counts')plt.tight_layout()if saveimg ==True:figname = feature + '.png'fig_feature.savefig(figname, dpi = 75)train.gender.value_counts()性别区分
-unknown- 95688 FEMALE 63041 MALE 54440 OTHER 282 Name: gender, dtype: int64可以看train到空值有9万多，男的有5万多，女士6万多
-unknown- 33792 FEMALE 14483 MALE 13769 OTHER 52 Name: gender, dtype: int64
可以看test到空值有3万多，男的有1.3万多，女士1.4万多
feature_barplot('gender',saveimg= True)-----------------------------------------------------------------------------------
feature_barplot('signup_method')注册方式，最多的是basic，其次是facebook，最后是谷歌
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for feat in ohe_feats:feature_barplot(feature=feat)
男女人数分布图
注册方式分布图
从那个页面进来注册的，0是主页注册页面
英语第一，因为Airbnb是美国公司，所以第一吧，中文第二
从什么地方引流过来的
付费推广的渠道
注册最多的是浏览器
台式机mac最多是最多，可能美国台式mac比较多吧，毕竟是发达国家嘛
Airbnb使用时候的浏览器
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通过上面可以发现，年龄的极值需要处理，性别空值需要处理
下面开始查看session数据
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn as sk
import datetime
from datetime import date
import seaborn as sns
from sklearn.preprocessing import *
from sklearn.preprocessing import LabelEncoder #标准化标签，将标签值统一转换成range(标签值个数-1)范围内
from sklearn.cross_validation import StratifiedShuffleSplit#数据集划分df_sessions.head() action action_type action_detail device_type secs_elapsed id
0 lookup NaN NaN Windows Desktop 319.0 d1mm9tcy42
1 search_results click view_search_results Windows Desktop 67753.0 d1mm9tcy42
2 lookup NaN NaN Windows Desktop 301.0 d1mm9tcy42
3 search_results click view_search_results Windows Desktop 22141.0 d1mm9tcy42
4 lookup NaN NaN Windows Desktop 435.0 d1mm9tcy42可以看到id有重复的，说明一个用户有多次操作
df_sessions.shape(10567737, 6)
有多少用户呢
dgr_sess = df_sessions.groupby(['id'])共有135483用户制造出了1000多万的数据
查看session有多少空值
df_sessions.isnull().sum()action 79626 action_type 1126204 action_detail 1126204 device_type 0 secs_elapsed 136031 id 34496 dtype: int64
对空置进行处理，先对空置进行NAN填充，因为有些空值是Nan，none等，所以对空值进行统一填充
print('Working on Session data...')df_sessions.action = df_sessions.action.fillna('NAN')
df_sessions.action_type = df_sessions.action_type.fillna('NAN')
df_sessions.action_detail = df_sessions.action_detail.fillna('NAN')
df_sessions.device_type = df_sessions.device_type.fillna('NAN')-----------------------------------------------------------------------------------
act_freq = 100
act = dict(zip(df_sessions.action.value_counts().index,df_sessions.action.value_counts().values))
df_sessions.action = df_sessions.action.apply(lambda x: 'OTHER' if act[x] < act_freq else x)
f_act = df_sessions.action.value_counts().argsort()
f_act_detail = df_sessions.action_detail.value_counts().argsort() #对action_detail频繁进行排序
f_act_type = df_sessions.action_type.value_counts().argsort() #对action_type频繁进行排序
f_dev_type = df_sessions.device_type.value_counts().argsort()
print(f_act.shape)
print(f_act_detail.shape)
print(f_act_type.shape)
print(f_dev_type.shape)因为有不频繁的值，所以把小于100的进行OTHER归类，归类特别细致的话会对模型没有朴实性
df_sessions.action.value_counts()-----------------------------------------------------------------------------------
samples = []
cont = 0
In = len(dgr_sess)for g in dgr_sess:if cont%1000 ==0:print('%s from %s'%(cont,In)) #提示打印多少行数据了gr = g[1]l = []l.append(g[0])l.append(len(gr))sev = gr.secs_elapsed.fillna(0).valuesc_act = [0] * len(f_act)for i,v in enumerate(gr.action.values):c_act[f_act[v]] += 1_, c_act_uqc = np.unique(gr.action.values,return_counts=True)c_act += [len(c_act_uqc), np.mean(c_act_uqc), np.std(c_act_uqc)]l = l + c_actc_act_detail = [0] * len(f_act_detail)for i,v in enumerate(gr.action_detail.values):c_act_detail[f_act_detail[v]] += 1_, c_act_det_uqc = np.unique(gr.action_detail.values,return_counts=True)c_act_detail += [len(c_act_det_uqc), np.mean(c_act_det_uqc), np.std(c_act_det_uqc)]l = l + c_act_detaill_act_type = [0] * len(f_act_type)c_act_type = [0] * len(f_act_type)for i,v in enumerate(gr.action_type.values):l_act_type[f_act_type[v]] += sev[i]c_act_type[f_act_type[v]] += 1l_act_type = np.log(1 + np.array(l_act_type)).tolist()_, c_act_type_uqc = np.unique(gr.action_type.values,return_counts=True)c_act_type += [len(c_act_type_uqc), np.mean(c_act_type_uqc), np.std(c_act_type_uqc)]l = l + c_act_type + l_act_typec_dev_type = [0] * len(f_dev_type)for i,v in enumerate(gr.device_type.values):c_dev_type[f_dev_type[v]] += 1c_dev_type.append(len(np.unique(gr.device_type.values)))_, c_dev_type_uqc = np.unique(gr.device_type.values,return_counts=True)c_dev_type += [len(c_dev_type_uqc), np.mean(c_dev_type_uqc),np.std(c_dev_type_uqc)]l = l + c_dev_typel_secs = [0] * 5l_log = [0] * 15if len(sev) > 0:l_secs[0] = np.log(1 + np.sum(sev))l_secs[1] = np.log(1 + np.mean(sev))l_secs[2] = np.log(1 + np.std(sev))l_secs[3] = np.log(1 + np.median(sev))l_secs[4] = l_secs[0] / float(l[1])log_sev = np.log(1+sev).astype(int)l_log = np.bincount(log_sev,minlength=15).tolist()l = l + l_secs + l_logsamples.append(l)cont += 1 共有135483用户承载出了1000多万的数据，所以遍历数据，以人为单位，每个人有多少个action，f_act列出所有的action，c_act求出每个人有多少个action，把这个人action这一组里面所有不同的action列出来
samples = np.array(samples)
samp_ar = samples[:,1:].astype(np.float16)
samp_id = samples[:,0]col_names = []
for i in range(len(samples[0])-1):col_names.append('c_' + str(i))df_agg_sess = pd.DataFrame(samp_ar, columns=col_names)
df_agg_sess['id'] = samp_id
df_agg_sess.index = df_agg_sess.iddf_agg_sess.shape(135483, 335)
现在有13万数据，335列
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导入train和test数据集
train = pd.read_csv('/Users/qinpeng/Documents/airbnb/train_users_2.csv')
test = pd.read_csv('/Users/qinpeng/Documents/airbnb/test_users.csv')train_row = train.shape[0]labels = train['country_destination'].valuesid_test = test['id']train.drop(['country_destination','date_first_booking'], axis = 1,inplace=True)
test.drop(['date_first_booking'], axis=1,inplace=True)删除掉3行数据
df = pd.concat([train,test],axis = 0,ignore_index=True)
df.shape把train和test两个数据进行拼接
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tfa = df.timestamp_first_active.astype(str).apply(lambda x:datetime.datetime(int(x[:4]),int(x[4:6]),int(x[6:8]),int(x[8:10]),int(x[10:12]),int(x[12:])))df['tfa_year'] = np.array([x.year for x in tfa])
df['tfa_month'] = np.array([x.month for x in tfa])
df['tfa_day'] = np.array([x.day for x in tfa])
df['tfa_wd'] = np.array([x.isoweekday() for x in tfa])#isoweekday 分成周几
df_tfa_wd = pd.get_dummies(df.tfa_wd,prefix='tfa_wd') # get_dummies is 'one hot encoding'
df = pd.concat((df,df_tfa_wd),axis = 1)
df.drop(['tfa_wd'],axis = 1,inplace=True)
df.head()处理时间，把时间detetime标准化，提取出年月日，根据年月日分出是周几，进行one hot encoding编码
Y = 2000 #北半球，确定月份的四季
seasons = [(0, (date(Y, 1, 1), date(Y, 3, 20))), #winter(1, (date(Y, 3, 21), date(Y, 6, 20))), #spring(2, (date(Y, 6, 21), date(Y, 9, 22))), #summer(3, (date(Y, 9, 23), date(Y, 12, 20))), #autumn(0, (date(Y, 12, 21), date(Y, 12, 31))),#winter]把一年四季的四个季节进行划分
df['tfa_season'] = np.array([get_season(x) for x in tfa])
df_tfa_season = pd.get_dummies(df.tfa_season, prefix = 'tfa_season')
df = pd.concat((df,df_tfa_season),axis = 1)
df.drop(['tfa_season'],axis = 1,inplace=True)
df.head()def get_season(dt):dt = dt.date()dt = dt.replace(year=Y)return next(season for season, (start, end) in seasons if start <= dt <=end)写一个划分一年四季的函数，对这个四个季节进行one hot encoding编码
dac = pd.to_datetime(df.date_account_created)df['dac_year'] = np.array([x.year for x in dac])
df['dac_month'] = np.array([x.month for x in dac])
df['dac_day'] = np.array([x.day for x in dac])df['dac_wd'] = np.array([x.isoweekday() for x in dac])
df_dac_wd = pd.get_dummies(df.dac_wd, prefix='dac_wd')
df = pd.concat((df,df_dac_wd),axis = 1)
df.drop(['dac_wd'],axis = 1,inplace=True)把df时间进行拆分，分析出是周几，再进行one hot encoding编码
df['dac_season'] = np.array([get_season(x) for x in dac])
df_dac_season = pd.get_dummies(df.dac_season, prefix='dac_season')
df = pd.concat((df,df_dac_season),axis = 1)
df.drop(['dac_season'],axis = 1,inplace=True)
df.head()-----------------------------------------------------------------------------------
dt_span = dac.subtract(tfa).dt.days
plt.scatter(dt_span.value_counts().index.values,dt_span.value_counts().values)可以看出第一次注册时间和第一次激活时间的时间差
写一个函数，把小于-1天的为一天，小于30天大于-1天的为一个月，大于30天小于365天的为一年
为什么是-1可能是服务器更新把第一次注册时间放到下一天了，所以看到-1最频繁
对及其不频繁的进行归类
def get_span(dt):if dt == -1:return 'OneDay'elif (dt<30) & (dt>-1):return 'OneMonth'elif (dt>=30) & (dt<=365):return 'OneYear'else:return 'Other'df['dt_span'] = np.array([get_span(x) for x in dt_span])
df_dt_span = pd.get_dummies(df.dt_span,prefix='dt_span')
df = pd.concat((df,df_dt_span),axis = 1)
df.drop(['dt_span'],axis = 1,inplace=True)
df.head()-----------------------------------------------------------------------------------
df.drop(['date_account_created','timestamp_first_active'],axis = 1, inplace=True)删除两个时间列，分别是注册时间和激活时间
下面处理年龄
av = df.age.values
av = np.where(np.logical_and(av<2000,av>1900),2018-av,av)
df['age'] = avage = df.age
age.fillna(-1,inplace=True)
div = 15
def get_age(age):if age < 0:return 'NA'elif (age < div):return divelif (age <= div*2):return div*2elif (age <= div*3):return div*3elif (age <= div*4):return div*4elif (age <=div*5):return div*5elif (age <=110):return div*6else:return 'Unphysical'对年龄段进行划分，我推理把小于2000大于1900岁的用户，我用2018（今年）减去用户的岁数，得到他现在的年龄，把一部分极值信息变成有用的信息
df['age'] = np.array([get_age(x) for x in age])
df_age = pd.get_dummies(df.age,prefix='age')
df_age.head()对年龄段进行one hot encoding编码
df = pd.concat((df,df_age),axis = 1)
df.drop(['age'],axis = 1,inplace=True)
df.head()把age列删除
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处理其它列
feat_toOHE = ['gender','signup_method','signup_flow','language','affiliate_channel','affiliate_provider','first_affiliate_tracked','signup_app','first_device_type','first_browser']for f in feat_toOHE:df_ohe = pd.get_dummies(df[f],prefix=f,dummy_na=True)df.drop([f],axis = 1,inplace=True)df = pd.concat((df,df_ohe),axis = 1)把其它列进行one hot encoding编码
df.shape(275547, 208)
现在有208列的特征
df_all = pd.merge(df,df_agg_sess,how='left')
df_all = df_all.drop(['id'],axis = 1)
af_all = df_all.fillna(-2)df_all['all_null'] = np.array([sum(r<0) for r in df_all.values])把df和df_agg_sess表进行合并
删除id列，对空值用-2填充
添加一个all_null列，小于-2的都是为空值，计算空置的数量
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Xtrain = df_all.iloc[:train_row,:]
Xtest = df_all.iloc[train_row:,:]le = LabelEncoder()
le.fit(labels)
ytrain = le.transform(labels)print(train.shape[0] == Xtrain.shape[0])
print(test.shape[0] == Xtest.shape[0])Xtrain.to_csv('/Users/qinpeng/Documents/airbnb/Airbnb_Xtrain_v2.csv')
Xtest.to_csv('/Users/qinpeng/Documents/airbnb/Airbnb_Xtest_v2.csv')
labels.tofile('/Users/qinpeng/Documents/airbnb/Airbnb_Ytrain_v2.csv',sep='\n',format='%s')这篇关于分析Airbnb新用户订房地点的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
