[人工智能] 【无标题】22222

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[人工智能]【无标题】22222

%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as st
import os
import re
import gc
import warnings


warnings.filterwarnings('ignore')

plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号

pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', 100)

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

path = 'traina/'  
path1 = 'trainb/'

入住信息

cust_info = pd.read_csv(path + '网约房平台入住人表.csv')
cust_info1 = pd.read_csv(path1 + '网约房平台入住人表.csv')
cust = pd.concat([cust_info, cust_info1])
cust.head(2)

	ORDER_PRIMARY_ID	GUEST_ID	BDATE	XZQH	IN_TIME	OUT_TIME
0	07C5BF73B18B44B0877DEED007F8771D	NaN	19800627	222405	NaN	NaN
1	3525D57CAE104A078E4962B2B89377B0	371099C301202107090001	19951025	370523	2.021071e+11	2.021071e+11

gc.collect()

cust['BDATE'] = cust['BDATE'].astype('str')
cust['XZQH'] = cust['XZQH'].astype('str')
cust['IN_TIME'] = cust['IN_TIME'].fillna(0).astype('str').apply(lambda x: x[:12])
cust['OUT_TIME'] = cust['OUT_TIME'].fillna(0).astype('str').apply(lambda x: x[:12])

cust.head(2)

	ORDER_PRIMARY_ID	GUEST_ID	BDATE	XZQH	IN_TIME	OUT_TIME
0	07C5BF73B18B44B0877DEED007F8771D	NaN	19800627	222405	0.0	0.0
1	3525D57CAE104A078E4962B2B89377B0	371099C301202107090001	19951025	370523	202107092103	202107111158

cust.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 44030 entries, 0 to 3343
Data columns (total 6 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   ORDER_PRIMARY_ID  44030 non-null  object
 1   GUEST_ID          5158 non-null   object
 2   BDATE             44030 non-null  object
 3   XZQH              44030 non-null  object
 4   IN_TIME           44030 non-null  object
 5   OUT_TIME          44030 non-null  object
dtypes: object(6)
memory usage: 2.4+ MB

cust.nunique()

ORDER_PRIMARY_ID    43883
GUEST_ID             3698
BDATE                9503
XZQH                 2659
IN_TIME              3396
OUT_TIME              236
dtype: int64

cust['ORDER_PRIMARY_ID'].value_counts()[5:10]

03158D5CA62E42339D697612EA347FB3    2
0A99D74F1E0248D68A729CB79FC640E6    2
1FE3E448558347D89C56B0AFCEC8ACFB    2
864E417B7BDE4C7B9449B7E087E9F21E    2
3DEFA75566934CA5929D0C47F0B95FDE    2
Name: ORDER_PRIMARY_ID, dtype: int64

cust.sort_values(['ORDER_PRIMARY_ID', 'GUEST_ID'], inplace=True)

stat = cust.groupby(['ORDER_PRIMARY_ID'])['BDATE'].count().reset_index()
stat.columns = ['ORDER_PRIMARY_ID', 'guest_sum']
stat['guest_sum_notnull'] = cust.groupby(['ORDER_PRIMARY_ID'])['GUEST_ID'].agg('count').values
cust = cust.merge(stat, on=['ORDER_PRIMARY_ID'], how='left')

cust.drop_duplicates('ORDER_PRIMARY_ID', keep='first', inplace=True)

cust[cust['ORDER_PRIMARY_ID']=='70798782D6C04A438360D80AFE4845C1']

	ORDER_PRIMARY_ID	GUEST_ID	BDATE	XZQH	IN_TIME	OUT_TIME	guest_sum	guest_sum_notnull
22801	70798782D6C04A438360D80AFE4845C1	371099B389202107040001	19990314	130983	0.0	0.0	3	2

cust['IN_TIME'].max(), cust['IN_TIME'].min()

('202110041413', '0.0')

订单信息

# platform infomation
order_info = pd.read_csv(path + '网约平台旅客订单信息.csv') 
order1 = pd.read_csv(path1 + '网约平台旅客订单信息.csv')
order = pd.concat([order_info, order1])
order.head(2)

	ORDER_ID	ORDER_PRIMARY_ID	HOTELID	PRE_IN_TIME	PRE_OUT_TIME	ORDER_TIME	STATUS	CANCEL_TIME	INSERT_TIME	MODIFY_TIME	FIRM
0	923521	96BBDB7CC049421C85826AE07020B139	278337	202008011200	202008021200	1.596120e+11	1	NaN	20200730224152	20200730224152	3
1	923696	C72F20539AD1447D86CD1A8E5EAEC63A	282932	202008041400	202008061200	1.596121e+11	1	NaN	20200730225524	20200730225524	3

order[order['ORDER_PRIMARY_ID']=='3CDEDB5E03534D379687645675898CA4']

	ORDER_ID	ORDER_PRIMARY_ID	HOTELID	PRE_IN_TIME	PRE_OUT_TIME	ORDER_TIME	STATUS	CANCEL_TIME	INSERT_TIME	MODIFY_TIME	FIRM
30970	4422027	3CDEDB5E03534D379687645675898CA4	170898	202108081358	202108101158	2.021071e+11	3	NaN	20210712210153	20210712210153	3

order['PRE_IN_TIME'] = order['PRE_IN_TIME'].astype('str')
order['PRE_OUT_TIME'] = order['PRE_OUT_TIME'].astype('str')
# order['ORDER_TIME'] = order['ORDER_TIME'].astype('str')
order['INSERT_TIME'] = order['INSERT_TIME'].astype('str')
order['MODIFY_TIME'] = order['MODIFY_TIME'].astype('str')
order['CANCEL_TIME'] = order['CANCEL_TIME'].astype('str')

order['ORDER_TIME'] = order['ORDER_TIME'].fillna(0).astype('str').apply(lambda x: x[:12])

##########################没有考虑包含取消时间的订单

order = order[(order['CANCEL_TIME']=='nan')]

order = order.sort_values(['ORDER_ID', 'MODIFY_TIME'])

order.drop_duplicates('ORDER_ID', keep='last', inplace=True)

order.shape

(29941, 11)

order[order['ORDER_ID']==4402846]

	ORDER_ID	ORDER_PRIMARY_ID	HOTELID	PRE_IN_TIME	PRE_OUT_TIME	ORDER_TIME	STATUS	CANCEL_TIME	INSERT_TIME	MODIFY_TIME	FIRM
30141	4402846	BC0E8E3602434EA6A5F29A6F6FF42233	100177	202107111358	202107121158	202107111032	1	nan	20210711140042	20210711140042	3

order.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 29941 entries, 2 to 18590
Data columns (total 11 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   ORDER_ID          29941 non-null  int64 
 1   ORDER_PRIMARY_ID  29941 non-null  object
 2   HOTELID           29941 non-null  object
 3   PRE_IN_TIME       29941 non-null  object
 4   PRE_OUT_TIME      29941 non-null  object
 5   ORDER_TIME        29941 non-null  object
 6   STATUS            29941 non-null  int64 
 7   CANCEL_TIME       29941 non-null  object
 8   INSERT_TIME       29941 non-null  object
 9   MODIFY_TIME       29941 non-null  object
 10  FIRM              29941 non-null  int64 
dtypes: int64(3), object(8)
memory usage: 2.7+ MB

order.nunique()

ORDER_ID            29941
ORDER_PRIMARY_ID    29941
HOTELID              4801
PRE_IN_TIME          6875
PRE_OUT_TIME          729
ORDER_TIME          26818
STATUS                  3
CANCEL_TIME             1
INSERT_TIME         25432
MODIFY_TIME         25432
FIRM                    2
dtype: int64

order_info['FIRM'].value_counts()

3     32029
10     8521
Name: FIRM, dtype: int64

order['STATUS'].value_counts()

1    26137
2     3221
3      583
Name: STATUS, dtype: int64

#插入时间不等于修改时间，就是进行过修改过的订单,最后发现为空，等于没有修改过
order[order['INSERT_TIME']!=order['MODIFY_TIME']]

	ORDER_ID	ORDER_PRIMARY_ID	HOTELID	PRE_IN_TIME	PRE_OUT_TIME	ORDER_TIME	STATUS	CANCEL_TIME	INSERT_TIME	MODIFY_TIME	FIRM

合并数据order和cust

# order时间包含了cust时间
df = pd.merge(order, cust, on='ORDER_PRIMARY_ID')

月份分为1、3、5、7、8、10、12：31天；2：28天；4、6、9、11：30天

df['IN_TIME'] = df['IN_TIME'].apply(lambda x: np.nan if x=='0.0' else x)
df['OUT_TIME'] = df['OUT_TIME'].apply(lambda x: np.nan if x=='0.0' else x)
df['IN_TIME'].fillna(df['PRE_IN_TIME'], inplace=True)
df['OUT_TIME'].fillna(df['PRE_OUT_TIME'], inplace=True)

df['OUT_TIME'] = df.apply(lambda x: x['OUT_TIME'] if x['OUT_TIME'] >= x['PRE_OUT_TIME'] else x['PRE_OUT_TIME'], axis=1)
df['IN_TIME'] = df.apply(lambda x: x['IN_TIME'] if x['IN_TIME'] >= x['PRE_IN_TIME'] else x['PRE_IN_TIME'], axis=1)

df.drop(['PRE_IN_TIME', 'PRE_OUT_TIME', 'CANCEL_TIME'], axis=1, inplace=True)

df.head(2)

	ORDER_ID	ORDER_PRIMARY_ID	HOTELID	ORDER_TIME	STATUS	INSERT_TIME	MODIFY_TIME	FIRM	GUEST_ID	BDATE	XZQH	IN_TIME	OUT_TIME	guest_sum	guest_sum_notnull
0	706648	A4AACE06B518418C8A8CA1935DDD1C5A	227185	202007092241	1	20200714171021	20200714171021	3	NaN	20010409	371002	202007151300	202007161200	1	0
1	748647	67D551AACFD049AE9CC2AB65E9870678	9483	202007132109	1	20201002101040	20201002101040	3	NaN	19881023	341202	202010011400	202010021011	1	0

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 29941 entries, 0 to 29940
Data columns (total 15 columns):
 #   Column             Non-Null Count  Dtype 
---  ------             --------------  ----- 
 0   ORDER_ID           29941 non-null  int64 
 1   ORDER_PRIMARY_ID   29941 non-null  object
 2   HOTELID            29941 non-null  object
 3   ORDER_TIME         29941 non-null  object
 4   STATUS             29941 non-null  int64 
 5   INSERT_TIME        29941 non-null  object
 6   MODIFY_TIME        29941 non-null  object
 7   FIRM               29941 non-null  int64 
 8   GUEST_ID           3561 non-null   object
 9   BDATE              29941 non-null  object
 10  XZQH               29941 non-null  object
 11  IN_TIME            29941 non-null  object
 12  OUT_TIME           29941 non-null  object
 13  guest_sum          29941 non-null  int64 
 14  guest_sum_notnull  29941 non-null  int64 
dtypes: int64(5), object(10)
memory usage: 3.7+ MB

df.nunique();

df['in_time'] = pd.to_datetime(df['IN_TIME'], errors='coerce', format='%Y%m%d%H%M')
df['out_time'] = pd.to_datetime(df['OUT_TIME'], errors='coerce', format='%Y%m%d%H%M')

df = df[~df['out_time'].isnull()]

df.head(2)

	ORDER_ID	ORDER_PRIMARY_ID	HOTELID	ORDER_TIME	STATUS	INSERT_TIME	MODIFY_TIME	FIRM	GUEST_ID	BDATE	XZQH	IN_TIME	OUT_TIME	guest_sum	guest_sum_notnull	in_time	out_time
0	706648	A4AACE06B518418C8A8CA1935DDD1C5A	227185	202007092241	1	20200714171021	20200714171021	3	NaN	20010409	371002	202007151300	202007161200	1	0	2020-07-15 13:00:00	2020-07-16 12:00:00
1	748647	67D551AACFD049AE9CC2AB65E9870678	9483	202007132109	1	20201002101040	20201002101040	3	NaN	19881023	341202	202010011400	202010021011	1	0	2020-10-01 14:00:00	2020-10-02 10:11:00

# df['in_time_year'] = df['in_time'].dt.year.fillna(0).astype('int')                 #入住时间的年份
# df['out_time_year'] = df['out_time'].dt.year.fillna(0).astype('int')               #退房时间的年份
# df['in_time_month'] = df['in_time'].dt.month.fillna(0).astype('int')               #入住时间的月份
# df['out_time_month'] = df['out_time'].dt.month.fillna(0).astype('int')             #退房时间的月份
# df['in_time_day'] = df['in_time'].dt.day.fillna(0).astype('int')                   #入住时间的日期
# df['out_time_day'] = df['out_time'].dt.day.fillna(0).astype('int')                 #退房时间的日期
# df['in_time_hour'] = df['in_time'].dt.hour.fillna(0).astype('int')                 #入住时间的小时
# df['out_time_hour'] = df['out_time'].dt.hour.fillna(0).astype('int')               #退房时间的小时

#2月份没有
# df['day_gap'] = df['out_time_day'] - df['in_time_day'] + (df['out_time_month'] - df['in_time_month']).apply(lambda x: x*30 if x>0 else 0)

# df['day_gap'] = df.apply(lambda x: x['day_gap']+31 if x['in_time_month']==12 and x['out_time_month']==1 else x['day_gap'], axis=1)

# month_ = [1, 3, 5, 7, 8, 10]
# df['day_gap'] = df.apply(lambda x: x['day_gap']+1 if x['in_time_month'] in month_ and x['out_time_month']>x['in_time_month'] else x['day_gap'], axis=1)

# df['day_gap'].sum()

#后移一天
df[‘lock_time’][0] + pd.Timedelta(days=1)
#前移一天
df[‘lock_time’][0] + pd.Timedelta(days=-1)
#df[‘in_time’] - timedelta(days=1) #将day_gap列为0的数据全部往后推迟一天；；；；；；；还有钟点房

# df_0 = df[(df['day_gap']==0)]
# df_0['date'] = df_0['IN_TIME']
# 这里边包含了钟点房和当天入住并退房的。

dfs = []
for idx, group in df.groupby(['ORDER_PRIMARY_ID']):
    stat1 = pd.DataFrame()
    stat1['datetime'] = pd.date_range(start=group['IN_TIME'].values[0], end=group['OUT_TIME'].values[0], freq='D', normalize=False, closed=None)
    stat1['ORDER_PRIMARY_ID'] = group['ORDER_PRIMARY_ID'].values[0]
    dfs.append(stat1)

df_date = pd.concat(dfs).reset_index(drop=True)
df1 = df.merge(df_date, on=['ORDER_PRIMARY_ID'], how='left')

df1.shape

(60973, 18)

# df1[(df1['out_time']!=df1['datetime']) & (df1['day_gap']!=0)].shape

# df1[df1['day_gap']==0][:2]

df1['in_date'] = df1['in_time'].dt.date
df1['out_date'] = df1['out_time'].dt.date
df1['date'] = df1['datetime'].dt.date

df2 = df1[(df1['out_date']!=df1['date']) | ((df1['out_date']==df1['date']) & (df1['in_date']==df1['out_date']))]

df2.shape

(49547, 21)

# len(df1[(df1['out_date']!=df1['date'])]), len(df1[df1['day_gap']==0]), len(df1[(df1['out_date']==df1['date']) & (df1['day_gap']==0)])

# pd.date_range(start=group['IN_TIME'].values[0], end=group['OUT_TIME'].values[0], freq='D', normalize=False, closed=None)

df2.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 49547 entries, 0 to 60971
Data columns (total 21 columns):
 #   Column             Non-Null Count  Dtype         
---  ------             --------------  -----         
 0   ORDER_ID           49547 non-null  int64         
 1   ORDER_PRIMARY_ID   49547 non-null  object        
 2   HOTELID            49547 non-null  object        
 3   ORDER_TIME         49547 non-null  object        
 4   STATUS             49547 non-null  int64         
 5   INSERT_TIME        49547 non-null  object        
 6   MODIFY_TIME        49547 non-null  object        
 7   FIRM               49547 non-null  int64         
 8   GUEST_ID           5963 non-null   object        
 9   BDATE              49547 non-null  object        
 10  XZQH               49547 non-null  object        
 11  IN_TIME            49547 non-null  object        
 12  OUT_TIME           49547 non-null  object        
 13  guest_sum          49547 non-null  int64         
 14  guest_sum_notnull  49547 non-null  int64         
 15  in_time            49547 non-null  datetime64[ns]
 16  out_time           49547 non-null  datetime64[ns]
 17  datetime           49547 non-null  datetime64[ns]
 18  in_date            49547 non-null  object        
 19  out_date           49547 non-null  object        
 20  date               49547 non-null  object        
dtypes: datetime64[ns](3), int64(5), object(13)
memory usage: 8.3+ MB

room_info = pd.read_csv(path + '网约房注册民宿.csv')
room_info.head(1)

	CODE	HOTELID	JYMJ	ROOM_NUM	BED_NUM	FWLY	CZLY	CALLED	CITY_CODE	BUR_CODE	STA_CODE	SSX	ADDRESS	MPHM	JYQK	FIRM	DJSJ	BGSJ	STATUS	AUDITSTATUS
0	100177	3710830002	0.0	1	1	2	1	【山海边公寓】海景大床房设施齐全空调做饭宽带应有尽	371000000000	NaN	NaN	371083	乳山市长江路银泰海景花园 55-301	ROOM001	【山海边公寓】海景大床房设施齐全空调做饭宽带应有尽	3	2020-05-18 10:33:55	2020-07-15 10:23:58	NaN	NaN

room_info['STATUS'].value_counts()

1.0    2658
Name: STATUS, dtype: int64

room_info.drop(['HOTELID', 'FWLY', 'CITY_CODE', 'STATUS','AUDITSTATUS', 'JYQK'], axis=1, inplace=True)

room_info.rename(columns={'CODE':'HOTELID'}, inplace= True)

room_info['FIRM'].value_counts()

3     3429
10    1878
Name: FIRM, dtype: int64

room_info.nunique()

HOTELID     5307
JYMJ         173
ROOM_NUM      11
BED_NUM       17
CZLY           4
CALLED      4232
BUR_CODE       7
STA_CODE      54
SSX            5
ADDRESS     4850
MPHM         584
FIRM           2
DJSJ        3753
BGSJ        3294
dtype: int64

登记时间和变更时间无变化的只有两家

#经营面积为0的修改为空值
room_info['JYMJ'] = room_info['JYMJ'].apply(lambda x: np.nan if x==0 else x)

#登记时间和变更时间的差值
room_use_col = ['HOTELID', 'JYMJ', 'ROOM_NUM', 'BED_NUM', 'FIRM', 'STATUS']
freq_col = ['CALLED', 'ADDRESS', 'JYMJ', 'ROOM_NUM', 'BED_NUM', 'CZLY', 'BUR_CODE', 'STA_CODE', 'SSX', 'MPHM', 'FIRM']
for col in freq_col:
    st = room_info[col].value_counts().reset_index()
    st.columns = [col, col+'_freq']
    room_info = room_info.merge(st, on=col, how='left')

#房间面积率和床位率\单间房床位量
room_info['room_ratio'] = room_info['JYMJ'] / room_info['ROOM_NUM']
room_info['bed_ratio'] = room_info['JYMJ'] / room_info['BED_NUM']
room_info['room_bed'] = room_info['BED_NUM'] / room_info['ROOM_NUM']

room_info['DJSJ'] = pd.to_datetime(room_info['DJSJ'], format='%Y-%m-%d %H:%M:%S', errors='coerce')
room_info['BGSJ'] = pd.to_datetime(room_info['BGSJ'], format='%Y-%m-%d %H:%M:%S', errors='coerce')

room_info['DJ_date'] = room_info['DJSJ'].dt.date
room_info['BG_date'] = room_info['BGSJ'].dt.date

#等级时间和变更时间的时间差
room_info['DJ_gap'] = (room_info['BG_date'] - room_info['DJ_date']).dt.days

room_info['DJSJ'].min(), room_info['DJSJ'].max(), room_info['BGSJ'].min(), room_info['BGSJ'].max()

(Timestamp('2020-05-12 13:16:24'),
 Timestamp('2021-10-19 16:05:41'),
 Timestamp('2020-07-15 10:23:06'),
 Timestamp('2021-10-19 16:09:15'))

# room['BG_dayofweek'].value_counts()

plt.figure(figsize=(20, 8))
sns.countplot(room_info['JYMJ'])

<AxesSubplot:xlabel='JYMJ', ylabel='count'>

?
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?

经营面积缺失超过一半，3000多，考虑删除

合并room到df中

df3 = df2.merge(room_info, on=['HOTELID', 'FIRM'], how='left')

df3 = df3.sort_values(by=['HOTELID', 'date'])

#增加民宿单日的总订单量
in_num = df3.groupby(['HOTELID', 'date'])['ORDER_ID'].count().reset_index()
in_num.columns = ['HOTELID', 'date', 'in_hotel_num']
df4 = df3.merge(in_num, on=['HOTELID', 'date'], how='left')

df4.head(2)

	ORDER_ID	ORDER_PRIMARY_ID	HOTELID	ORDER_TIME	STATUS	INSERT_TIME	MODIFY_TIME	FIRM	GUEST_ID	BDATE	XZQH	IN_TIME	OUT_TIME	guest_sum	guest_sum_notnull	in_time	out_time	datetime	in_date	out_date	date	JYMJ	ROOM_NUM	BED_NUM	CZLY	CALLED	BUR_CODE	STA_CODE	SSX	ADDRESS	MPHM	DJSJ	BGSJ	CALLED_freq	ADDRESS_freq	JYMJ_freq	ROOM_NUM_freq	BED_NUM_freq	CZLY_freq	BUR_CODE_freq	STA_CODE_freq	SSX_freq	MPHM_freq	FIRM_freq	room_ratio	bed_ratio	room_bed	DJ_date	BG_date	DJ_gap	in_hotel_num
0	4402846	BC0E8E3602434EA6A5F29A6F6FF42233	100177	202107111032	1	20210711140042	20210711140042	3	NaN	19731004	370602	202107111358	202107121158	1	0	2021-07-11 13:58:00	2021-07-12 11:58:00	2021-07-11 13:58:00	2021-07-11	2021-07-12	2021-07-11	NaN	1	1	1	【山海边公寓】海景大床房设施齐全空调做饭宽带应有尽	NaN	NaN	371083	乳山市长江路银泰海景花园 55-301	ROOM001	2020-05-18 10:33:55	2020-07-15 10:23:58	1	1	NaN	3091	1502	4727	NaN	NaN	150	2565	3429	NaN	NaN	1.0	2020-05-18	2020-07-15	58.0	1
1	4455098	01D7394D02B44376913536F6071151AD	100177	202107151446	1	20210724140006	20210724140006	3	NaN	19941001	429006	202107241358	202107261158	1	0	2021-07-24 13:58:00	2021-07-26 11:58:00	2021-07-24 13:58:00	2021-07-24	2021-07-26	2021-07-24	NaN	1	1	1	【山海边公寓】海景大床房设施齐全空调做饭宽带应有尽	NaN	NaN	371083	乳山市长江路银泰海景花园 55-301	ROOM001	2020-05-18 10:33:55	2020-07-15 10:23:58	1	1	NaN	3091	1502	4727	NaN	NaN	150	2565	3429	NaN	NaN	1.0	2020-05-18	2020-07-15	58.0	1

df4.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 49547 entries, 0 to 49546
Data columns (total 51 columns):
 #   Column             Non-Null Count  Dtype         
---  ------             --------------  -----         
 0   ORDER_ID           49547 non-null  int64         
 1   ORDER_PRIMARY_ID   49547 non-null  object        
 2   HOTELID            49547 non-null  object        
 3   ORDER_TIME         49547 non-null  object        
 4   STATUS             49547 non-null  int64         
 5   INSERT_TIME        49547 non-null  object        
 6   MODIFY_TIME        49547 non-null  object        
 7   FIRM               49547 non-null  int64         
 8   GUEST_ID           5963 non-null   object        
 9   BDATE              49547 non-null  object        
 10  XZQH               49547 non-null  object        
 11  IN_TIME            49547 non-null  object        
 12  OUT_TIME           49547 non-null  object        
 13  guest_sum          49547 non-null  int64         
 14  guest_sum_notnull  49547 non-null  int64         
 15  in_time            49547 non-null  datetime64[ns]
 16  out_time           49547 non-null  datetime64[ns]
 17  datetime           49547 non-null  datetime64[ns]
 18  in_date            49547 non-null  object        
 19  out_date           49547 non-null  object        
 20  date               49547 non-null  object        
 21  JYMJ               14248 non-null  float64       
 22  ROOM_NUM           49547 non-null  int64         
 23  BED_NUM            49547 non-null  int64         
 24  CZLY               49547 non-null  int64         
 25  CALLED             49547 non-null  object        
 26  BUR_CODE           22916 non-null  float64       
 27  STA_CODE           22916 non-null  object        
 28  SSX                49547 non-null  int64         
 29  ADDRESS            49547 non-null  object        
 30  MPHM               49547 non-null  object        
 31  DJSJ               49497 non-null  datetime64[ns]
 32  BGSJ               49547 non-null  datetime64[ns]
 33  CALLED_freq        49547 non-null  int64         
 34  ADDRESS_freq       49547 non-null  int64         
 35  JYMJ_freq          14248 non-null  float64       
 36  ROOM_NUM_freq      49547 non-null  int64         
 37  BED_NUM_freq       49547 non-null  int64         
 38  CZLY_freq          49547 non-null  int64         
 39  BUR_CODE_freq      22916 non-null  float64       
 40  STA_CODE_freq      22916 non-null  float64       
 41  SSX_freq           49547 non-null  int64         
 42  MPHM_freq          49547 non-null  int64         
 43  FIRM_freq          49547 non-null  int64         
 44  room_ratio         14248 non-null  float64       
 45  bed_ratio          14248 non-null  float64       
 46  room_bed           49547 non-null  float64       
 47  DJ_date            49497 non-null  object        
 48  BG_date            49547 non-null  object        
 49  DJ_gap             49497 non-null  float64       
 50  in_hotel_num       49547 non-null  int64         
dtypes: datetime64[ns](5), float64(9), int64(18), object(19)
memory usage: 19.7+ MB

df4['date'].min(), df4['date'].max()

(datetime.date(2020, 7, 15), datetime.date(2021, 11, 2))

df4['in_year'] = df4['datetime'].dt.year.fillna(0).astype('int')
df4['in_month'] = df4['datetime'].dt.month.fillna(0).astype('int')
df4['in_day'] = df4['datetime'].dt.day.fillna(0).astype('int')
df4['in_quarter'] = df4['datetime'].dt.quarter.fillna(0).astype('int')
df4['in_dayofweek'] = df4['datetime'].dt.dayofweek.fillna(0).astype('int')
df4['in_dayofyear'] = df4['datetime'].dt.dayofyear.fillna(0).astype('int')
df4['in_is_wknd'] = df4['datetime'].dt.dayofweek // 5                 #是否周末

df4[:2]

	ORDER_ID	ORDER_PRIMARY_ID	HOTELID	ORDER_TIME	STATUS	INSERT_TIME	MODIFY_TIME	FIRM	GUEST_ID	BDATE	XZQH	IN_TIME	OUT_TIME	guest_sum	guest_sum_notnull	in_time	out_time	datetime	in_date	out_date	date	JYMJ	ROOM_NUM	BED_NUM	CZLY	CALLED	BUR_CODE	STA_CODE	SSX	ADDRESS	MPHM	DJSJ	BGSJ	CALLED_freq	ADDRESS_freq	JYMJ_freq	ROOM_NUM_freq	BED_NUM_freq	CZLY_freq	BUR_CODE_freq	STA_CODE_freq	SSX_freq	MPHM_freq	FIRM_freq	room_ratio	bed_ratio	room_bed	DJ_date	BG_date	DJ_gap	in_hotel_num	in_year	in_month	in_day	in_quarter	in_dayofweek	in_dayofyear	record_is_wknd
0	4402846	BC0E8E3602434EA6A5F29A6F6FF42233	100177	202107111032	1	20210711140042	20210711140042	3	NaN	19731004	370602	202107111358	202107121158	1	0	2021-07-11 13:58:00	2021-07-12 11:58:00	2021-07-11 13:58:00	2021-07-11	2021-07-12	2021-07-11	NaN	1	1	1	【山海边公寓】海景大床房设施齐全空调做饭宽带应有尽	NaN	NaN	371083	乳山市长江路银泰海景花园 55-301	ROOM001	2020-05-18 10:33:55	2020-07-15 10:23:58	1	1	NaN	3091	1502	4727	NaN	NaN	150	2565	3429	NaN	NaN	1.0	2020-05-18	2020-07-15	58.0	1	2021	7	11	3	6	192	1
1	4455098	01D7394D02B44376913536F6071151AD	100177	202107151446	1	20210724140006	20210724140006	3	NaN	19941001	429006	202107241358	202107261158	1	0	2021-07-24 13:58:00	2021-07-26 11:58:00	2021-07-24 13:58:00	2021-07-24	2021-07-26	2021-07-24	NaN	1	1	1	【山海边公寓】海景大床房设施齐全空调做饭宽带应有尽	NaN	NaN	371083	乳山市长江路银泰海景花园 55-301	ROOM001	2020-05-18 10:33:55	2020-07-15 10:23:58	1	1	NaN	3091	1502	4727	NaN	NaN	150	2565	3429	NaN	NaN	1.0	2020-05-18	2020-07-15	58.0	1	2021	7	24	3	5	205	1

df4['date1'] = df4['date'].astype('str')

#总数据量49547
# 周末数据量15755
use_month = [5, 6, 7, 8, 9]
df_1 = df4[(df4['in_is_wknd']==1) & (df4['in_month'].isin(use_month))]

df_1.shape

(11583, 59)

df_1['in_month'].value_counts()

7    4391
5    2520
6    2192
8    1656
9     824
Name: in_month, dtype: int64

holiday = ['2021-05-01', '2021-05-02', '2021-05-03', '2021-05-04', '2021-05-05', '2021-06-12', '2021-06-13', '2021-06-14', 
           '2021-09-19', '2021-09-20', '2021-09-21']

df4[df4['date']=='2021-05-03']

	ORDER_ID	ORDER_PRIMARY_ID	HOTELID	ORDER_TIME	STATUS	INSERT_TIME	MODIFY_TIME	FIRM	GUEST_ID	BDATE	XZQH	IN_TIME	OUT_TIME	guest_sum	guest_sum_notnull	in_time	out_time	datetime	in_date	out_date	date	JYMJ	ROOM_NUM	BED_NUM	CZLY	CALLED	BUR_CODE	STA_CODE	SSX	ADDRESS	MPHM	DJSJ	BGSJ	CALLED_freq	ADDRESS_freq	JYMJ_freq	ROOM_NUM_freq	BED_NUM_freq	CZLY_freq	BUR_CODE_freq	STA_CODE_freq	SSX_freq	MPHM_freq	FIRM_freq	room_ratio	bed_ratio	room_bed	DJ_date	BG_date	DJ_gap	in_hotel_num	in_year	in_month	in_day	in_quarter	in_dayofweek	in_dayofyear	record_is_wknd	in_is_wknd

df_2 = df4[df4['date1'].isin(holiday)]

df_2['holiday'] = 1

df_1['holiday'] = 0

df5 = pd.concat([df_2, df_1])

df5.shape

(14662, 61)

df5.drop(['CALLED', 'ADDRESS', 'INSERT_TIME', 'MODIFY_TIME', 'IN_TIME', 'OUT_TIME', 'guest_sum_notnull'], axis=1, inplace=True)

df5[:3]

	ORDER_ID	ORDER_PRIMARY_ID	HOTELID	ORDER_TIME	STATUS	FIRM	GUEST_ID	BDATE	XZQH	guest_sum	in_time	out_time	datetime	in_date	out_date	date	JYMJ	ROOM_NUM	BED_NUM	CZLY	BUR_CODE	STA_CODE	SSX	MPHM	DJSJ	BGSJ	CALLED_freq	ADDRESS_freq	JYMJ_freq	ROOM_NUM_freq	BED_NUM_freq	CZLY_freq	BUR_CODE_freq	STA_CODE_freq	SSX_freq	MPHM_freq	FIRM_freq	room_ratio	bed_ratio	room_bed	DJ_date	BG_date	DJ_gap	in_hotel_num	in_year	in_month	in_day	in_quarter	in_dayofweek	in_dayofyear	record_is_wknd	in_is_wknd	date1	holiday
9	3597092	CFD17F6BB142485DB3DEC6B5E2D0B664	100193	202105012046	1	3	NaN	19771008	132826	1	2021-05-01 20:46:00	2021-05-02 11:58:00	2021-05-01 20:46:00	2021-05-01	2021-05-02	2021-05-01	NaN	2	2	1	NaN	NaN	371083	ROOM001	2020-05-18 10:33:55	2020-07-15 10:23:58	1	1	NaN	1249	2281	4727	NaN	NaN	150	2565	3429	NaN	NaN	1.0	2020-05-18	2020-07-15	58.0	1	2021	5	1	2	5	121	1	1	2021-05-01	1
11	3601837	5C96E986C1F04691A3EB07962BCC7B00	100195	202105021435	1	3	NaN	19951009	370203	1	2021-05-02 14:35:00	2021-05-03 11:58:00	2021-05-02 14:35:00	2021-05-02	2021-05-03	2021-05-02	NaN	2	3	1	NaN	NaN	371083	ROOM001	2020-05-18 10:33:55	2020-07-15 10:23:58	1	1	NaN	1249	895	4727	NaN	NaN	150	2565	3429	NaN	NaN	1.5	2020-05-18	2020-07-15	58.0	1	2021	5	2	2	6	122	1	1	2021-05-02	1
53	3314117	C0C5516462D3487C895DD654B836F855	10037	202104082130	1	3	NaN	19730313	120103	1	2021-04-30 13:58:00	2021-05-03 11:58:00	2021-05-01 13:58:00	2021-04-30	2021-05-03	2021-05-01	NaN	1	2	1	NaN	NaN	371002	ROOM001	2020-05-18 10:22:13	2020-07-15 10:23:58	2	2	NaN	3091	2281	4727	NaN	NaN	4661	2565	3429	NaN	NaN	2.0	2020-05-18	2020-07-15	58.0	1	2021	5	1	2	5	121	1	1	2021-05-01	1

drop_col = df5.columns.tolist()

drop_col.remove('holiday')

df5.drop_duplicates(drop_col, keep='first', inplace=True)

df5.shape

(12708, 54)

dd = df_1.groupby(['in_year', 'in_month'])['ORDER_ID'].count().reset_index()

dd

	in_year	in_month	ORDER_ID	ym
0	2020	7	60	202007
1	2020	8	511	202008
2	2020	9	680	202009
3	2020	10	1487	202010
4	2020	11	665	202011
5	2020	12	904	202012
6	2021	1	999	202101
7	2021	2	71	202102
8	2021	3	2	202103
9	2021	4	4	202104
10	2021	5	2520	202105
11	2021	6	2192	202106
12	2021	7	4331	202107
13	2021	8	1145	202108
14	2021	9	144	202109
15	2021	10	40	202110

dd['ym'] = dd['in_year']*100+dd['in_month']

dd['ym'].value_counts()

202106    1
202008    1
202110    1
202109    1
202012    1
202011    1
202010    1
202009    1
202007    1
202104    1
202102    1
202101    1
202103    1
202108    1
202107    1
202105    1
202111    1
Name: ym, dtype: int64

plt.figure(figsize=(15,8))
plt.scatter(dd.index, dd['ORDER_ID'])

<matplotlib.collections.PathCollection at 0x13562173a00>

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