def product_report(date):
api_list = ['product_list', 'home', 'product']
#api_list=['home','product','article','product_list','cart','order','topic_list','topic_view','topic_create','private_msg','reply','topic_like','topic_follow','device']
al_df = pd.concat(da.getOneDayUnionDf(date, api_list))
al_df = al_df.sort('time', ascending=True)
al_df['action'] = al_df['time'] + '&' + al_df['api_tag'] + '&' + al_df[
'values'].map(str)
al_df['uuid'] = al_df['app_id'] + ' ' + al_df['device_id']
grouped = al_df.groupby('uuid')
actions = grouped['action'].agg(' , '.join)
result = grouped['api_tag'].agg(','.join).map(combine).value_counts()
total = sum(result)
typed_product_list = []
for key in actions.keys():
typed_product_list += getViewProductType(key, actions.get(key, ''))
result_df = pd.DataFrame(typed_product_list,
columns=[
'api_key', 'device_id', 'time', 'api_type',
'value', 'from_type'
])
result_df['count'] = 1
typed_product_list = result_df.groupby(['value',
'from_type'])['count'].sum()
print type(typed_product_list)
total = 0
for key in typed_product_list.keys():
product_id, from_type = key
num = typed_product_list.get(key, 0)
total += num
print product_id, from_type, num
def getCommunityDfs(date, device_ids):
c_list = [
'topic_list', 'topic_view', 'topic_create', 'private_msg', 'reply',
'topic_like', 'topic_follow'
]
all_dfs = da.getOneDayUnionDf(date, c_list)
targetDevices_dfs = map(lambda x: targetDevicesDf(x, device_ids), all_dfs)
return pd.concat(targetDevices_dfs)
def product_pv_statics(date):
#print pr_format.format('new','active','pv','new_user_order','all_order','date')
home_df,device_df,product_df,order_df=da.getOneDayUnionDf(date,['home','device','product','order'])
new_user_devices=device_df['device_id'].unique()
return pr_format.format(len(home_df['device_id'].unique()),\
len(new_user_devices),\
len(product_df),\
len(order_df[order_df['device_id'].isin(new_user_devices)]),\
len(order_df),date)
def product_pv_statics(date):
#print pr_format.format('new','active','pv','new_user_order','all_order','date')
home_df, device_df, product_df, order_df = da.getOneDayUnionDf(
date, ['home', 'device', 'product', 'order'])
new_user_devices = device_df['device_id'].unique()
return pr_format.format(len(home_df['device_id'].unique()),\
len(new_user_devices),\
len(product_df),\
len(order_df[order_df['device_id'].isin(new_user_devices)]),\
len(order_df),date)
def product_report(date):
api_list=['product_list','home','product']
#api_list=['home','product','article','product_list','cart','order','topic_list','topic_view','topic_create','private_msg','reply','topic_like','topic_follow','device']
al_df=pd.concat(da.getOneDayUnionDf(date,api_list))
al_df=al_df.sort('time',ascending=True)
al_df['action']=al_df['time']+'&'+al_df['api_tag']+'&'+al_df['values'].map(str)
al_df['uuid']=al_df['app_id']+' '+al_df['device_id']
grouped=al_df.groupby('uuid')
actions=grouped['action'].agg(' , '.join)
result=grouped['api_tag'].agg(','.join).map(combine).value_counts()
total=sum(result)
typed_product_list=[]
for key in actions.keys():
typed_product_list+=getViewProductType(key,actions.get(key,''))
result_df=pd.DataFrame(typed_product_list,columns=['api_key','device_id','time','api_type','value','from_type'])
result_df['count']=1
typed_product_list=result_df.groupby(['value','from_type'])['count'].sum()
print type(typed_product_list)
total=0
for key in typed_product_list.keys():
product_id,from_type=key
num=typed_product_list.get(key,0)
total+=num
print product_id,from_type,num
def getTargetDevicesDfs(datetime, device_ids):
import data_mining as dm
all_dfs = da.getOneDayUnionDf(datetime, dm.all_api_list)
targetDevices_dfs = map(lambda x: targetDevicesDf(x, device_ids), all_dfs)
return pd.concat(targetDevices_dfs)
def getActiveUserdevices(date):
c_list = [
'topic_list', 'topic_view', 'topic_create', 'private_msg', 'reply',
'topic_like', 'topic_follow'
]
return pd.concat(da.getOneDayUnionDf(date, c_list))['device_id'].unique()
def getCommunityDfs(date,device_ids):
c_list=['topic_list','topic_view','topic_create','private_msg','reply','topic_like','topic_follow']
all_dfs=da.getOneDayUnionDf(date,c_list)
targetDevices_dfs=map(lambda x:targetDevicesDf(x,device_ids),all_dfs)
return pd.concat(targetDevices_dfs)
def getTargetDevicesDfs(datetime,device_ids):
import data_mining as dm
all_dfs=da.getOneDayUnionDf(datetime,dm.all_api_list)
targetDevices_dfs=map(lambda x:targetDevicesDf(x,device_ids),all_dfs)
return pd.concat(targetDevices_dfs)
def getActiveUserdevices(date):
c_list=['topic_list','topic_view','topic_create','private_msg','reply','topic_like','topic_follow']
return pd.concat(da.getOneDayUnionDf(date,c_list))['device_id'].unique()