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单事件属性漏斗概率

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Àî×ÚÕñ 2022-08-18 22:34:51 +08:00
parent b75e84b3cf
commit 240cbccb52
1 changed files with 155 additions and 104 deletions
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259
api/api_v1/endpoints/query.py
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@ -1061,112 +1061,51 @@ async def funnel_model(
date_range = res['date_range']
cond_level = res['cond_level']
groupby = res['groupby']
switch_test = analysis.event_view.switchTest
if switch_test:
df = await ckdb.query_dataframe(sql)
if df.empty:
return schemas.Msg(code=-9, msg='无数据', data=None)
# 补齐level数据
concat_data = []
for key, tmp_df in df.groupby(['date'] + groupby):
not_exists_level = {i for i in range(1, len(cond_level) + 1)} - set(tmp_df['level'])
for item in not_exists_level:
key = key if isinstance(key, tuple) else (key,)
concat_data.append((*key, item, 0))
# 合并数据
df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
df = await ckdb.query_dataframe(sql)
if df.empty:
return schemas.Msg(code=-9, msg='无数据', data=None)
# 补齐level数据
concat_data = []
for key, tmp_df in df.groupby(['date'] + groupby):
not_exists_level = {i for i in range(1, len(cond_level) + 1)} - set(tmp_df['level'])
for item in not_exists_level:
key = key if isinstance(key, tuple) else (key,)
concat_data.append((*key, item, 0))
# 合并数据
df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
# df.set_index('date',inplace=True)
data_list = []
date_data = {}
if df.shape == (0, 0):
return schemas.Msg(code=0, msg='ok', data={'list': data_list, 'level': cond_level})
# df.set_index('date',inplace=True)
data_list = []
date_data = {}
if df.shape == (0, 0):
return schemas.Msg(code=0, msg='ok', data={'list': data_list, 'level': cond_level})
tmp = {'title': '总体'}
# 以level分组后的和
tmp_df = df[['level', 'values']].groupby('level').sum()
# 在原数据上对索引进行排序
tmp_df.sort_index(inplace=True)
for i in tmp_df.index:
tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
tmp['n'] = tmp_df['values'].to_list()
tmp['p1'] = [100]
# tmp['p2'] = []
for i, v in tmp_df.loc[2:, 'values'].items():
tmp['p1'].append(round(v * 100 / tmp_df.loc[1, 'values'], 2))
# tmp['p2'].append(round(v*100 / tmp_df.loc[i - 1, 'values'], 2))
data_list.append(tmp)
# 补齐日期
all_idx = {(dt, lv) for dt in date_range for lv in range(1, len(cond_level) + 1)}
concat_data = []
for i in all_idx - set(df.set_index(['date', 'level']).index):
concat_data.append((*i, 0))
summary_df = pd.concat(
[df[['date', 'level', 'values']], pd.DataFrame(concat_data, columns=['date', 'level', 'values'])])
for key, tmp_df in summary_df.groupby('date'):
tmp_df = tmp_df.groupby('level').sum()
tmp = {'title': '总体'}
# 以level分组后的和
tmp_df = df[['level', 'values']].groupby('level').sum()
# 在原数据上对索引进行排序
tmp_df.sort_index(inplace=True)
for i in tmp_df.index:
tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
tmp = dict()
tmp['n'] = tmp_df['values'].to_list()
tmp['p1'] = [100]
# tmp['p2'] = []
for i, v in tmp_df.loc[2:, 'values'].items():
var = round(v * 100 / tmp_df.loc[1, 'values'], 2)
var = 0 if np.isnan(var) else var
tmp['p1'].append(var)
tmp['p1'].append(round(v * 100 / tmp_df.loc[1, 'values'], 2))
# tmp['p2'].append(round(v*100 / tmp_df.loc[i - 1, 'values'], 2))
data_list.append(tmp)
_ = date_data.setdefault(key.strftime('%Y-%m-%d'), {})
_['总体'] = tmp
# 分组
if groupby:
# 补齐数据
# 补齐日期
all_idx = {(dt, lv) for dt in date_range for lv in range(1, len(cond_level) + 1)}
concat_data = []
idx = set(df.set_index(['date'] + groupby).index)
all_idx = {(*j, i) for i in range(1, len(cond_level) + 1) for j in idx}
for i in all_idx - set(df.set_index(list(('date', *groupby, 'level'))).index):
for i in all_idx - set(df.set_index(['date', 'level']).index):
concat_data.append((*i, 0))
df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
# df.sort_values(list((*groupby, 'level')), inplace=True, ascending=False)
# 映射对应中文返回给前端展示
for i in groupby:
if i == 'svrindex':
if game == 'mfmh5':
game = 'mzmfmh5'
chinese = {}
resp = await crud.select_map.get_one(db, game, i)
if not resp:
continue
for ii in resp:
chinese[ii['id']] = ii['title']
for k, v in chinese.items():
# 开始映射
df.loc[df[i] == k, i] = v
for key, tmp_df in df.groupby(groupby):
tmp = {'title': key}
tmp_df = tmp_df.groupby('level').sum()
tmp_df.sort_index(inplace=True)
for i in tmp_df.index:
tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
tmp['n'] = tmp_df['values'].to_list()
tmp['p1'] = [100]
# tmp['p2'] = []
for i, v in tmp_df.loc[2:, 'values'].items():
var = round(v * 100 / tmp_df.loc[1, 'values'], 2)
var = 0 if np.isnan(var) else var
tmp['p1'].append(var)
# tmp['p2'].append(round(v*100 / tmp_df.loc[i - 1, 'values'], 2))
data_list.append(tmp)
for key, tmp_df in df.groupby(['date'] + groupby):
summary_df = pd.concat(
[df[['date', 'level', 'values']], pd.DataFrame(concat_data, columns=['date', 'level', 'values'])])
for key, tmp_df in summary_df.groupby('date'):
tmp_df = tmp_df.groupby('level').sum()
tmp_df.sort_index(inplace=True)
for i in tmp_df.index:
@ -1183,18 +1122,130 @@ async def funnel_model(
tmp['p1'].append(var)
# tmp['p2'].append(round(v*100 / tmp_df.loc[i - 1, 'values'], 2))
_ = date_data.setdefault(key[0].strftime('%Y-%m-%d'), {})
# [key[1]] = tmp
title = (groupby or ['总体']) + cond_level
resp = {'list': data_list,
'date_data': date_data,
'title': title,
'level': cond_level,
'start_date': res['start_date'],
'end_date': res['end_date'],
'time_particle': res['time_particle']
}
return schemas.Msg(code=0, msg='ok', data=resp)
_ = date_data.setdefault(key.strftime('%Y-%m-%d'), {})
_['总体'] = tmp
# 分组
if groupby:
# 补齐数据
concat_data = []
idx = set(df.set_index(['date'] + groupby).index)
all_idx = {(*j, i) for i in range(1, len(cond_level) + 1) for j in idx}
for i in all_idx - set(df.set_index(list(('date', *groupby, 'level'))).index):
concat_data.append((*i, 0))
df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
# df.sort_values(list((*groupby, 'level')), inplace=True, ascending=False)
# 映射对应中文返回给前端展示
for i in groupby:
if i == 'svrindex':
if game == 'mfmh5':
game = 'mzmfmh5'
chinese = {}
resp = await crud.select_map.get_one(db, game, i)
if not resp:
continue
for ii in resp:
chinese[ii['id']] = ii['title']
for k, v in chinese.items():
# 开始映射
df.loc[df[i] == k, i] = v
for key, tmp_df in df.groupby(groupby):
tmp = {'title': key}
tmp_df = tmp_df.groupby('level').sum()
tmp_df.sort_index(inplace=True)
for i in tmp_df.index:
tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
tmp['n'] = tmp_df['values'].to_list()
tmp['p1'] = [100]
# tmp['p2'] = []
for i, v in tmp_df.loc[2:, 'values'].items():
var = round(v * 100 / tmp_df.loc[1, 'values'], 2)
var = 0 if np.isnan(var) else var
tmp['p1'].append(var)
# tmp['p2'].append(round(v*100 / tmp_df.loc[i - 1, 'values'], 2))
data_list.append(tmp)
for key, tmp_df in df.groupby(['date'] + groupby):
tmp_df = tmp_df.groupby('level').sum()
tmp_df.sort_index(inplace=True)
for i in tmp_df.index:
tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
tmp = dict()
tmp['n'] = tmp_df['values'].to_list()
tmp['p1'] = [100]
# tmp['p2'] = []
for i, v in tmp_df.loc[2:, 'values'].items():
var = round(v * 100 / tmp_df.loc[1, 'values'], 2)
var = 0 if np.isnan(var) else var
tmp['p1'].append(var)
# tmp['p2'].append(round(v*100 / tmp_df.loc[i - 1, 'values'], 2))
_ = date_data.setdefault(key[0].strftime('%Y-%m-%d'), {})
# [key[1]] = tmp
title = (groupby or ['总体']) + cond_level
resp = {'list': data_list,
'date_data': date_data,
'title': title,
'level': cond_level,
'switch_test': switch_test,
'start_date': res['start_date'],
'end_date': res['end_date'],
'time_particle': res['time_particle']
}
return schemas.Msg(code=0, msg='ok', data=resp)
else:
try:
res = await analysis.guide_model_sql()
except Exception as e:
return schemas.Msg(code=-9, msg='报表配置参数异常')
sql = res['sql']
df = await ckdb.query_dataframe(sql)
if df.empty:
return schemas.Msg(code=-9, msg='无数据', data=None)
group_str = res['analysis']
# 转int
df[group_str] = df[group_str].astype(int)
step_list = [str(i) for i in sorted(df[group_str].unique())]
dict_k = {}
for k, nedf in df.groupby("date"):
ste_k = {}
for kk, ste_df in nedf.groupby(group_str):
value_list = ste_df.iloc[:, -1].to_list()
ste_k[str(kk)] = int(sum(value_list))
for ste in step_list:
if ste not in list(ste_k.keys()):
ste_k[ste] = 0
dict_k[str(k)] = ste_k
p_data = {}
data = {}
for dict_key, dict_data in dict_k.items():
dict_data1 = deepcopy(dict_data)
dict_k1 = {int(k): v for k, v in dict_data1.items()}
sorted(dict_k1.keys())
data_values = list(dict_k1.values())
p_values = [round(i / sum(data_values), 2) or 0 for i in data_values]
p_values.insert(0, dict_key)
data_values.insert(0, dict_key)
data[dict_key] = data_values
p_data[dict_key] = p_values
step_list.insert(0, '日期')
resp = {'list': data,
'date_data': p_data,
'title': '1',
'level': step_list,
'switch_test': switch_test,
'start_date': res['start_date'],
'end_date': res['end_date'],
'time_particle': res['time_particle']
}
return schemas.Msg(code=0, msg='ok', data=resp)
@router.post("/scatter_model_sql")