导出分布
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wuaho
parent
c08d244708
commit
7ae11c4b90
@ -587,6 +587,7 @@ async def scatter_model_sql(
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data = analysis.scatter_model_sql()
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return schemas.Msg(code=0, msg='ok', data=[data])
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@router.post("/scatter_model_export")
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async def retention_model_export(request: Request,
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game: str,
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@ -596,17 +597,101 @@ async def retention_model_export(request: Request,
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):
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""" 分布分析 数据导出"""
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await analysis.init(data_where=current_user.data_where)
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data = analysis.scatter_model_sql()
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res = analysis.scatter_model_sql()
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file_name = quote(f'分布分析.xlsx')
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mime = mimetypes.guess_type(file_name)[0]
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sql = data['sql']
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sql = res['sql']
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df = await ckdb.query_dataframe(sql)
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df_to_stream = DfToStream((df, '分布分析'))
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with df_to_stream as d:
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export = d.to_stream()
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return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
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interval_type = res['interval_type']
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analysis = res['analysis']
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groupby = res['groupby']
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quota_interval_arr = res['quota_interval_arr']
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if analysis != 'number_of_days' and interval_type != 'discrete':
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max_v = int(df['values'].max())
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min_v = int(df['values'].min())
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interval = (max_v - min_v) // 10 or 1
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resp = {'list': dict(),
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'start_date': res['start_date'],
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'end_date': res['end_date'],
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'time_particle': res['time_particle']
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}
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if not quota_interval_arr:
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resp['label'] = [f'[{i},{i + interval})' for i in range(min_v, max_v, interval)]
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bins = [i for i in range(min_v, max_v + interval, interval)]
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else:
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quota_interval_arr = [-float('inf')] + quota_interval_arr + [float('inf')]
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resp['label'] = []
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bins = [quota_interval_arr[0]]
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for i, v in enumerate(quota_interval_arr[1:]):
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resp['label'].append(f'[{quota_interval_arr[i]},{v})')
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bins.append(v)
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# 这是整体的
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for key, tmp_df in df.groupby('date'):
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bins_s = pd.cut(tmp_df['values'], bins=bins,
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right=False).value_counts()
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bins_s.sort_index(inplace=True)
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total = int(bins_s.sum())
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resp['list'][key.strftime('%Y-%m-%d')] = dict()
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resp['list'][key.strftime('%Y-%m-%d')]['总体'] = {'n': bins_s.to_list(), 'total': total,
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'p': round(bins_s * 100 / total, 2).to_list(),
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'title': '总体'}
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# 分组的
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if groupby:
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export_df = pd.DataFrame(columns=resp['label'])
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for key, tmp_df in df.groupby(['date', *groupby]):
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bins_s = pd.cut(tmp_df['values'], bins=bins,
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right=False).value_counts()
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bins_s.sort_index(inplace=True)
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total = int(bins_s.sum())
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title = '.'.join(key[1:])
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date = key[0]
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resp['list'][date.strftime('%Y-%m-%d')][title] = {'n': bins_s.to_list(), 'total': total,
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'p': round(bins_s * 100 / total, 2).to_list(),
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'title': title
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}
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export_df.loc[(date.strftime('%Y-%m-%d'), title)] = bins_s.to_list()
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df_to_stream = DfToStream((export_df, '分布分析'), (df, '分布分析原始数据'), index=True)
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with df_to_stream as d:
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export = d.to_stream()
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return StreamingResponse(export, media_type=mime,
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headers={'Content-Disposition': f'filename="{file_name}"'})
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# elif analysis == 'number_of_days':
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else:
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resp = {'list': {}, 'label': [],
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'start_date': res['start_date'],
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'end_date': res['end_date'],
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'time_particle': res['time_particle']
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}
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total_dict = {}
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labels = [str(i) for i in sorted(df['values'].unique())]
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resp['label'] = labels
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for key, tmp_df in df.groupby(['date']):
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total = len(tmp_df)
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dt = key.strftime('%Y-%m-%d')
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labels_dict = {}
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for key2, tmp_df2 in tmp_df.groupby('values'):
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label = str(key2)
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n = len(tmp_df2)
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labels_dict[label] = n
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resp['list'][dt] = {'总体': {'n': [labels_dict.get(i, 0) for i in labels], 'total': total,
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'p': [round(labels_dict.get(i, 0) * 100 / total, 2) for i in labels]}}
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export_df = pd.DataFrame(columns=resp['label'])
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for d, v in resp['list'].items():
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export_df.loc[d] = v['总体']['n']
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df_to_stream = DfToStream((export_df, '分布分析'), (df, '分布分析原始数据'), index=True)
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with df_to_stream as d:
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export = d.to_stream()
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return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
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@router.post("/scatter_model")
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@ -694,7 +779,7 @@ async def scatter_model(
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labels_dict[label] = n
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resp['list'][dt] = {'总体': {'n': [labels_dict.get(i, 0) for i in labels], 'total': total,
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'p': [round(labels_dict.get(i, 0)*100 / total, 2) for i in labels]}}
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'p': [round(labels_dict.get(i, 0) * 100 / total, 2) for i in labels]}}
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return schemas.Msg(code=0, msg='ok', data=resp)
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# bins_s = pd.cut(tmp_df['values'], bins=bins,
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@ -22,5 +22,5 @@ class DfToStream:
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for item in self.dfs:
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df = item[0]
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sheet_name = item[1]
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df.to_excel(self.writer, encoding='utf-8', sheet_name=sheet_name, index=False)
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df.to_excel(self.writer, encoding='utf-8', sheet_name=sheet_name, index=self.index)
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return self.output
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