Python 长度必须匹配以进行比较（根据两个标准进行选择）

我正在为每个用户生成值，如下所示：

loDf = locDfs[user] # locDfs is a copy of locationDf elsewhere in the code... sorry for all the variable names.
loDf.reset_index(inplace=True)
loDf = pd.crosstab([loDf.date, loDf.uid], loDf.location_id)
loDf.reset_index(inplace=True)

loDf.set_index('date', inplace=True)
loDf.drop('uid', axis=1, inplace=True)

# join the location crosstab columns with the app crosstab columns per user
userLocAppDfs[user] = userLocAppDfs[user].join(loDf, how='outer')
# convert from just "1" at each location change event followed by zeros, to "1" continuing until next location change
userLocAppDfs[user] = userLocAppDfs[user].resample('15T').agg('max')
userLocAppDfs[user]['uid'].fillna(user, inplace=True)

这将获取位置数据并将位置id转换为列，并将其与timeseries中的其他数据组合

这基本上包括重塑数据。然后我需要规范化，要做到这一点，我需要查看每个列的值：

for loc in locationDf[(locationDf['location_id'] in loDf.columns.values) & (locationDf['uid'] == user)].location_id.unique():

以下是完整的函数：

def normalize(inputMetricDf, inputLocationDf):
    '''
    normalize, resample, and combine data into a single data source
    '''
    metricDf = inputMetricDf.copy()
    locationDf = inputLocationDf.copy()

    appDf = metricDf[['date', 'uid', 'app_id', 'metric']].copy()

    locDf = locationDf[['date', 'uid', 'location_id']]
    locDf.set_index('date', inplace=True)

    # convert location data to "15 minute interval" rows
    locDfs = {}
    for user, user_loc_dc in locDf.groupby('uid'):
        locDfs[user] = user_loc_dc.resample('15T').agg('max').bfill()

    aDf = appDf.copy()
    aDf.set_index('date', inplace=True)

    userLocAppDfs = {}
    user = ''
    for uid, a2_df in aDf.groupby('uid'):
        user = uid
        # per user, convert app data to 15m interval
        userDf = a2_df.resample('15T').agg('max')

        # assign metric for each app to an app column for each app, per user
        userDf.reset_index(inplace=True)
        userDf = pd.crosstab(index=userDf['date'], columns=userDf['app_id'],
                             values=userDf['metric'], aggfunc=np.mean).fillna(np.nan, downcast='infer')

        userDf['uid'] = user

        userDf.reset_index(inplace=True)
        userDf.set_index('date', inplace=True)

        # reapply 15m intervals now that we have new data per app
        userLocAppDfs[user] = userDf.resample('15T').agg('max')

        # assign location data to location columns per location, creates a "1" at the 15m interval of the location change event in the location column created
        loDf = locDfs[user]
        loDf.reset_index(inplace=True)
        loDf = pd.crosstab([loDf.date, loDf.uid], loDf.location_id)
        loDf.reset_index(inplace=True)

        loDf.set_index('date', inplace=True)
        loDf.drop('uid', axis=1, inplace=True)

        # join the location crosstab columns with the app crosstab columns per user
        userLocAppDfs[user] = userLocAppDfs[user].join(loDf, how='outer')
        # convert from just "1" at each location change event followed by zeros, to "1" continuing until next location change
        userLocAppDfs[user] = userLocAppDfs[user].resample('15T').agg('max')
        userLocAppDfs[user]['uid'].fillna(user, inplace=True)

        for loc in locationDf[(locationDf['location_id'] in loDf.columns.values) & (locationDf['uid'] == user)].location_id.unique():
            # fill location NaNs
            userLocAppDfs[user][loc] = userLocAppDfs[user][loc].replace(
                np.nan, 0)

        # fill app NaNs
        for app in a2_df['app_id'].unique():
            userLocAppDfs[user][app].interpolate(
                method='linear', limit_area='inside', inplace=True)
            userLocAppDfs[user][app].fillna(value=0, inplace=True)

    df = userLocAppDfs[user].copy()

    # ensure actual normality
    alpha = 0.05
    for app in aDf['app_id'].unique():
        _, p = normaltest(userLocAppDfs[user][app])
        if(p > alpha):
            raise DataNotNormal(args=(user, app))

    # for loc in userLocAppDfs[user]:
        # could also test location data

    return df

但这会产生错误：

  File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py", line 346, in run_http_function
    result = _function_handler.invoke_user_function(flask.request)
  File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py", line 223, in invoke_user_function
    loop.run_until_complete(future)
  File "/opt/python3.7/lib/python3.7/asyncio/base_events.py", line 573, in run_until_complete
    return future.result()
  File "/user_code/main.py", line 31, in default_model
    train, endog, exog, _, _, rawDf = preprocess(ledger, apps)
  File "/user_code/Wrangling.py", line 67, in preprocess
    rawDf = normalize(appDf, locDf)
  File "/user_code/Wrangling.py", line 185, in normalize
    for loc in locationDf[(locationDf['location_id'] in loDf.columns.values) & (locationDf['uid'] == user)].location_id.unique():
  File "/env/local/lib/python3.7/site-packages/pandas/core/ops.py", line 1745, in wrapper
    raise ValueError('Lengths must match to compare')
ValueError: Lengths must match to compare

在我注意到由于重塑可能会丢失locationsDf中的位置之前，我只是在做：

    for loc in locationDf[locationDf['uid'] == user].location_id.unique():

这对其他任何情况都有效。但是，如果在同一个15t时间段中有两个位置，其中一个只出现在那里，但由于15t窗口而被删除，那么它会给我一个错误。所以我需要另一个条件

locationDf['location\u id']只是一个字符串，就像交叉表列名一样

为什么这会让我犯错误

尝试回答的错误： 将您的条件更改为（使用

isin

）

更新

con1 = (locationDf['location_id'].isin(loDf.columns.values)
con2 = (locationDf['uid'].isin(pd.Series(user))

locationDf.loc[con1&con2,'location_id'].unique()

---

我现在把这个推到我的应用程序上，我想它会工作的。。但是为什么它们都必须是isin，而其中一个是？@robertotomás我不确定

user

是

类型列表

还是字符串它是一个列表，我把它括在括号里，并把它推到了错误的底部，所以你需要在这里额外的保护

（locationDf['uid'].isin（pd.Series（user）））

@robertomasás

locationDf.loc[(locationDf['location_id'].isin(loDf.columns.values)) 
           & (locationDf['uid'].isin(user)),'location_id'].unique()

con1 = (locationDf['location_id'].isin(loDf.columns.values)
con2 = (locationDf['uid'].isin(pd.Series(user))

locationDf.loc[con1&con2,'location_id'].unique()