小编Emi*_* OB的帖子

我开发电子商务应用程序已经大约 3 个月了，最近我切换到 MacO。\n当我在 iOS 模拟器中运行代码时遇到问题\每次运行它时都会收到此消息。

\nCocoaPods' output:\n\xe2\x86\xb3\n      Preparing\n    Analyzing dependencies\n    Inspecting targets to integrate\n      Using `ARCHS` setting to build architectures of target `Pods-Runner`: (``)\n    Fetching external sources\n    -> Fetching podspec for `Flutter` from `Flutter`\n    -> Fetching podspec for `image_picker` from `.symlinks/plugins/image_picker/ios`\n    -> Fetching podspec for `path_provider` from `.symlinks/plugins/path_provider/ios`\n    -> Fetching podspec for `shared_preferences` from `.symlinks/plugins/shared_preferences/ios`\n    -> Fetching podspec for `sqflite` from `.symlinks/plugins/sqflite/ios`\n    Resolving dependencies of `Podfile`\nError output from CocoaPods:\n\xe2\x86\xb3\n    Ignoring ffi-1.15.4 because its extensions are not built. Try: …

我有相同长度的列表a, b, ,... 。c我想按照排序获得的顺序对所有这些进行排序a，即我可以执行装饰-排序-取消装饰模式

a, b, c = map(list, zip(*sorted(zip(a, b, c))))

或类似的东西。但是，我希望列表就地排序（我假设将sorted传递给它的临时迭代器中的所有内容拉到临时列表中，然后zip填充到三个输出列表中，因此输入中的每个数据都被不必要地复制两次）无需创建临时对象。所以我的意思不是：

a_sorted, b_sorted, c_sorted = map(list, zip(*sorted(zip(a, b, c))))
a[:] = a_sorted
b[:] = b_sorted
c[:] = c_sorted

我怎样才能做到这一点？

我正在使用 Pandas 数据框，并将趋势应用于最早的数据点，以尝试尽可能最好地填充缺失的历史数据。我知道迭代 Pandas 数据框是错误的，但我还没有找到替代方法来做到这一点，因为新值取决于下一个值。如果有人知道实现这一目标的更好方法，那就太好了！

示例 df：

   Week no  Data  Trend
0        1   0.0    1.5
1        2   0.0    1.5
2        3   0.0    1.0
3        4   0.0    0.5
4        5  10.0    0.6

我目前使用的代码：

for wk in range(len(df)-1, 0, -1):
       if (df.loc[wk, 'Data'] != 0 and df.loc[wk-1, 'Data'] == 0
               and not math.isnan(df.loc[wk, 'Trend'])):
           df.loc[wk-1, 'Data'] = (df.loc[wk, 'Data']
                                          *df.loc[wk, 'Trend'])

结果：

  Week no  Data  Trend
0        1   4.5    1.5
1        2   3.0    1.5
2        3   3.0    1.0
3        4   6.0    0.5 …

compare我在 Pandas 文档中找到了该函数：

pandas.DataFrame.compare
DataFrame.compare(other, align_axis=1, keep_shape=False, keep_equal=False)

来源

   def New_names(self, df_old, df_new):

    #New records for insert
    df_old_names = set(df_old['name'])
    df_new_names = set(df_new['name'])

    difference = df_new_names - df_old_names
    coincidence = df_old_names - difference

    exit_insert = df_new[df_new['name'].isin(difference)]

    #Check old records

    df_old_names_restrict = df_old[df_new.columns]

    exit_update = df_new.compare(df_old_names_restrict)
    

    return (exit_insert, exit_update)

但它会引发以下错误：

Traceback (most recent call last):   File
"C:/Users/shulya403/Shulya403_works/all_gid_2/Database/db_insert_pd.py",
line 203, in <module>
 FillDB.Pruducts_to_SQL(df_new=FillDB.df_Products.head(20))   File "C:/Users/shulya403/Shulya403_works/all_gid_2/Database/db_insert_pd.py",
line 184, in Pruducts_to_SQL
    df_select = self.New_names(self.Select_SQL_Products(), df_new)[0]   File
"C:/Users/shulya403/Shulya403_works/all_gid_2/Database/db_insert_pd.py",
line 175, in New_names
    exit_update = …