带有 df.loc 的 Pandas/Python 中的 SettingWithCopyWarning 消息

Question

OBS：我在 SO、Pandas 文档和其他一些网站上搜索了几个小时，但无法理解我的代码在哪里工作。

我的 UDF：

def indice(dfb, lb, ub):
    dfb['isOutlier'] = ~dfb['valor_unitario'].between(lb, ub)
    dfb = dfb[~dfb.isOutlier]

    dfb['indice'] = (dfb['valor_unitario'] - lb) / (ub - lb) * 2000
    df = df.astype({'indice': 'int64'})
    return dfb

重要：

• isOutlier 列不存在。我现在正在这个函数中创建它。
• indice 列不存在。我现在正在这个函数中创建它。
• valor_unitario 存在并且它是一个浮点数
• lb 和 ub 是之前定义的
• 此函数在主代码的循环内（但由于 n=0 引发此警告）

发出警告

C:\Users\...\calculoindice_support.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  dfb['isOutlier'] = ~dfb['valor_unitario'].between(lb, ub)

我在网上找到了一些文章和问题，而且 StackOverflow 说使用 loc 可以解决问题。我试过了，但没有成功

1º 尝试 - 使用 loc

def indice(dfb, lb, ub):
->  dfb.loc[:,'isOutlier'] = ~dfb['valor_unitario'].between(lb, ub)
    dfb = dfb[~dfb.isOutlier]

->  dfb.loc[:,'indice'] = (dfb['valor_unitario'] - lb) / (ub - lb) * 2000
    df = df.astype({'indice': 'int64'})
    return dfb

我也尝试过每次都使用 loc 其实我尝试了很多可能的组合...尝试在dfb['valor_unitario'] 中使用df.loc 等等 em>

现在我有两次相同的警告，但有点不同：

self._setitem_single_column(ilocs[0], value, pi) 和 self.obj[key] = value

C:\ProgramData\Anaconda3\envs\Indice\lib\site-packages\pandas\core\indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-> self._setitem_single_column(ilocs[0], value, pi)

和

C:\ProgramData\Anaconda3\envs\Indice\lib\site-packages\pandas\core\indexing.py:1597: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-> self.obj[key] = value

我也尝试过使用复制。第一次出现这个警告，简单使用copy() 解决了这个问题，我不知道为什么现在它不起作用（我只是加载了更多数据）

2º 尝试 - 使用 copy()

我尝试将copy()放在三个地方，没有成功

dfb = dfb[~dfb.isOutlier].copy()

dfb['isOutlier'] = ~dfb['valor_unitario'].between(lb, ub).copy()

dfb['isOutlier'] = ~dfb['valor_unitario'].copy().between(lb, ub)

C:\Users\...\calculoindice_support.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  dfb['isOutlier'] = ~dfb['valor_unitario'].between(lb, ub)

我没有更多的想法，非常感谢您的支持。

------- Minimun Reproducible Example --------

Main_testing.py

import pandas as pd
import calculoindice_support as indice # module 01
import getitemsid_support as getitems # module 02

df = pd.DataFrame({'loja':[1,4,6,6,4,5,7,8],
                   'cod_produto':[21,21,21,55,55,43,26,30],
                   'valor_unitario':[332.21,333.40,333.39,220.40,220.40,104.66,65.00,14.00],
                   'documento':['324234','434144','532552','524523','524525','423844','529585','239484'],
                   'empresa':['ABC','ABC','ABC','ABC','ABC','CDE','CDE','CDE']
                   })

nome_coluna = 'cod_produto'
# getting items id to loop over them
product_ids = getitems.getitemsid(df, nome_coluna)

# initializing main DF with no data 
df_nf = pd.DataFrame(columns=list(df.columns.values))

n = 0
while n < len(product_ids):
    item = product_ids[n]
    df_item = df[df[nome_coluna] == item]
    # assigning bounds to each variable
    lb, ub = indice.limites(df_item, 10)
    # calculating index over DF, using LB and UB
    # creating temporary (for each loop) DF
    df_nf_aux = indice.indice(df_item, lb, ub)
    # assigning temporary DF to main DF that will be exported later
    df_nf = pd.concat([df_nf, df_nf_aux],ignore_index=True)
    n += 1

calculoindice_support.py（模块 01）

import pandas as pd

def limites(dfa,n):
    n_sigma = n * dfa.valor_unitario.std()
    mean = dfa.valor_unitario.mean()
    lb: float = mean - n_sigma
    ub: float = mean + n_sigma
    return (lb, ub)


def indice(dfb, lb, ub):
    if lb == ub:
        dfb.loc[:, 'isOutlier'] = False
        dfb.loc[:, 'indice'] = 1
    else:
        dfb['isOutlier'] = ~dfb['valor_unitario'].between(lb, ub)
        dfb = dfb[~dfb.isOutlier]

        dfb['indice'] = (dfb['valor_unitario'] - lb) / (ub - lb) * 2000
        # df = df.astype({'indice': 'int64'})

    return dfb

getitemsid_support.py（模块 02）

def getitemsid(df, coluna):
    a = df[coluna].tolist()
    return list(set(a))

警告输出：

C:\Users\...\calculoindice_support.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  dfb['isOutlier'] = ~dfb['valor_unitario'].between(lb, ub)

C:\ProgramData\Anaconda3\envs\Indice\lib\site-packages\pandas\core\indexing.py:1597: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self.obj[key] = value
C:\ProgramData\Anaconda3\envs\Indice\lib\site-packages\pandas\core\indexing.py:1720: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(loc, value, pi)

C:\Users\...\calculoindice_support.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  dfb['isOutlier'] = ~dfb['valor_unitario'].between(lb, ub)
C:\Users\...\calculoindice_support.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  dfb['isOutlier'] = ~dfb['valor_unitario'].between(lb, ub)

Answer 1

问题出在您的Main_testing.py

while n < len(product_ids):
    df_item = df[df[nome_coluna] == item]

    df_nf_aux = indice.indice(df_item, lb, ub)

首先你用条件df[nome_coluna] == item分割你的df，这将返回一个数据帧的副本（你可以通过访问_is_view或_is_copy属性来检查这个）。然后将过滤后的数据框传递给indice 方法。

def indice(dfb, lb, ub):
    dfb['isOutlier'] = ~dfb['valor_unitario'].between(lb, ub)

在indice 方法中，您为过滤后的数据框分配一个新列。这是一个隐式链式赋值。 Pandas 不知道您是要将新列添加到原始数据框中还是仅添加到过滤后的数据框中，所以 Pandas 会给您一个警告。

要抑制这个警告，你可以明确告诉 pandas 你想做什么

def indice(dfb, lb, ub):
    dfb = dfb.copy()
    dfb['isOutlier'] = ~dfb['valor_unitario'].between(lb, ub)

在上述情况下，我创建了过滤后的数据框的副本。这意味着我想将新列添加到过滤后的非原始数据框中。