Python 使用.loc[row\u indexer，col\u indexer]=value更新列时收到警告

我正试图对收到的数据进行清理

代码如下：

将熊猫作为pd导入
def清理（df:pd.DataFrame）->pd.DataFrame:
#从IT部门删除条目
掩码=（df['dept']！='IT'）
df=df[遮罩]
#将其余行的部门从marketing重命名为comms
mask=df['dept']=='marketing'
df.loc[掩码，'部门]=“通信”
#警告出现在这里。。。
#将其余行的部门从会计重命名为财务
mask=df['dept']=='accounting'
df.loc[掩码，'部门]='财务'
返回df
数据=[[1，“营销”]，[2，“会计”]，[3，“营销”]，[4，“IT”]，[5，“IT”]，[6，“董事会”]]
df=pd.DataFrame（数据，列=['id'，'dept']）
df=清理（df）

我得到以下警告：

/opt/conda/lib/python3.7/site packages/pandas/core/index.py:480:SettingWithCopyWarning: 试图在数据帧切片的副本上设置值。 尝试改用.loc[row\u indexer，col\u indexer]=value

请参阅文档中的注意事项： self.obj[item]=s

我有点担心这个警告，因为返回的数据是正确的，并且在这种情况下似乎不适用

我的代码有什么问题吗？

---

或者我可以安全地忽略警告吗？

错误表明了这一点。您的

df

是由

df=df[mask]

生成的另一帧的切片。尝试更新原始帧，而不是切片：

def cleanup(df: pd.DataFrame) -> pd.DataFrame:
    # Remove entries from the IT dept
    mask1 = (df['dept'] != 'IT')

    # Rename the dept from marketing to comms for the remaining rows
    mask2 = df['dept'] == 'marketing'
    df.loc[mask1 & mask2, 'dept'] = "comms"
    # The warning occurs here...

    # Rename the dept from accounting to finance for the remaining rows
    mask2 = df['dept'] == 'accounting'
    df.loc[mask1&mask2, 'dept'] = 'finance'

    return df

data = [[1,"marketing"],[2,"accounting"],[3,"marketing"],[4,"IT"],[5,"IT"],[6,"board"]]
df = pd.DataFrame(data, columns = ['id', 'dept'])
df=cleanup(df)

修改后的函数返回新的

df

，其中包含

部门中的
值。事实上，如果您不想要这些记录，您可以复制并更新这些记录：

def cleanup(df: pd.DataFrame) -> pd.DataFrame:
    # Remove entries from the IT dept
    mask = (df['dept'] != 'IT')

    # we copy the data frame here so it's no longer a slice
    df = df[mask].copy()  

    # Rename the dept from marketing to comms for the remaining rows
    mask = df['dept'] == 'marketing'
    df.loc[mask, 'dept'] = "comms"
    # The warning occurs here...

    # Rename the dept from accounting to finance for the remaining rows
    mask = df['dept'] == 'accounting'
    df.loc[mask, 'dept'] = 'finance'

    return df

错误说明了这一点。您的
df
是由
df=df[mask]
生成的另一帧的切片。尝试更新原始帧，而不是切片：

def cleanup(df: pd.DataFrame) -> pd.DataFrame:
    # Remove entries from the IT dept
    mask1 = (df['dept'] != 'IT')

    # Rename the dept from marketing to comms for the remaining rows
    mask2 = df['dept'] == 'marketing'
    df.loc[mask1 & mask2, 'dept'] = "comms"
    # The warning occurs here...

    # Rename the dept from accounting to finance for the remaining rows
    mask2 = df['dept'] == 'accounting'
    df.loc[mask1&mask2, 'dept'] = 'finance'

    return df

data = [[1,"marketing"],[2,"accounting"],[3,"marketing"],[4,"IT"],[5,"IT"],[6,"board"]]
df = pd.DataFrame(data, columns = ['id', 'dept'])
df=cleanup(df)

修改后的函数返回新的
df
，其中包含
部门中的
值。事实上，如果您不想要这些记录，您可以复制并更新这些记录：

def cleanup(df: pd.DataFrame) -> pd.DataFrame:
    # Remove entries from the IT dept
    mask = (df['dept'] != 'IT')

    # we copy the data frame here so it's no longer a slice
    df = df[mask].copy()  

    # Rename the dept from marketing to comms for the remaining rows
    mask = df['dept'] == 'marketing'
    df.loc[mask, 'dept'] = "comms"
    # The warning occurs here...

    # Rename the dept from accounting to finance for the remaining rows
    mask = df['dept'] == 'accounting'
    df.loc[mask, 'dept'] = 'finance'

    return df

关于
df=df[mask].copy（）
你不再使用切片了吗？关于
df=df[mask].copy（）
你不再使用切片了吗？