[已解决]如何修改才能消除这警告，求大师帮忙，谢谢

futui

警告内容如下：

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
  filtered_df['B'] = filtered_df['B'].astype(int)
最大值: 12
对应的日期: 2022-12-15

运行的代码：
import pandas as pd

data = [["004243","C","2022-12-22",'',"4"],["162719","A","2022-12-20","8","6"],
       ["004243","C","2022-12-19",'',"12"],["519212","B","2022-12-18","56","3"],
       ["162411","E","2022-12-17","8","7"],["007844","FC","2022-12-16","","8"],
       ["163208","G","2022-12-15","66","12"],["162719","A","2022-12-14","8","6"],
        ["519212","B","2022-12-12","56","3"],["162411","E","2022-12-11","8","7"],
        ["007844","FC","2022-12-10","","18"],["163208","G","2022-12-8","66","12"]]
columns = ["编号","名称","日期","A","B"]
df = pd.DataFrame(data, columns=columns)

# 将日期列转换为日期格式
df['日期'] = pd.to_datetime(df['日期'])

# 筛选日期大于2022-12-13的行
filtered_df = df[df['日期'] > '2022-12-13']

# 将B列转换为整数格式
filtered_df['B'] = filtered_df['B'].astype(int)

# 找到B列的最大值
max_value = filtered_df['B'].max()

# 找到对应最大值的日期
max_date = filtered_df.loc[filtered_df['B'] == max_value, '日期'].iloc[-1]

print("最大值:", max_value)
print("对应的日期:", max_date.strftime('%Y-%m-%d'))

最佳答案

月排行榜 / 总排行榜

isdkz

2023-4-18 09:04:32

futui 发表于 2023-4-18 08:41
新的提示：
: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set ...

为了解决DeprecationWarning，请使用df[df.columns[i]] = newvals替代.loc[]方法。以下是修改后的代码：

import pandas as pd

data = [["004243","C","2022-12-22",'',"4"],["162719","A","2022-12-20","8","6"],
       ["004243","C","2022-12-19",'',"12"],["519212","B","2022-12-18","56","3"],
       ["162411","E","2022-12-17","8","7"],["007844","FC","2022-12-16","","8"],
       ["163208","G","2022-12-15","66","12"],["162719","A","2022-12-14","8","6"],
        ["519212","B","2022-12-12","56","3"],["162411","E","2022-12-11","8","7"],
        ["007844","FC","2022-12-10","","18"],["163208","G","2022-12-8","66","12"]]
columns = ["编号","名称","日期","A","B"]
df = pd.DataFrame(data, columns=columns)

# 将日期列转换为日期格式
df['日期'] = pd.to_datetime(df['日期'])

# 筛选日期大于2022-12-13的行
filtered_df = df[df['日期'] > '2022-12-13'].copy()

# 将B列转换为整数格式
filtered_df[filtered_df.columns[4]] = filtered_df['B'].astype(int)

# 找到B列的最大值
max_value = filtered_df['B'].max()

# 找到对应最大值的日期
max_date = filtered_df.loc[filtered_df['B'] == max_value, '日期'].iloc[-1]

print("最大值:", max_value)
print("对应的日期:", max_date.strftime('%Y-%m-%d'))

跳转到最佳答案楼层

isdkz

警告里有这一句提示：Try using .loc[row_indexer,col_indexer] = value instead

故您可以使用.loc[row_indexer,col_indexer] = value来修改数据，以消除SettingWithCopyWarning警告。请参考以下修改后的代码：

import pandas as pd

data = [["004243","C","2022-12-22",'',"4"],["162719","A","2022-12-20","8","6"],
       ["004243","C","2022-12-19",'',"12"],["519212","B","2022-12-18","56","3"],
       ["162411","E","2022-12-17","8","7"],["007844","FC","2022-12-16","","8"],
       ["163208","G","2022-12-15","66","12"],["162719","A","2022-12-14","8","6"],
        ["519212","B","2022-12-12","56","3"],["162411","E","2022-12-11","8","7"],
        ["007844","FC","2022-12-10","","18"],["163208","G","2022-12-8","66","12"]]
columns = ["编号","名称","日期","A","B"]
df = pd.DataFrame(data, columns=columns)

# 将日期列转换为日期格式
df['日期'] = pd.to_datetime(df['日期'])

# 筛选日期大于2022-12-13的行
filtered_df = df[df['日期'] > '2022-12-13'].copy()

# 将B列转换为整数格式
filtered_df.loc[:, 'B'] = filtered_df['B'].astype(int)

# 找到B列的最大值
max_value = filtered_df['B'].max()

# 找到对应最大值的日期
max_date = filtered_df.loc[filtered_df['B'] == max_value, '日期'].iloc[-1]

print("最大值:", max_value)
print("对应的日期:", max_date.strftime('%Y-%m-%d'))

请注意，在筛选出新的DataFrame时，使用copy()方法来创建新的DataFrame副本，以避免在原始DataFrame上进行修改。在将'B'列转换为整数格式时，我们使用.loc[]方法。

futui

新的提示：
: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  filtered_df.loc[:, 'B'] = filtered_df['B'].astype(int)
最大值: 12
对应的日期: 2022-12-15

isdkz

futui 发表于 2023-4-18 08:41
新的提示：
: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set ...

为了解决DeprecationWarning，请使用df[df.columns[i]] = newvals替代.loc[]方法。以下是修改后的代码：

import pandas as pd

data = [["004243","C","2022-12-22",'',"4"],["162719","A","2022-12-20","8","6"],
       ["004243","C","2022-12-19",'',"12"],["519212","B","2022-12-18","56","3"],
       ["162411","E","2022-12-17","8","7"],["007844","FC","2022-12-16","","8"],
       ["163208","G","2022-12-15","66","12"],["162719","A","2022-12-14","8","6"],
        ["519212","B","2022-12-12","56","3"],["162411","E","2022-12-11","8","7"],
        ["007844","FC","2022-12-10","","18"],["163208","G","2022-12-8","66","12"]]
columns = ["编号","名称","日期","A","B"]
df = pd.DataFrame(data, columns=columns)

# 将日期列转换为日期格式
df['日期'] = pd.to_datetime(df['日期'])

# 筛选日期大于2022-12-13的行
filtered_df = df[df['日期'] > '2022-12-13'].copy()

# 将B列转换为整数格式
filtered_df[filtered_df.columns[4]] = filtered_df['B'].astype(int)

# 找到B列的最大值
max_value = filtered_df['B'].max()

# 找到对应最大值的日期
max_date = filtered_df.loc[filtered_df['B'] == max_value, '日期'].iloc[-1]

print("最大值:", max_value)
print("对应的日期:", max_date.strftime('%Y-%m-%d'))