Python 如何在熊猫中使用就地保存修改？

如果使用此代码删除列实体：

t、 删除“实体”，轴=1，在位=True

然后，前面的数据帧再次出现在这里。那么，如何使用inplace=True保存修改，以便删除实体列呢

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你可以两个都来一次

rd.drop[实体，代码]，轴=1，在位=True

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不清楚你想放弃什么。如果使用axis=1，则尝试删除列。。。在这种情况下，看起来您的代码工作正常，我没有看到数据帧输出中的列代码

如果您试图根据Entity的值删除行，则需要使用df.loc的索引，如中所示

`df.drop(df.loc[df['columnsname']=="Entity"].index, inplace=True)` 

或

以此为例

from dataframefromstring import DataFrameFromString

df = DataFrameFromString() # Just loads a df for this example. Nothing to do with your code

# show the starting dataframe    
print("=== Original DF ==============")
print(df)

# Drop the column "Name"
df.drop('Name', axis=1, inplace=True)
print("=== Dropped column ==============")
print(df)

# Drop multiple columns
df.drop(['Ticket_No', "Fare"], axis=1, inplace=True)
print("=== Dropped mutiple columns ==============")
print(df)

# Drop rows based on column X having some value
df.drop(df.loc[df['Sex']=="male"].index, inplace=True)
print("=== Drop row where Column = <something> ==============")
print(df)

# Drop rows based on multiple values value
df.drop(df.loc[ (df['Sex'].isnull() ) | (df["Age"] < 42 ) ].index, inplace=True)
print("=== Drop rows based on multiple conditions ==============")
print(df)

输出：

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您的t.drop'Entity'，axis=1，inplace=True的代码是正确的

在运行drop命令之前，您还可以向我们显示数据帧吗？据我所见，您的代码似乎运行良好。另外，请将您的代码、数据和所需输出以缩进文本而不是图片的形式放入问题中，以便我们可以重新创建问题。

from dataframefromstring import DataFrameFromString

df = DataFrameFromString() # Just loads a df for this example. Nothing to do with your code

# show the starting dataframe    
print("=== Original DF ==============")
print(df)

# Drop the column "Name"
df.drop('Name', axis=1, inplace=True)
print("=== Dropped column ==============")
print(df)

# Drop multiple columns
df.drop(['Ticket_No', "Fare"], axis=1, inplace=True)
print("=== Dropped mutiple columns ==============")
print(df)

# Drop rows based on column X having some value
df.drop(df.loc[df['Sex']=="male"].index, inplace=True)
print("=== Drop row where Column = <something> ==============")
print(df)

# Drop rows based on multiple values value
df.drop(df.loc[ (df['Sex'].isnull() ) | (df["Age"] < 42 ) ].index, inplace=True)
print("=== Drop rows based on multiple conditions ==============")
print(df)

=== Original DF ==============
      Name     Sex   Age Ticket_No    Fare
0   Braund    male  22.0   HN07681  2500.0
1      NaN  female  42.0   HN05681  6895.0
2    peter    male   NaN    KKSN55   800.0
3      NaN    male  56.0   HN07681  2500.0
4    Daisy  female  22.0   hf55s44     NaN
5   Manson     NaN  48.0   HN07681  8564.0
6   Piston    male   NaN   HN07681  5622.0
7  Racline  female  42.0   Nh55146     NaN
8      NaN    male  22.0   HN07681  4875.0
9      NaN     NaN   NaN       NaN     NaN

=== Dropped column ==============
      Sex   Age Ticket_No    Fare
0    male  22.0   HN07681  2500.0
1  female  42.0   HN05681  6895.0
2    male   NaN    KKSN55   800.0
3    male  56.0   HN07681  2500.0
4  female  22.0   hf55s44     NaN
5     NaN  48.0   HN07681  8564.0
6    male   NaN   HN07681  5622.0
7  female  42.0   Nh55146     NaN
8    male  22.0   HN07681  4875.0
9     NaN   NaN       NaN     NaN

=== Dropped mutiple columns ==============
      Sex   Age
0    male  22.0
1  female  42.0
2    male   NaN
3    male  56.0
4  female  22.0
5     NaN  48.0
6    male   NaN
7  female  42.0
8    male  22.0
9     NaN   NaN

=== Drop row where Column = <something> ==============
      Sex   Age
1  female  42.0
4  female  22.0
5     NaN  48.0
7  female  42.0
9     NaN   NaN

=== Drop rows based on multiple conditions ==============
      Sex   Age
1  female  42.0
7  female  42.0