如何在熊猫中使用就地保存修改？答案

【问题标题】：How do i save modification using inplace in pandas?如何在熊猫中使用就地保存修改？
【发布时间】：2019-10-26 09:43:06
【问题描述】：

如果我使用此代码删除列 Entity：

t.drop('Entity', axis=1, inplace=True)

然后之前的数据帧再次来到这里。那么如何使用inplace=True 保存修改以便删除Entity 列？

【问题讨论】：

您能否在运行 drop 命令之前向我们展示 DataFrame？从我所见，您的代码看起来运行良好。另外，请将您的代码、数据和所需输出作为缩进文本而不是图片放入问题中，以便我们重新创建问题。

标签： python python-3.x pandas jupyter-notebook

【解决方案1】：

你可以把两个都放进去一次。

rd.drop(["Entity", "Code"], axis=1, inplace=True)

【讨论】：

【解决方案2】：

你t.drop('Entity', axis=1, inplace=True)的代码是正确的！

【讨论】：

【解决方案3】：

不清楚你想放弃什么。如果您使用的是axis=1，那么您正在尝试删除 column ...在这种情况下，您的代码看起来可以正常工作（我在 out DataFrame 输出中没有看到Code 列.)

如果您尝试根据 Entity 的值删除 rows，您需要使用 df.loc 的索引，如...

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

或

`df.drop(df.loc[df['Entity']=="<some value>"].index, inplace=True)`

以这个为例……

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：

=== 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

【讨论】：