Python 使用 map 时的 Pandas 警告：试图在 DataFrame 的切片副本上设置值
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Pandas warning when using map: A value is trying to be set on a copy of a slice from a DataFrame
提问by Mike
I've got the following code and it works. This basically renames values in columns so that they can be later merged.
我有以下代码并且它有效。这基本上是重命名列中的值，以便以后可以合并它们。
pop = pd.read_csv('population.csv')
pop_recent = pop[pop['Year'] == 2014]
mapping = {
'Korea, Rep.': 'South Korea',
'Taiwan, China': 'Taiwan'
}
f= lambda x: mapping.get(x, x)
pop_recent['Country Name'] = pop_recent['Country Name'].map(f)
Warning: 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 the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copypop_recent['Country Name'] = pop_recent['Country Name'].map(f)
警告： 正在尝试在来自 DataFrame 的切片副本上设置值。尝试使用 .loc[row_indexer,col_indexer] = value 代替查看文档中的警告：http: //pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy pop_recent[' Country Name'] = pop_recent['Country Name'].map(f)
I did google this! But no examples seem to be using map, so I'm at a loss...
我确实谷歌了这个！但似乎没有例子使用地图，所以我不知所措......
采纳答案by Anand S Kumar
The issue is with chained indexing, what you are actually trying to do is to set values to - pop[pop['Year'] == 2014]['Country Name']- this would not work most of the times (as explained very well in the linked documentation) as this is two different calls and one of the calls may return a copy of the dataframe (I believe the boolean indexing) is returning the copy of the dataframe).
问题在于链式索引，您实际尝试做的是将值设置为 - pop[pop['Year'] == 2014]['Country Name']- 这在大多数情况下不起作用（如链接文档中所解释的那样），因为这是两个不同的调用和其中一个调用可能会返回数据帧的副本（我相信布尔索引）正在返回数据帧的副本）。
Hence, when you try to set values to that copy, it does not reflect in the original dataframe. Example -
因此，当您尝试为该副本设置值时，它不会反映在原始数据框中。例子 -
In [6]: df
Out[6]:
A B
0 1 2
1 3 4
2 4 5
3 6 7
4 8 9
In [7]: df[df['A']==1]['B'] = 10
/path/to/ipython-script.py:1: 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 the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
if __name__ == '__main__':
In [8]: df
Out[8]:
A B
0 1 2
1 3 4
2 4 5
3 6 7
4 8 9
As noted , instead of chained indexing you should use DataFrame.locto index the rows as well as the columns to update in a single call, avoiding this error. Example -
如前所述，您应该使用DataFrame.loc索引行和列而不是链式索引以在一次调用中更新，从而避免此错误。例子 -
pop.loc[(pop['year'] == 2014), 'Country Name'] = pop.loc[(pop['year'] == 2014), 'Country Name'].map(f)
Or if this seem too long to you, you can create a mask (boolean dataframe) beforehand and assign to a variable, and use that in the above statement. Example -
或者，如果这对您来说太长，您可以事先创建一个掩码（布尔数据帧）并分配给一个变量，然后在上面的语句中使用它。例子 -
mask = pop['year'] == 2014
pop.loc[mask,'Country Name'] = pop.loc[mask,'Country Name'].map(f)
Demo -
演示 -
In [9]: df
Out[9]:
A B
0 1 2
1 3 4
2 4 5
3 6 7
4 8 9
In [10]: mapping = { 1:2 , 3:4}
In [11]: f= lambda x: mapping.get(x, x)
In [12]: df.loc[(df['B']==2),'A'] = df.loc[(df['B']==2),'A'].map(f)
In [13]: df
Out[13]:
A B
0 2 2
1 3 4
2 4 5
3 6 7
4 8 9
Demo with the maskmethod -
使用掩码方法演示-
In [18]: df
Out[18]:
A B
0 1 2
1 3 4
2 4 5
3 6 7
4 8 9
In [19]: mask = df['B']==2
In [20]: df.loc[mask,'A'] = df.loc[mask,'A'].map(f)
In [21]: df
Out[21]:
A B
0 2 2
1 3 4
2 4 5
3 6 7
4 8 9
回答by Gregg
I recommend you to reset indicesin pop_recent = pop[pop['Year'] == 2014].
我建议你重置指数在pop_recent = pop[pop['Year'] == 2014]。
If you want to apply some function to some column of dataframe, try to use function applyfunction of DataFrame API. Simple demo:
如果你想对数据帧的某列应用某个函数，请尝试使用applyDataFrame API 的函数函数。简单演示：
mapping = {
'Korea, Rep.': 'South Korea',
'Taiwan, China': 'Taiwan'
}
df = pandas.DataFrame({'Country':['Korea, Rep.', 'Taiwan, China', 'Japan', 'USA'], 'date':[2014, 2014, 2015, 2014]})
df_recent = df[df['date'] == 2014].reset_index()
df_recent['Country'] = df_recent['Country'].apply(lambda x: mapping.get(x, x))
Output:
输出：
>>> df_recent
index Country date
0 0 South Korea 2014
1 1 Taiwan 2014
2 3 USA 2014
