pandas 如何基于数值变量创建分类变量
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How to create categorical variable based on a numerical variable
提问by Klausos Klausos
My DataFrame hase one column:
我的 DataFrame 有一列：
import pandas as pd
list=[1,1,4,5,6,6,30,20,80,90]
df=pd.DataFrame({'col1':list})
How can I add one more column 'col2' that would contain categorical information in reference to col1:
如何再添加一列“col2”，该列将包含参考 col1 的分类信息：
if col1 > 0 and col1 <= 10 then col2 = 'xxx'
if col1 > 10 and col1 <= 50 then col2 = 'yyy'
if col1 > 50 then col2 = 'zzz'
采纳答案by YS-L
You could first create a new column col2, and update its values based on the conditions:
您可以先创建一个新列col2，然后根据条件更新其值：
df['col2'] = 'zzz'
df.loc[(df['col1'] > 0) & (df['col1'] <= 10), 'col2'] = 'xxx'
df.loc[(df['col1'] > 10) & (df['col1'] <= 50), 'col2'] = 'yyy'
print df
Output:
输出：
col1 col2
0 1 xxx
1 1 xxx
2 4 xxx
3 5 xxx
4 6 xxx
5 6 xxx
6 30 yyy
7 20 yyy
8 80 zzz
9 90 zzz
Alternatively, you can also apply a function based on the column col1:
或者，您也可以应用基于列的函数col1：
def func(x):
if 0 < x <= 10:
return 'xxx'
elif 10 < x <= 50:
return 'yyy'
return 'zzz'
df['col2'] = df['col1'].apply(func)
and this will result in the same output.
这将导致相同的输出。
The applyapproach should be preferred in this case as it is much faster:
该apply方法应在这种情况下是首选，因为它的速度要快得多：
%timeit run() # packaged to run the first approach
# 100 loops, best of 3: 3.28 ms per loop
%timeit df['col2'] = df['col1'].apply(func)
# 10000 loops, best of 3: 187 μs per loop
However, when the size of the DataFrame is large, the built-in vectorized operations (i.e. with the masking approach) might be faster.
但是，当 DataFrame 的大小很大时，内置的矢量化操作（即使用掩码方法）可能会更快。
回答by DontDivideByZero
回答by EdChum
2 ways, use a couple loccalls to mask the rows where the conditions are met:
2 种方法，使用几个loc调用来屏蔽满足条件的行：
In [309]:
df.loc[(df['col1'] > 0) & (df['col1']<= 10), 'col2'] = 'xxx'
df.loc[(df['col1'] > 10) & (df['col1']<= 50), 'col2'] = 'yyy'
df.loc[df['col1'] > 50, 'col2'] = 'zzz'
df
Out[309]:
col1 col2
0 1 xxx
1 1 xxx
2 4 xxx
3 5 xxx
4 6 xxx
5 6 xxx
6 30 yyy
7 20 yyy
8 80 zzz
9 90 zzz
Or use a nested np.where:
或者使用嵌套的np.where：
In [310]:
df['col2'] = np.where((df['col1'] > 0) & (df['col1']<= 10), 'xxx', np.where((df['col1'] > 10) & (df['col1']<= 50), 'yyy', 'zzz'))
df
Out[310]:
col1 col2
0 1 xxx
1 1 xxx
2 4 xxx
3 5 xxx
4 6 xxx
5 6 xxx
6 30 yyy
7 20 yyy
8 80 zzz
9 90 zzz
