Pandas:依赖于另一个值的列

我有一个像下面这样的Pandas数据帧:

   col1  col2  col3  col4
0     5     1    11     9
1     2     3    14     7
2     6     5    54     8
3    11     2    67    44
4    23     8     2    23
5     1     5     9     8
6     9     7    45    71

我想创建一个第5列(col5),它取决于col1的值,并取其他列之一的值.

这是我希望它看起来的样子,但我遇到了一些问题.

if col1 < 3:
   col5 == col2
elif col1 < 7 & col1 >= 3:
   col5 == col3
elif col1 >= 7 & col1 < 50:
   col5 == col4

哪个会产生以下数据帧:

   col1  col2  col3  col4  col5
0     5     1    11     9    11
1     2     3    14     7     3
2     6     5    54     8    54
3    11     2    67    44    44
4    23     8     2    23    23
5    97     5     9     8     8
6     9     7    45    71    71

如果您有任何问题,请提前致谢并告诉我

您可以使用多个numpy.where,如果没有条件是True(col1 => 50)添加了最后一个值1:

df['col5'] = np.where(df['col1'] <3, df['col2'], 
             np.where((df['col1'] <7) & (df['col1'] >=3 ), df['col3'], 
             np.where((df['col1'] >=7) & (df['col1'] <50 ), df['col4'], 1))) 
print (df)
   col1  col2  col3  col4  col5
0     5     1    11     9    11
1     2     3    14     7     3
2     6     5    54     8    54
3    11     2    67    44    44
4    23     8     2    23    23
5    97     5     9     8     1
6     9     7    45    71    71

通过更改值编辑:

如果需要col4所有值>=7:

df['col5'] = np.where(df['col1'] <3, df['col2'], 
             np.where((df['col1'] <7) & (df['col1'] >=3 ), df['col3'], df['col4']))
print (df)
   col1  col2  col3  col4  col5
0     5     1    11     9    11
1     2     3    14     7     3
2     6     5    54     8    54
3    11     2    67    44    44
4    23     8     2    23    23
5    97     5     9     8     8
6     9     7    45    71    71

时间len(df)=7000:

In [441]: %timeit df['col51'] = np.where(df['col1'] <3, df['col2'], np.where((df['col1'] <7) & (df['col1'] >=3 ), df['col3'], df['col4']))
The slowest run took 5.31 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 1.25 ms per loop

In [442]: %timeit df["col52"] = df.apply(lambda x: col52(x), axis=1)
1 loop, best of 3: 552 ms per loop

In [443]: %timeit df["col53"] = [col53(c1,c2,c3,c4) for c1,c2,c3,c4 in zip(df.col1,df.col2,df.col3,df.col4)]
100 loops, best of 3: 9.87 ms per loop

时间在 len(df)=70k

In [446]: %timeit df['col51'] = np.where(df['col1'] <3, df['col2'], np.where((df['col1'] <7) & (df['col1'] >=3 ), df['col3'], df['col4']))
100 loops, best of 3: 2.5 ms per loop

In [447]: %timeit df["col52"] = df.apply(lambda x: col52(x), axis=1)
1 loop, best of 3: 5.36 s per loop

In [448]: %timeit df["col53"] = [col53(c1,c2,c3,c4) for c1,c2,c3,c4 in zip(df.col1,df.col2,df.col3,df.col4)]
10 loops, best of 3: 96.3 ms per loop

时间代码:

#change 1000 to 10000 for 70k
df = pd.concat([df]*1000).reset_index(drop=True)

def col52(x):
    if x["col1"] < 3:
        return x["col2"]
    elif x["col1"] >=3 and x["col1"] < 7:
        return x["col3"]
    elif x["col1"] >= 7 and x["col1"] < 50:
        return x["col4"] 
def col53(c1,c2,c3,c4):
    if c1 < 3:
        return c2
    elif c1 >=3 and c1 < 7:
        return c3
    elif c1>= 7 and c1< 50:
        return c4    

df['col51'] = np.where(df['col1'] <3, df['col2'], np.where((df['col1'] <7) & (df['col1'] >=3 ), df['col3'], df['col4']))       
df["col52"] = df.apply(lambda x: col52(x), axis=1)
df["col53"] = [col53(c1,c2,c3,c4) for c1,c2,c3,c4 in zip(df.col1,df.col2,df.col3,df.col4)]
print (df)