I am trying to create a column which contains only the minimum of the one row and a few columns, for example:
A0 A1 A2 B0 B1 B2 C0 C1
0 0.84 0.47 0.55 0.46 0.76 0.42 0.24 0.75
1 0.43 0.47 0.93 0.39 0.58 0.83 0.35 0.39
2 0.12 0.17 0.35 0.00 0.19 0.22 0.93 0.73
3 0.95 0.56 0.84 0.74 0.52 0.51 0.28 0.03
4 0.73 0.19 0.88 0.51 0.73 0.69 0.74 0.61
5 0.18 0.46 0.62 0.84 0.68 0.17 0.02 0.53
6 0.38 0.55 0.80 0.87 0.01 0.88 0.56 0.72
Here I am trying to create a column which contains the minimum for each row of columns B0, B1, B2.
The output would look like this:
A0 A1 A2 B0 B1 B2 C0 C1 Minimum
0 0.84 0.47 0.55 0.46 0.76 0.42 0.24 0.75 0.42
1 0.43 0.47 0.93 0.39 0.58 0.83 0.35 0.39 0.39
2 0.12 0.17 0.35 0.00 0.19 0.22 0.93 0.73 0.00
3 0.95 0.56 0.84 0.74 0.52 0.51 0.28 0.03 0.51
4 0.73 0.19 0.88 0.51 0.73 0.69 0.74 0.61 0.51
5 0.18 0.46 0.62 0.84 0.68 0.17 0.02 0.53 0.17
6 0.38 0.55 0.80 0.87 0.01 0.88 0.56 0.72 0.01
Here is part of the code, but it is not doing what I want it to do:
for i in range(0,2):
df['Minimum'] = df.loc[0,'B'+str(i)].min()
This is a one-liner, you just need to use the axis argument for min to tell it to work across the columns rather than down:
df['Minimum'] = df.loc[:, ['B0', 'B1', 'B2']].min(axis=1)
If you need to use this solution for different numbers of columns, you can use a for loop or list comprehension to construct the list of columns:
n_columns = 2
cols_to_use = ['B' + str(i) for i in range(n_columns)]
df['Minimum'] = df.loc[:, cols_to_use].min(axis=1)
DenPO
df['Minimum'] = df[['B0', 'B1', 'B2']].apply(lambda x: min(x[0],x[1],x[2]), axis=1)
来源：https://stackoverflow.com/questions/25479607/pandas-min-of-selected-row-and-columns