Pandas min() of selected row and columns

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)

For my tasks a universal and flexible approach is the following example:

df['Minimum'] = df[['B0', 'B1', 'B2']].apply(lambda x: min(x[0],x[1],x[2]), axis=1)

The target column 'Minimum' is assigned the result of the lambda function based on the selected DF columns['B0', 'B1', 'B2']. Access elements in a function through the function alias and his new Index(if count of elements is more then one). Be sure to specify axis=1, which indicates line-by-line calculations. This is very convenient when you need to make complex calculations. However, I assume that such a solution may be inferior in speed.

As for the selection of columns, in addition to the 'for' method, I can suggest using a filter like this:

calls_to_use = list(filter(lambda f:'B' in f, df.columns))

literally, a filter is applied to the list of DF columns through a lambda function that checks for the occurrence of the letter 'B'.

after that the first example can be written as follows:

calls_to_use = list(filter(lambda f:'B' in f, df.columns))    
df['Minimum'] = df[calls_to_use].apply(lambda x: min(x), axis=1)

although after pre-selecting the columns, it would be preferable:

df['Minimum'] = df[calls_to_use].min(axis=1)