Pandas: get the min value between 2 dataframe columns

Use df.min(axis=1)

df['c'] = df.min(axis=1)dfOut[41]:    A  B  c0  2  1  11  2  1  12  2  4  23  2  4  24  3  5  35  3  5  36  3  6  37  3  6  3

This returns the min row-wise (when passing axis=1)

For non-heterogenous dtypes and large dfs you can use numpy.min which will be quicker:

In[42]:df['c'] = np.min(df.values,axis=1)dfOut[42]:    A  B  c0  2  1  11  2  1  12  2  4  23  2  4  24  3  5  35  3  5  36  3  6  37  3  6  3

timings:

In[45]:df = pd.DataFrame({'A': [2, 2, 2, 2, 3, 3, 3, 3],                   'B': [1, 1, 4, 4, 5, 5, 6, 6]})df = pd.concat([df]*1000, ignore_index=True)df.shapeOut[45]: (8000, 2)

So for a 8K row df:

%timeit df.min(axis=1)%timeit np.min(df.values,axis=1)314 µs ± 3.63 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)34.4 µs ± 161 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

You can see that the numpy version is nearly 10x quicker (note I pass df.values so we pass a numpy array), this will become more of a factor when we get to even larger dfs

Note

for versions 0.24.0 or greater, use to_numpy()

so the above becomes:

df['c'] = np.min(df.to_numpy(),axis=1)

Timings:

%timeit df.min(axis=1)%timeit np.min(df.values,axis=1)%timeit np.min(df.to_numpy(),axis=1)314 µs ± 3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)35.2 µs ± 680 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)35.5 µs ± 262 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

There is a minor discrepancy between .values and to_numpy(), it depends on whether you know upfront that the dtype is not mixed, and that the likely dtype is a factor e.g. float 16 vs float 32 see that link for further explanation. Pandas is doing a little more checking when calling to_numpy