I have a dataframe with columns `A`

,`B`

. I need to create a column `C`

such that for every record / row:

`C = max(A, B)`

.

How should I go about doing this?

Thanks.

You can get the maximum like this:

```
>>> import pandas as pd
>>> df = pd.DataFrame({"A": [1,2,3], "B": [-2, 8, 1]})
>>> df
A B
0 1 -2
1 2 8
2 3 1
>>> df[["A", "B"]]
A B
0 1 -2
1 2 8
2 3 1
>>> df[["A", "B"]].max(axis=1)
0 1
1 8
2 3
```

and so:

```
>>> df["C"] = df[["A", "B"]].max(axis=1)
>>> df
A B C
0 1 -2 1
1 2 8 8
2 3 1 3
```

If you know that "A" and "B" are the only columns, you could even get away with

```
>>> df["C"] = df.max(axis=1)
```

And you could use `.apply(max, axis=1)`

too, I guess.

@DSM's answer is perfectly fine in almost any normal scenario. But if you're the type of programmer who wants to go a little deeper than the surface level, you might be interested to know that it is a little faster to call numpy functions on the underlying ** .to_numpy()** (or

`.values`

for <0.24) array instead of directly calling the (cythonized) functions defined on the DataFrame/Series objects. For example, you can use ** ndarray.max()** along the first axis.

```
# Data borrowed from @DSM's post.
df = pd.DataFrame({"A": [1,2,3], "B": [-2, 8, 1]})
df
A B
0 1 -2
1 2 8
2 3 1
df['C'] = df[['A', 'B']].values.max(1)
# Or, assuming "A" and "B" are the only columns,
# df['C'] = df.values.max(1)
df
A B C
0 1 -2 1
1 2 8 8
2 3 1 3
```

If your data has `NaN`

s, you will need ** numpy.nanmax**:

```
df['C'] = np.nanmax(df.values, axis=1)
df
A B C
0 1 -2 1
1 2 8 8
2 3 1 3
```

You can also use `numpy.maximum.reduce`

. ** numpy.maximum** is a ufunc (Universal Function), and every ufunc has a

`reduce`

:```
df['C'] = np.maximum.reduce(df['A', 'B']].values, axis=1)
# df['C'] = np.maximum.reduce(df[['A', 'B']], axis=1)
# df['C'] = np.maximum.reduce(df, axis=1)
df
A B C
0 1 -2 1
1 2 8 8
2 3 1 3
```

`np.maximum.reduce`

and `np.max`

appear to be more or less the same (for most normal sized DataFrames)â€”and happen to be a shade faster than `DataFrame.max`

. I imagine this difference roughly remains constant, and is due to internal overhead (indexing alignment, handling NaNs, etc).

The graph was generated using perfplot. Benchmarking code, for reference:

```
import pandas as pd
import perfplot
np.random.seed(0)
df_ = pd.DataFrame(np.random.randn(5, 1000))
perfplot.show(
setup=lambda n: pd.concat([df_] * n, ignore_index=True),
kernels=[
lambda df: df.assign(new=df.max(axis=1)),
lambda df: df.assign(new=df.values.max(1)),
lambda df: df.assign(new=np.nanmax(df.values, axis=1)),
lambda df: df.assign(new=np.maximum.reduce(df.values, axis=1)),
],
labels=['df.max', 'np.max', 'np.maximum.reduce', 'np.nanmax'],
n_range=[2**k for k in range(0, 15)],
xlabel='N (* len(df))',
logx=True,
logy=True)
```

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