# How do I create a new df.column based on 2+ conditions in Pandas without iteration?

I have a normal df

`````` A = pd.DataFrame([[1, 5, 2], [2, 4, 4], [3, 3, 1], [4, 2, 2], [5, 1, 4]],
columns=['A', 'B', 'C'], index=[1, 2, 3, 4, 5])
```

```

If I want to create a column based on a condition in another column, I will do something like this and it works as expected.

`````` In : A['D'] = A['C'] > 2
In : A
Out:
A  B  C      D
1  1  5  2  False
2  2  4  4   True
3  3  3  1  False
4  4  2  2  False
5  5  1  4   True
```

```

However, if I want to do the same using 2 conditions ... for example:

``````A['D'] = A['C'] > 2 and A['B'] > 2      or     A['D'] = A['C'] > 2 & A['B'] > 2
```

```

I get infamous

``````ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
```

```

How can I solve without iteration? The purpose of creating this new column based on two conditions is to use a groupby function of type:

``````A.groupby('D').apply(custom_fuction)
```

```

So, maybe there is a way to use groupby to accomplish all of this, but I don't know how.

thank

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Use `&`

, not `and`

for performing elementary logical operations and operations:

``````In : A['D'] = (A['C'] > 2) & (A['B'] > 2)

In : A
Out:
A  B  C      D
1  1  5  2  False
2  2  4  4   True
3  3  3  1  False
4  4  2  2  False
5  5  1  4  False
```

```

You can also omit the column definition `D`

:

``````In : A.groupby((A['C'] > 2) & (A['B'] > 2))
Out: <pandas.core.groupby.DataFrameGroupBy object at 0xab5b6ac>
```

```
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