# How to determine whether a Pandas Column contains a particular value

I am trying to determine whether there is an entry in a Pandas column that has a particular value. I tried to do this with `if x in df['id']`

. I thought this was working, except when I fed it a value that I knew was not in the column `43 in df['id']`

it still returned `True`

. When I subset to a data frame only containing entries matching the missing id `df[df['id'] == 43]`

there are, obviously, no entries in it. How to I determine if a column in a Pandas data frame contains a particular value and why doesn't my current method work? (FYI, I have the same problem when I use the implementation in this answer to a similar question).

`in`

of a Series checks whether the value is in the index:

```
In [11]: s = pd.Series(list('abc'))
In [12]: s
Out[12]:
0 a
1 b
2 c
dtype: object
In [13]: 1 in s
Out[13]: True
In [14]: 'a' in s
Out[14]: False
```

One option is to see if it's in unique values:

```
In [21]: s.unique()
Out[21]: array(['a', 'b', 'c'], dtype=object)
In [22]: 'a' in s.unique()
Out[22]: True
```

or a python set:

```
In [23]: set(s)
Out[23]: {'a', 'b', 'c'}
In [24]: 'a' in set(s)
Out[24]: True
```

As pointed out by @DSM, it may be more efficient (especially if you're just doing this for one value) to just use in directly on the values:

```
In [31]: s.values
Out[31]: array(['a', 'b', 'c'], dtype=object)
In [32]: 'a' in s.values
Out[32]: True
```

From: stackoverflow.com/q/21319929

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