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

You can simply use this:

'07311954' in df.date.values which returns True or False

Here is the further explanation:

In pandas, using in check directly with DataFrame and Series (e.g. val in df or val in series ) will check whether the val is contained in the Index.

BUT you can still use in check for their values too (instead of Index)! Just using val in df.col_name.values or val in series.values. In this way, you are actually checking the val with a Numpy array.

And .isin(vals) is the other way around, it checks whether the DataFrame/Series values are in the vals. Here vals must be set or list-like. So this is not the natural way to go for the question.