Check if a value exists in pandas dataframe index

Multi index works a little different from single index. Here are some methods for multi-indexed dataframe.

df = pd.DataFrame({'col1': ['a', 'b','c', 'd'], 'col2': ['X','X','Y', 'Y'], 'col3': [1, 2, 3, 4]}, columns=['col1', 'col2', 'col3'])
df = df.set_index(['col1', 'col2'])

in df.index works for the first level only when checking single index value.

'a' in df.index     # True
'X' in df.index     # False

Check df.index.levels for other levels.

'a' in df.index.levels[0] # True
'X' in df.index.levels[1] # True

Check in df.index for an index combination tuple.

('a', 'X') in df.index  # True
('a', 'Y') in df.index  # False

Just for reference as it was something I was looking for, you can test for presence within the values or the index by appending the ".values" method, e.g.

g in df.<your selected field>.values
g in df.index.values

I find that adding the ".values" to get a simple list or ndarray out makes exist or "in" checks run more smoothly with the other python tools. Just thought I'd toss that out there for people.

This should do the trick

'g' in df.index

Code below does not print boolean, but allows for dataframe subsetting by index... I understand this is likely not the most efficient way to solve the problem, but I (1) like the way this reads and (2) you can easily subset where df1 index exists in df2:

df3 = df1[df1.index.isin(df2.index)]

or where df1 index does not exist in df2...

df3 = df1[~df1.index.isin(df2.index)]