I am sure there is an obvious way to do this but cant think of anything slick right now.
Basically instead of raising exception I would like to get True
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)]
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
df = pandas.DataFrame({'g':[1]}, index=['isStop'])
#df.loc['g']
if 'g' in df.index:
print("find g")
if 'isStop' in df.index:
print("find a")
with DataFrame: df_data
>>> df_data
id name value
0 a ampha 1
1 b beta 2
2 c ce 3
I tried:
>>> getattr(df_data, 'value').isin([1]).any()
True
>>> getattr(df_data, 'value').isin(['1']).any()
True
but:
>>> 1 in getattr(df_data, 'value')
True
>>> '1' in getattr(df_data, 'value')
False
So fun :D
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