multi-index

I'm suspicious that this is trivial, but I yet to discover the incantation that will let me select rows from a Pandas dataframe based on the values of a hierarchical key. So, for example, imagine we have the following dataframe: import pandas df = pandas.DataFrame({'group1': ['a','a','a','b','b','b'], 'group2': ['c','c','d','d','d','e'], 'value1': [1.1,2,3,4,5,6], 'value2': [7.1,8,9,10,11,12] }) df = df.set_index(['group1', 'group2']) df looks as we would expect: If df were not indexed on group1 I could do the following: df['group1' == 'a'] But that fails on this dataframe with an index. So

I have a pandas dataframe with 3 levels of a MultiIndex. I am trying to pull out rows of this dataframe according to a list of values that correspond to two of the levels. I have something like this: ix = pd.MultiIndex.from_product([[1, 2, 3], ['foo', 'bar'], ['baz', 'can']], names=['a', 'b', 'c']) data = np.arange(len(ix)) df = pd.DataFrame(data, index=ix, columns=['hi']) print(df) hi a b c 1 foo baz 0 can 1 bar baz 2 can 3 2 foo baz 4 can 5 bar baz 6 can 7 3 foo baz 8 can 9 bar baz 10 can 11 Now I want to take all rows where index levels 'b' and 'c' are in this index: ix_use = pd.MultiIndex

I have a dataframe, grouped , with multiindex columns as below: import pandas as pd codes = ["one","two","three"]; colours = ["black", "white"]; textures = ["soft", "hard"]; N= 100 # length of the dataframe df = pd.DataFrame({ 'id' : range(1,N+1), 'weeks_elapsed' : [random.choice(range(1,25)) for i in range(1,N+1)], 'code' : [random.choice(codes) for i in range(1,N+1)], 'colour': [random.choice(colours) for i in range(1,N+1)], 'texture': [random.choice(textures) for i in range(1,N+1)], 'size': [random.randint(1,100) for i in range(1,N+1)], 'scaled_size': [random.randint(100,1000) for i in

For example, I have: In [1]: df = pd.DataFrame([8, 9], index=pd.MultiIndex.from_tuples([(1, 1, 1), (1, 3, 2)]), columns=['A']) In [2] df Out[2]: A 1 1 1 8 3 2 9 Is there a better way to remove the last level from the index than this: In [3]: pd.DataFrame(df.values, index=df.index.droplevel(2), columns=df.columns) Out[3]: A 1 1 8 3 9 df.reset_index(level=2, drop=True) Out[29]: A 1 1 8 3 9 Andy Hayden You don't need to create a new DataFrame instance! You can modify the index: df.index = df.index.droplevel(2) df A 1 1 8 3 9 You can also specify negative indices, for selection from the end: df

I have a multi-index data frame with columns 'A' and 'B'. Is there is a way to select rows by filtering on one column of the multi-index without resetting the index to a single column index? For Example. # has multi-index (A,B) df #can I do this? I know this doesn't work because the index is multi-index so I need to specify a tuple df.ix[df.A ==1] One way is to use the get_level_values Index method: In [11]: df Out[11]: 0 A B 1 4 1 2 5 2 3 6 3 In [12]: df.iloc[df.index.get_level_values('A') == 1] Out[12]: 0 A B 1 4 1 In 0.13 you'll be able to use xs with drop_level argument : df.xs(1, level='A

I have DataFrame with MultiIndex columns that looks like this: # sample data col = pd.MultiIndex.from_arrays([['one', 'one', 'one', 'two', 'two', 'two'], ['a', 'b', 'c', 'a', 'b', 'c']]) data = pd.DataFrame(np.random.randn(4, 6), columns=col) data What is the proper, simple way of selecting only specific columns (e.g. ['a', 'c'] , not a range) from the second level? Currently I am doing it like this: import itertools tuples = [i for i in itertools.product(['one', 'two'], ['a', 'c'])] new_index = pd.MultiIndex.from_tuples(tuples) print(new_index) data.reindex_axis(new_index, axis=1) It doesn't