When sorting by a MultiIndex you need to contain the tuple describing the column inside a list*: In [11]: df.sort_values([(‘Group1’, ‘C’)], ascending=False) Out[11]: Group1 Group2 A B C A B C 2 5 6 9 1 0 0 1 1 0 3 2 5 7 3 7 0 2 0 3 5 * so as … Read more
You need set display.multi_sparse to False: #if need temporary use option with pd.option_context(‘display.multi_sparse’, False): print (df) 0 Jim Betty 20 N/A 0.201643 50 N/A 0.201643 20 10 0.201643 20 30 0.201643 If this display option is required throughout a notebook, the option can be set once and for all as follows: # if permanent use … Read more
1. Update using Python 3.6+ use f-string formatting with list comprehension: df.columns = [f'{i}{j}’ for i, j in df.columns] 2. Use map and join: df.columns = df.columns.map(”.join) 3. If your columns has numeric datatypes, use map and format: df.columns = df.columns.map(‘{0[0]}{0[1]}’.format) Output: barone bartwo bazone baztwo fooone footwo foothree A 1 2 3 4 5 … Read more
You can make a new multi index based on the Cartesian product of the levels of the existing multi index. Then, re-index your data frame using the new index. new_index = pd.MultiIndex.from_product(df.index.levels) new_df = df.reindex(new_index) # Optional: convert missing values to zero, and convert the data back # to integers. See explanation below. new_df = … Read more
pd.Grouper allows you to specify a “groupby instruction for a target object”. In particular, you can use it to group by dates even if df.index is not a DatetimeIndex: df.groupby(pd.Grouper(freq=’2D’, level=-1)) The level=-1 tells pd.Grouper to look for the dates in the last level of the MultiIndex. Moreover, you can use this in conjunction with … Read more
There is a get_level_values method that you can use in conjunction with boolean indexing to get the the intended result. In [13]: df = pd.DataFrame(np.random.random((4,4))) df.columns = pd.MultiIndex.from_product([[1,2],[‘A’,’B’]]) print df 1 2 A B A B 0 0.543980 0.628078 0.756941 0.698824 1 0.633005 0.089604 0.198510 0.783556 2 0.662391 0.541182 0.544060 0.059381 3 0.841242 0.634603 0.815334 … Read more
The reset_index() is a pandas DataFrame method that will transfer index values into the DataFrame as columns. The default setting for the parameter is drop=False (which will keep the index values as columns). All you have to do call .reset_index() after the name of the DataFrame: df = df.reset_index()
I think this will do it In [3]: df = DataFrame(dict(A = ‘foo’, B = ‘bar’, value = 1),index=range(5)).set_index([‘A’,’B’]) In [4]: df Out[4]: value A B foo bar 1 bar 1 bar 1 bar 1 bar 1 In [5]: df.to_csv(‘test.csv’) In [6]: !cat test.csv A,B,value foo,bar,1 foo,bar,1 foo,bar,1 foo,bar,1 foo,bar,1 In [7]: pd.read_csv(‘test.csv’,index_col=[0,1]) Out[7]: value … Read more
Hierarchical indexing (also referred to as “multi-level” indexing) was introduced in the pandas 0.4 release. This opens the door to some quite sophisticated data analysis and manipulation, especially for working with higher dimensional data. In essence, it enables you to effectively store and manipulate arbitrarily high dimension data in a 2-dimensional tabular structure (DataFrame), for … Read more
The most straightforward way is with .loc: >>> data.loc[:, ([‘one’, ‘two’], [‘a’, ‘b’])] one two a b a b 0 0.4 -0.6 -0.7 0.9 1 0.1 0.4 0.5 -0.3 2 0.7 -1.6 0.7 -0.8 3 -0.9 2.6 1.9 0.6 Remember that [] and () have special meaning when dealing with a MultiIndex object: (…) a … Read more