multi-index

The pandas pivot tables documentation seems to recomend dealing with more than two dimensions of data by using multiindexing: In [1]: import pandas as pd In [2]: import numpy as np In [3]: import pandas.util.testing as tm; tm.N = 3 In [4]: def unpivot(frame): ...: N, K = frame.shape ...: data = {'value' : frame.values.ravel('F'), ...: 'variable' : np.asarray(frame.columns).repeat(N), ...: 'date' : np.tile(np.asarray(frame.index), K)} ...: return pd.DataFrame(data, columns=['date', 'variable', 'value']) ...: In [5]: df = unpivot(tm.makeTimeDataFrame()) In [6]: df Out[6]: date variable value

This question already has an answer here: Select rows in pandas MultiIndex DataFrame 1 answer I have a multi-index dataframe that looks like uid tid text abc x t1 bcd y t2 uid and tid are the indexes. I have a list of uid s, and want to get the rows corresponding to the uids in that list, but keeping the 2nd level index values (tid). I want to do it without running any explicit loop. Is that possible? Data: L = ['abc', 'bcd'] print (df) text uid tid abc x t1 abc1 x t1 bcd y t2 1. slicers idx = pd.IndexSlice df1 = df.loc[idx[L,:],:] 2. boolean indexing + mask with get_level_values + isin : df1

I have an Excel file with some (mostly) nicely grouped rows. I built a fake example below. Is there a way to get read_excel in Pandas to produce a multiindex preserving this structure? For this example the MultiIndex would have four levels (Family, Individual, Child (optional), investment). If the subtotal values were lost that would be fine as they can easily be recreated in Pandas. No, pandas can't read such a structure. An alternative solution is to use pandas to read your data, but transform this into an easily accessible dictionary, rather than keeping your data in a dataframe with

I am attempting to update the first N rows in a multi-index dataframe but was having a bit of trouble finding a solution so thought I'd create a post for it. The example code is as follows: # Imports import numpy as np import pandas as pd # Set Up Data Frame dates = pd.date_range('1/1/2000', periods=8) df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D']) df['DATE'] = dates df['CATEGORY'] = ['A','B','A','B','A','B','A','B'] # Set Index df.set_index(['CATEGORY','DATE'],inplace=True) df.sort(inplace=True) # Get First Two Rows of Each Category df.groupby(level=0).apply(lambda x:

Given the following pivot table: df=pd.DataFrame({'A':['a','a','a','a','a','b','b','b','b'], 'B':['x','y','z','x','y','z','x','y','z'], 'C':['a','b','a','b','a','b','a','b','a'], 'D':[7,5,3,4,1,6,5,3,1]}) table = pd.pivot_table(df, index=['A', 'B','C'],aggfunc='sum') table D A B C a x a 7 b 4 y a 1 b 5 z a 3 b x a 5 y b 3 z a 1 b 6 I'd like to access each value of 'C' (or level 2) as a list to use for plotting. I'd like to do the same for 'A' and 'B' (levels 0 and 1) in such a way that it preserves spacing so that I can use those lists as well. I'm ultimately trying to use them to create