Pandas - Print Multiple Indexes

Question

I have some data where I manipulated a data framework using the following code:

import pandas as pd import numpy as np data = pd.DataFrame([[0,0,0,3,6,5,6,1],[1,1,1,3,4,5,2,0],[2,1,0,3,6,5,6,1],[3,0,0,2,9,4,2,1],[4,0,1,3,4,8,1,1],[5,1,1,3,3,5,9,1],[6,1,0,3,3,5,6,1],[7,0,1,3,4,8,9,1]], columns=["id", "sex", "split", "group0Low", "group0High", "group1Low", "group1High", "trim"]) data #remove all where trim == 0 trimmed = data[(data.trim == 1)] trimmed #create df with columns to be split columns = ['group0Low', 'group0High', 'group1Low', 'group1High'] to_split = trimmed[columns] to_split level_group = np.where(to_split.columns.str.contains('0'), 0, 1) # output: array([0, 0, 1, 1]) level_low_high = np.where(to_split.columns.str.contains('Low'), 'low', 'high') # output: array(['low', 'high', 'low', 'high'], dtype='<U4') multi_level_columns = pd.MultiIndex.from_arrays([level_group, level_low_high], names=['group', 'val']) to_split.columns = multi_level_columns to_split.stack(level='group') sex = trimmed['sex'] split = trimmed['split'] horizontalStack = pd.concat([sex, split, to_split], axis=1) horizontalStack finalData = horizontalStack.groupby(['split', 'sex', 'group']) finalData.mean() 

My question is how to create averages using ggplot or seaborn, so for each split level I get a graph that looks like this:

At the bottom of the code, you can see that I tried to split the group factor so that I could split the columns, but this led to an error (KeyError: 'group'), and I think this is due to the path I used multi-indexing

Answer 1

I would use a chart of factors from the seabed.

Let's say you have the following data:

 import numpy as np import pandas import seaborn seaborn.set(style='ticks') np.random.seed(0) groups = ('Group 1', 'Group 2') sexes = ('Male', 'Female') means = ('Low', 'High') index = pandas.MultiIndex.from_product( [groups, sexes, means], names=['Group', 'Sex', 'Mean'] ) values = np.random.randint(low=20, high=100, size=len(index)) data = pandas.DataFrame(data={'val': values}, index=index).reset_index() print(data) Group Sex Mean val 0 Group 1 Male Low 64 1 Group 1 Male High 67 2 Group 1 Female Low 84 3 Group 1 Female High 87 4 Group 2 Male Low 87 5 Group 2 Male High 29 6 Group 2 Female Low 41 7 Group 2 Female High 56 

Then you can create a factor diagram with a single command + plus an extra line to remove redundant x-labels (for your data):

 fg = seaborn.factorplot(x='Group', y='val', hue='Mean', col='Sex', data=data, kind='bar') fg.set_xlabels('') 

What gives me:

Answer 2

In a related question, I found an alternative solution from @Stein that encodes multiindex levels as different labels. Here's what it looks like for your example:

 import pandas as pd import matplotlib.pyplot as plt from itertools import groupby import numpy as np %matplotlib inline groups = ('Group 1', 'Group 2') sexes = ('Male', 'Female') means = ('Low', 'High') index = pd.MultiIndex.from_product( [groups, sexes, means], names=['Group', 'Sex', 'Mean'] ) values = np.random.randint(low=20, high=100, size=len(index)) data = pd.DataFrame(data={'val': values}, index=index) # unstack last level to plot two separate columns data = data.unstack(level=-1) def add_line(ax, xpos, ypos): line = plt.Line2D([xpos, xpos], [ypos + .1, ypos], transform=ax.transAxes, color='gray') line.set_clip_on(False) ax.add_line(line) def label_len(my_index,level): labels = my_index.get_level_values(level) return [(k, sum(1 for i in g)) for k,g in groupby(labels)] def label_group_bar_table(ax, df): ypos = -.1 scale = 1./df.index.size for level in range(df.index.nlevels)[::-1]: pos = 0 for label, rpos in label_len(df.index,level): lxpos = (pos + .5 * rpos)*scale ax.text(lxpos, ypos, label, ha='center', transform=ax.transAxes) add_line(ax, pos*scale, ypos) pos += rpos add_line(ax, pos*scale , ypos) ypos -= .1 ax = df['val'].plot(kind='bar') #Below 2 lines remove default labels ax.set_xticklabels('') ax.set_xlabel('') label_group_bar_table(ax, df) 

This gives: