!date
import numpy as np, pandas as pd, matplotlib.pyplot as plt, mpld3, seaborn as sns
%matplotlib inline

Fri Dec 19 10:35:23 PST 2014

The Horizontal Bar Chart¶

A figure that I often want is the "horizontal bar chart", a way to represent one or two columns of a table visually for deep inspection. Each row of the table often has a descriptive name, and there are comparisons within columns and between rows, as well as within rows between columns that I think might be interesting.

Here is how it might be done with mpld3, and how it might be done better:

# get some of my favorite data
df = pd.read_csv('http://ghdx.healthdata.org/sites/default/files/'
                 'record-attached-files/IHME_PHMRC_VA_DATA_ADULT_Y2013M09D11_0.csv',
                low_memory=False)
df.head()

# make a summary table that would like to inspect
df['Field Site'] = df.site
df['Underlying Cause'] = df.gs_text34

g = df.groupby('Field Site')['Underlying Cause']

t = g.value_counts().unstack(0)
t = t.fillna(0)

t['Mean'] = t.mean(axis=1)
t['Max'] = t.max(axis=1)
t['Min'] = t.min(axis=1)

t

The Comparison¶

For demonstration purposes, I plan to compare the distribution of data collected on Pemba Island to the average across all study sites. This is a health-metrics-specific example, but let's not get into the details.

t = t.sort('Mean')  # sort the table in a meaningful way

fig = plt.figure(figsize=(12,16))  # make a nice, big figure for the plot

y = np.arange(len(t.index))  # select points on the y-axis for each bar

# do actual plotting
plt.barh(y+.05, t.Pemba, height=.45, color=sns.color_palette()[0], label='Pemba')
plt.barh( y+.5, t.Mean, height=.45, xerr=[t.Mean - t.Min, t.Max - t.Mean],  # annoying format for error bars
         color=sns.color_palette()[1], ecolor='k', 
         label='Cross-site Mean')

plt.axis(xmin=0)  # silly error-bars go below zero, but don't show that

plt.legend(loc=(.5,.1))  # legend uses label values from calls to plt.barh
plt.yticks(y+.5, t.index)  # label each tick on the y-axis with the corresponding cause
plt.subplots_adjust(left=.5)  # make sure there is enough room to read the tick labels

plt.xlabel('Verbal Autopsies Collected')  # label the plot so that it is easy to remember what it is in the future
pass  # do something with no output to keep display clean

# make the figure interactive
mpld3.display(fig)

Wishlist for interactive¶

A pan-and-zoom tool that mantains the minimum x-value of 0 on the x-axis
y-ticks that hide automatically when they are squeezed too close together
Automatic rescaling of the x-axis to maximize the dynamic range of the data

None of this is that hard... what would you want?

	site	module	gs_code34	gs_text34	va34	gs_code46	gs_text46	va46	gs_code55	gs_text55	...	newid
0	Mexico	Adult	K71	Cirrhosis	6	K71	Cirrhosis	8	K71	Cirrhosis	...	1
1	AP	Adult	G40	Epilepsy	12	G40	Epilepsy	16	G40	Epilepsy	...	2
2	AP	Adult	J12	Pneumonia	26	J12	Pneumonia	37	J12	Pneumonia	...	3
3	Mexico	Adult	J33	COPD	8	J33	COPD	10	J33	COPD	...	4
4	UP	Adult	I21	Acute Myocardial Infarction	17	I21	Acute Myocardial Infarction	23	I21	Acute Myocardial Infarction	...	5

Field Site	AP	Bohol	Dar	Mexico	Pemba	UP	Mean	Max	Min
AIDS	136	0	203	120	0	43	83.666667	203	0
Acute Myocardial Infarction	101	116	3	76	0	104	66.666667	116	0
Asthma	21	6	12	1	6	1	7.833333	21	1
Bite of Venomous Animal	31	1	0	1	0	33	11.000000	33	0
Breast Cancer	3	28	90	69	0	5	32.500000	90	0
COPD	25	4	2	63	0	77	28.500000	77	0
Cervical Cancer	3	9	108	31	0	4	25.833333	108	0
Cirrhosis	51	39	27	133	0	63	52.166667	133	0
Colorectal Cancer	7	24	33	35	0	0	16.500000	35	0
Diabetes	88	77	59	105	38	47	69.000000	105	38
Diarrhea/Dysentery	77	27	37	18	21	48	38.000000	77	18
Drowning	32	4	9	0	28	33	17.666667	33	0
Epilepsy	30	5	6	2	0	5	8.000000	30	0
Esophageal Cancer	1	5	34	0	0	0	6.666667	34	0
Falls	32	38	11	36	26	30	28.833333	38	11
Fires	35	7	25	18	6	31	20.333333	35	6
Homicide	31	35	30	37	3	31	27.833333	37	3
Leukemia/Lymphomas	7	15	59	72	0	3	26.000000	72	0
Lung Cancer	5	23	11	59	0	8	17.666667	59	0
Malaria	29	0	34	0	2	35	16.666667	35	0
Maternal	71	41	135	39	46	136	78.000000	136	39
Other Cardiovascular Diseases	76	78	112	81	0	69	69.333333	112	0
Other Infectious Diseases	34	51	37	37	3	101	43.833333	101	3
Other Injuries	31	7	23	2	3	37	17.166667	37	2
Other Non-communicable Diseases	90	125	171	122	54	37	99.833333	171	37
Pneumonia	55	142	95	102	41	105	90.000000	142	41
Poisonings	30	3	8	9	1	35	14.333333	35	1
Prostate Cancer	1	4	32	11	0	0	8.000000	32	0
Renal Failure	102	68	49	92	0	105	69.333333	105	0
Road Traffic	39	55	31	34	11	32	33.666667	55	11
Stomach Cancer	5	5	21	31	0	0	10.333333	31	0
Stroke	125	179	103	122	0	101	105.000000	179	0
Suicide	49	16	13	11	2	33	20.666667	49	2
TB	101	22	103	17	6	27	46.000000	103	6