pandas / matplotlib: faceting bar plots

Question

I am making a series of bar plots of data with two categorical variables and one numeric. What i have is the below, but what I would love to do is to facet by one of the categorical variables as with facet_wrap in ggplot. I have a somewhat working example, but I get the wrong plot type (lines and not bars) and I do subsetting of the data in a loop--that can't be the best way.

## first try--plain vanilla
import pandas as pd
import numpy as np
N = 100

## generate toy data
ind = np.random.choice(['a','b','c'], N)
cty = np.random.choice(['x','y','z'], N)
jobs = np.random.randint(low=1,high=250,size=N)

## prep data frame
df_city = pd.DataFrame({'industry':ind,'city':cty,'jobs':jobs})
df_city_grouped = df_city.groupby(['city','industry']).jobs.sum().unstack()
df_city_grouped.plot(kind='bar',stacked=True,figsize=(9, 6))

This gives something like this:

  city industry  jobs
0    z        b   180
1    z        c   121
2    x        a    33
3    z        a   121
4    z        c   236

However, what i would like to see is something like this:

## R code
library(plyr)
df_city<-read.csv('/home/aksel/Downloads/mockcity.csv',sep='\t')

## summarize
df_city_grouped <- ddply(df_city, .(city,industry), summarise, jobstot = sum(jobs))

## plot
ggplot(df_city_grouped, aes(x=industry, y=jobstot)) +
  geom_bar(stat='identity') +
  facet_wrap(~city)

The closest I get with matplotlib is something like this:

cols =df_city.city.value_counts().shape[0]
fig, axes = plt.subplots(1, cols, figsize=(8, 8))

for x, city in enumerate(df_city.city.value_counts().index.values):
    data = df_city[(df_city['city'] == city)]
    data = data.groupby(['industry']).jobs.sum()
    axes[x].plot(data)

So two questions:

1. Can I do bar plots (they plot lines as shown here) using the AxesSubplot object and end up with something along the lines of the facet_wrap example from ggplot example;
2. In loops generating charts such as this attempt, I subset the data in each. I can't imagine that is the 'proper' way to do this type of faceting?

Answer 1

Second example here: http://pandas-docs.github.io/pandas-docs-travis/visualization.html#bar-plots

Anyway, you can always do that by hand, as you did yourself.

EDIT: BTW, you can always use rpy2 in python, so you can do all the same things as in R.

Also, have a look at this: https://pandas.pydata.org/pandas-docs/version/0.14.1/rplot.html I am not sure, but it should be helpful for creating plots over many panels, though might require further reading.

Answer 2

@tcasell suggested the bar call in the loop. Here is a working, if not elegant, example.

## second try--facet by county

N = 100
industry = ['a','b','c']
city = ['x','y','z']
ind = np.random.choice(industry, N)
cty = np.random.choice(city, N)
jobs = np.random.randint(low=1,high=250,size=N)
df_city =pd.DataFrame({'industry':ind,'city':cty,'jobs':jobs})

## how many panels do we need?
cols =df_city.city.value_counts().shape[0]
fig, axes = plt.subplots(1, cols, figsize=(8, 8))

for x, city in enumerate(df_city.city.value_counts().index.values):
    data = df_city[(df_city['city'] == city)]
    data = data.groupby(['industry']).jobs.sum()
    print (data)
    print type(data.index)
    left=  [k[0] for k in enumerate(data)]
    right=  [k[1] for k in enumerate(data)]

    axes[x].bar(left,right,label="%s" % (city))
    axes[x].set_xticks(left, minor=False)
    axes[x].set_xticklabels(data.index.values)

    axes[x].legend(loc='best')
    axes[x].grid(True)
    fig.suptitle('Employment By Industry By City', fontsize=20)