pandas - plotting integration with matplotlib

Question

Given this data frame:

xlabel = list('xxxxxxyyyyyyzzzzzz')
fill= list('abc'*6)
val = np.random.rand(18)
df = pd.DataFrame({ 'xlabel':xlabel, 'fill':fill, 'val':val})

This is what I'm aiming at: http://matplotlib.org/mpl_examples/pylab_examples/barchart_demo.png

Applied to my example, Group would be x, y and z, Gender would be a, b and c, and Scores would be val.

I'm aware that in pandas plotting integration with matplotlib is still work in progress, so is it possible to do it directly in matplotlib?

note sure what you mean by work in progress: pandas.pydata.org/pandas-docs/stable/…, saying that not sure how to do the quantile ticks (someone will though) :) — Andy Hayden
– Andy Hayden, Commented Sep 3, 2013 at 18:24
@Andy Hayden: I was under the impression that not all matplotlib functionalities were working in pandas yet, given the Note at the top of the page you mentioned — HappyPy
– HappyPy, Commented Sep 3, 2013 at 18:46
@Andy Hayden: I posted an answer including the error bars, although I'm sure there's a better way to do it. — HappyPy
– HappyPy, Commented Sep 5, 2013 at 18:01

Viktor Kerkez · Accepted Answer · 2013-09-05 21:22:01Z

2

Is this what you want?

df.groupby(['fill', 'xlabel']).mean().unstack().plot(kind='bar')

or

df.pivot_table(rows='fill', cols='xlabel', values='val').plot(kind='bar')

You can brake it apart and fiddle with the labels and columns and title, but I think this basically gives you the plot you wanted.

For the error bars currently you'll have to go to the mpl directly.

mean_df = df.pivot_table(rows='fill', cols='xlabel',
                         values='val', aggfunc='mean')
err_df = df.pivot_table(rows='fill', cols='xlabel',
                        values='val', aggfunc='std')

rows = len(mean_df)
cols = len(mean_df.columns)
ind = np.arange(rows)
width = 0.8 / cols
colors = 'grb'

fig, ax = plt.subplots()
for i, col in enumerate(mean_df.columns):
    ax.bar(ind + i * width, mean_df[col], width=width,
           color=colors[i], yerr=err_df[col], label=col)

ax.set_xticks(ind + cols / 2.0 * width)
ax.set_xticklabels(mean_df.index)
ax.legend()

But there will be an enhancement, probably in the 0.13: issue 3796

edited Sep 5, 2013 at 21:22

answered Sep 3, 2013 at 18:26

Viktor Kerkez

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2 Comments

HappyPy Over a year ago

Is it possible to extend your code by including the error bars? I've posted an answer with possible solution, but there must be a better way.

Viktor Kerkez Over a year ago

@user2635863 For the error bars you'll have to use the matplotlib library directly. I updated the answer.

HappyPy · Accepted Answer · 2013-09-05 17:59:47Z

This was the only solution I found for displaying the error bars:

means = df.groupby(['fill', 'xlabel']).mean().unstack()
x_mean,y_mean,z_mean = means.val.x, means.val.y,means.val.z

sems = df.groupby(['fill','xlabel']).aggregate(stats.sem).unstack()
x_sem,y_sem,z_sem = sems.val.x, sems.val.y,sems.val.z

ind = np.array([0,1.5,3])
fig, ax = plt.subplots()
width = 0.35
bar_x = ax.bar(ind, x_mean, width, color='r', yerr=x_sem, ecolor='r')
bar_y = ax.bar(ind+width, y_mean, width, color='g', yerr=y_sem, ecolor='g')
bar_z = ax.bar(ind+width*2, z_mean, width, color='b', yerr=z_sem, ecolor='b')

ax.legend((bar_x[0], bar_y[0], bar_z[0]), ('X','Y','Z'))

I'd be happy to see a neater approach to tackle the problem though, possibly as an extension of Viktor Kerkez answer.

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