How to have clusters of stacked bars with python (Pandas)

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Question :

How to have clusters of stacked bars with python (Pandas)

So here is how my data set looks like :

In [1]: df1=pd.DataFrame(np.random.rand(4,2),index=["A","B","C","D"],columns=["I","J"])

In [2]: df2=pd.DataFrame(np.random.rand(4,2),index=["A","B","C","D"],columns=["I","J"])

In [3]: df1
          I         J
A  0.675616  0.177597
B  0.675693  0.598682
C  0.631376  0.598966
D  0.229858  0.378817

In [4]: df2
          I         J
A  0.939620  0.984616
B  0.314818  0.456252
C  0.630907  0.656341
D  0.020994  0.538303

I want to have stacked bar plot for each dataframe but since they have same index, I’d like to have 2 stacked bars per index.

I’ve tried to plot both on the same axes :

In [5]: ax = df1.plot(kind="bar", stacked=True)

In [5]: ax2 = df2.plot(kind="bar", stacked=True, ax = ax)

But it overlaps.

Then I tried to concat the two dataset first :

pd.concat(dict(df1 = df1, df2 = df2),axis = 1).plot(kind="bar", stacked=True)

but here everything is stacked

My best try is :

 pd.concat(dict(df1 = df1, df2 = df2),axis = 0).plot(kind="bar", stacked=True)

Which gives :

enter image description here

This is basically what I want, except that I want the bar ordered as

(df1,A) (df2,A) (df1,B) (df2,B) etc…

I guess there is a trick but I can’t found it !

After @bgschiller’s answer I got this :

enter image description here

Which is almost what I want. I would like the bar to be clustered by index, in order to have something visually clear.

Bonus : Having the x-label not redundant, something like :

df1 df2    df1 df2
_______    _______ ...
   A          B

Thanks for helping.

Asked By: jrjc


Answer #1:

I eventually found a trick (edit: see below for using seaborn and longform dataframe):

Solution with pandas and matplotlib

Here it is with a more complete example :

import pandas as pd
import as cm
import numpy as np
import matplotlib.pyplot as plt

def plot_clustered_stacked(dfall, labels=None, title="multiple stacked bar plot",  H="/", **kwargs):
    """Given a list of dataframes, with identical columns and index, create a clustered stacked bar plot. 
labels is a list of the names of the dataframe, used for the legend
title is a string for the title of the plot
H is the hatch used for identification of the different dataframe"""

    n_df = len(dfall)
    n_col = len(dfall[0].columns) 
    n_ind = len(dfall[0].index)
    axe = plt.subplot(111)

    for df in dfall : # for each data frame
        axe = df.plot(kind="bar",
                      **kwargs)  # make bar plots

    h,l = axe.get_legend_handles_labels() # get the handles we want to modify
    for i in range(0, n_df * n_col, n_col): # len(h) = n_col * n_df
        for j, pa in enumerate(h[i:i+n_col]):
            for rect in pa.patches: # for each index
                rect.set_x(rect.get_x() + 1 / float(n_df + 1) * i / float(n_col))
                rect.set_hatch(H * int(i / n_col)) #edited part     
                rect.set_width(1 / float(n_df + 1))

    axe.set_xticks((np.arange(0, 2 * n_ind, 2) + 1 / float(n_df + 1)) / 2.)
    axe.set_xticklabels(df.index, rotation = 0)

    # Add invisible data to add another legend
    for i in range(n_df):
        n.append(, 0, color="gray", hatch=H * i))

    l1 = axe.legend(h[:n_col], l[:n_col], loc=[1.01, 0.5])
    if labels is not None:
        l2 = plt.legend(n, labels, loc=[1.01, 0.1]) 
    return axe

# create fake dataframes
df1 = pd.DataFrame(np.random.rand(4, 5),
                   index=["A", "B", "C", "D"],
                   columns=["I", "J", "K", "L", "M"])
df2 = pd.DataFrame(np.random.rand(4, 5),
                   index=["A", "B", "C", "D"],
                   columns=["I", "J", "K", "L", "M"])
df3 = pd.DataFrame(np.random.rand(4, 5),
                   index=["A", "B", "C", "D"], 
                   columns=["I", "J", "K", "L", "M"])

# Then, just call :
plot_clustered_stacked([df1, df2, df3],["df1", "df2", "df3"])

And it gives that :

multiple stacked bar plot

You can change the colors of the bar by passing a cmap argument:

plot_clustered_stacked([df1, df2, df3],
                       ["df1", "df2", "df3"],

Solution with seaborn:

Given the same df1, df2, df3, below, I convert them in a long form:

df1["Name"] = "df1"
df2["Name"] = "df2"
df3["Name"] = "df3"
dfall = pd.concat([pd.melt(i.reset_index(),
                           id_vars=["Name", "index"]) # transform in tidy format each df
                   for i in [df1, df2, df3]],

The problem with seaborn is that it doesn’t stack bars natively, so the trick is to plot the cumulative sum of each bar on top of each other:

dfall.set_index(["Name", "index", "variable"], inplace=1)
dfall["vcs"] = dfall.groupby(level=["Name", "index"]).cumsum()

>>> dfall.head(6)
  Name index variable     value       vcs
0  df1     A        I  0.717286  0.717286
1  df1     B        I  0.236867  0.236867
2  df1     C        I  0.952557  0.952557
3  df1     D        I  0.487995  0.487995
4  df1     A        J  0.174489  0.891775
5  df1     B        J  0.332001  0.568868

Then loop over each group of variable and plot the cumulative sum:

c = ["blue", "purple", "red", "green", "pink"]
for i, g in enumerate(dfall.groupby("variable")):
    ax = sns.barplot(data=g[1],
                     zorder=-i, # so first bars stay on top
ax.legend_.remove() # remove the redundant legends 

multiple stack bar plot seaborn

It lacks the legend that can be added easily I think. The problem is that instead of hatches (which can be added easily) to differentiate the dataframes we have a gradient of lightness, and it’s a bit too light for the first one, and I don’t really know how to change that without changing each rectangle one by one (as in the first solution).

Tell me if you don’t understand something in the code.

Feel free to re-use this code which is under CC0.

Answered By: jrjc

Answer #2:

This is a great start but I think the colors could be modified a bit for clarity. Also be careful about importing every argument in Altair as this may cause collisions with existing objects in your namespace. Here is some reconfigured code to display the correct color display when stacking the values:

Altair Clustered Column Chart

Import packages

import pandas as pd
import numpy as np
import altair as alt

Generate some random data


def prep_df(df, name):
    df = df.stack().reset_index()
    df.columns = ['c1', 'c2', 'values']
    df['DF'] = name
    return df

df1 = prep_df(df1, 'DF1')
df2 = prep_df(df2, 'DF2')
df3 = prep_df(df3, 'DF3')

df = pd.concat([df1, df2, df3])

Plot data with Altair


    # tell Altair which field to group columns on
    x=alt.X('c2:N', title=None),

    # tell Altair which field to use as Y values and how to calculate

    # tell Altair which field to use to use as the set of columns to be  represented in each group
    column=alt.Column('c1:N', title=None),

    # tell Altair which field to use for color segmentation 
                # make it look pretty with an enjoyable color pallet
                range=['#96ceb4', '#ffcc5c','#ff6f69'],
        # remove grid lines around column clusters
Answered By: jrjc

Answer #3:

I have managed to do the same using pandas and matplotlib subplots with basic commands.

Here’s an example:

fig, axes = plt.subplots(nrows=1, ncols=3)

ax_position = 0
for concept in df.index.get_level_values('concept').unique():
idx = pd.IndexSlice
subset = df.loc[idx[[concept], :],
['cmp_tr_neg_p_wrk', 'exp_tr_pos_p_wrk',
'cmp_p_spot', 'exp_p_spot']]
subset = subset.groupby(
subset = subset / 4 # quarter hours
subset = subset / 100 # installed capacity
ax = subset.plot(kind="bar", stacked=True, colormap="Blues",
ax.set_title("Concept "" + concept + """""", fontsize=30, alpha=1.0)

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