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

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 pdimport matplotlib.cm as cmimport numpy as npimport matplotlib.pyplot as pltdef 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 legendtitle is a string for the title of the plotH 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",                      linewidth=0,                      stacked=True,                      ax=axe,                      legend=False,                      grid=False,                      **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)    axe.set_title(title)    # Add invisible data to add another legend    n=[]            for i in range(n_df):        n.append(axe.bar(0, 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])     axe.add_artist(l1)    return axe# create fake dataframesdf1 = 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 :

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

plot_clustered_stacked([df1, df2, df3],                       ["df1", "df2", "df3"],                       cmap=plt.cm.viridis)

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]],                   ignore_index=True)

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.reset_index(inplace=True) >>> dfall.head(6)  Name index variable     value       vcs0  df1     A        I  0.717286  0.7172861  df1     B        I  0.236867  0.2368672  df1     C        I  0.952557  0.9525573  df1     D        I  0.487995  0.4879954  df1     A        J  0.174489  0.8917755  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],                     x="index",                     y="vcs",                     hue="Name",                     color=c[i],                     zorder=-i, # so first bars stay on top                     edgecolor="k")ax.legend_.remove() # remove the redundant legends 

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.

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:

Import packages

import pandas as pdimport numpy as npimport altair as alt

Generate some random data

df1=pd.DataFrame(10*np.random.rand(4,3),index=["A","B","C","D"],columns=["I","J","K"])df2=pd.DataFrame(10*np.random.rand(4,3),index=["A","B","C","D"],columns=["I","J","K"])df3=pd.DataFrame(10*np.random.rand(4,3),index=["A","B","C","D"],columns=["I","J","K"])def prep_df(df, name):    df = df.stack().reset_index()    df.columns = ['c1', 'c2', 'values']    df['DF'] = name    return dfdf1 = prep_df(df1, 'DF1')df2 = prep_df(df2, 'DF2')df3 = prep_df(df3, 'DF3')df = pd.concat([df1, df2, df3])

Plot data with Altair

alt.Chart(df).mark_bar().encode(    # 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    y=alt.Y('sum(values):Q',        axis=alt.Axis(            grid=False,            title=None)),    # 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     color=alt.Color('DF:N',            scale=alt.Scale(                # make it look pretty with an enjoyable color pallet                range=['#96ceb4', '#ffcc5c','#ff6f69'],            ),        ))\    .configure_view(        # remove grid lines around column clusters        strokeOpacity=0        )

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 = 0for 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']]         print(subset.info())    subset = subset.groupby(        subset.index.get_level_values('datetime').year).sum()    subset = subset / 4  # quarter hours    subset = subset / 100  # installed capacity    ax = subset.plot(kind="bar", stacked=True, colormap="Blues",                     ax=axes[ax_position])    ax.set_title("Concept \"" + concept + "\"", fontsize=30, alpha=1.0)    ax.set_ylabel("Hours", fontsize=30),    ax.set_xlabel("Concept \"" + concept + "\"", fontsize=30, alpha=0.0),    ax.set_ylim(0, 9000)    ax.set_yticks(range(0, 9000, 1000))    ax.set_yticklabels(labels=range(0, 9000, 1000), rotation=0,                       minor=False, fontsize=28)    ax.set_xticklabels(labels=['2012', '2013', '2014'], rotation=0,                       minor=False, fontsize=28)    handles, labels = ax.get_legend_handles_labels()    ax.legend(['Market A', 'Market B',               'Market C', 'Market D'],              loc='upper right', fontsize=28)    ax_position += 1# look "three subplots"#plt.tight_layout(pad=0.0, w_pad=-8.0, h_pad=0.0)# look "one plot"plt.tight_layout(pad=0., w_pad=-16.5, h_pad=0.0)axes[1].set_ylabel("")axes[2].set_ylabel("")axes[1].set_yticklabels("")axes[2].set_yticklabels("")axes[0].legend().set_visible(False)axes[1].legend().set_visible(False)axes[2].legend(['Market A', 'Market B',                'Market C', 'Market D'],               loc='upper right', fontsize=28)

The dataframe structure of "subset" before grouping looks like this:

<class 'pandas.core.frame.DataFrame'>MultiIndex: 105216 entries, (D_REC, 2012-01-01 00:00:00) to (D_REC, 2014-12-31 23:45:00)Data columns (total 4 columns):cmp_tr_neg_p_wrk    105216 non-null float64exp_tr_pos_p_wrk    105216 non-null float64cmp_p_spot          105216 non-null float64exp_p_spot          105216 non-null float64dtypes: float64(4)memory usage: 4.0+ MB

and the plot like this:

It is formatted in the "ggplot" style with the following header:

import pandas as pdimport matplotlib.pyplot as pltimport matplotlibmatplotlib.style.use('ggplot')