Stacked bar charts using python matplotlib for positive and negative values

Question

I am trying to plot a stacked bar chart with python Matplotlib and I have positive and negative values that I want to draw. I have had a look at other posts talking about how to plot stacked bar charts with positive and negative values, but none of them has done it using Matplotlib so I couldn't find my solution. Stacked bar chart with negative JSON data d3.js stacked bar chart with positive and negative values Highcharts vertical stacked bar chart with negative values, is it possible?

I have used this code to plot stacked bar chart in python with matplotlib:

import numpy as np
import matplotlib.pyplot as plt
ind = np.arange(3)
a = np.array([4,-6,9])
b = np.array([2,7,1])
c = np.array([3,3,1])
d = np.array([4,0,-3])
p1 = plt.bar(ind, a, 1, color='g')
p2 = plt.bar(ind, b, 1, color='y',bottom=sum([a])) 
p3 = plt.bar(ind, c, 1, color='b', bottom=sum([a, b]))
p4 = plt.bar(ind, d, 1, color='c', bottom=sum([a, b, c]))
plt.show()

The above code gives the following graph:

This is not giving me correct results. Can someone tell me how I can code it whether using additional if else statement or any other way that would plot both negative and positive values in stacked bar charts.

I am expecting to get the results as in the following chart: expected chart

Now the first column is plotted correctly as it has all the positive values, but when python works on the second column, it gets all mixed up due to negative values. How can I get it to take the positive bottom when looking at positive values and negative bottom when plotting additional negative values?

Answer 1

The bottom keyword in ax.bar or plt.bar allows to set the lower bound of each bar disc precisely. We apply a 0-neg bottom to negative values, and a 0-pos bottom to positive values.

This code example creates the desired plot:

import numpy as np
import matplotlib.pyplot as plt

# Juwairia's data:     
a = [4,-6,9]
b = [2,7,1]
c = [3,3,1]
d = [4,0,-3]
data = np.array([a, b, c, d])

data_shape = np.shape(data)

# Take negative and positive data apart and cumulate
def get_cumulated_array(data, **kwargs):
    cum = data.clip(**kwargs)
    cum = np.cumsum(cum, axis=0)
    d = np.zeros(np.shape(data))
    d[1:] = cum[:-1]
    return d  

cumulated_data = get_cumulated_array(data, min=0)
cumulated_data_neg = get_cumulated_array(data, max=0)

# Re-merge negative and positive data.
row_mask = (data<0)
cumulated_data[row_mask] = cumulated_data_neg[row_mask]
data_stack = cumulated_data

cols = ["g", "y", "b", "c"]

fig = plt.figure()
ax = plt.subplot(111)

for i in np.arange(0, data_shape[0]):
    ax.bar(np.arange(data_shape[1]), data[i], bottom=data_stack[i], color=cols[i],)

plt.show()

This is the resulting plot: