Reputation: 11
I am a freshman in python, and I have a problem of how to draw a histogram in python.
First of all, I have ten intervals that are divided evenly according to the length of flowers' petal, from min to max. Thus I can separate flowers into ten intervals based on petals.
The number of flowers' kind is three, so I want to draw a histogram to describe the distribution of different kinds of flowers in different intervals(bins). And in the same bin, different flowers have different colors.
I know the hist
functions in Matplotlib, but I don't know how to use it to draw pictures like below.
The data are Lbins = [0.1 , 0.34, 0.58, 0.82, 1.06, 1.3 , 1.54, 1.78, 2.02, 2.26, 2.5 ]
and Data_bins
is an array of shape (number of flowers, 3).
Upvotes: 0
Views: 14794
Reputation: 4151
Here is an example of an histogram with multiple bars for each bins using hist
from Matplotlib:
import numpy as np
import matplotlib.pyplot as plt
length_of_flowers = np.random.randn(100, 3)
Lbins = [0.1 , 0.34, 0.58, 0.82, 1.06, 1.3 , 1.54, 1.78, 2.02, 2.26, 2.5 ]
# Lbins could also simply the number of wanted bins
colors = ['red','yellow', 'blue']
labels = ['red flowers', 'yellow flowers', 'blue flowers']
plt.hist(length_of_flowers, Lbins,
histtype='bar',
stacked=False,
fill=True,
label=labels,
alpha=0.8, # opacity of the bars
color=colors,
edgecolor = "k")
# plt.xticks(Lbins) # to set the ticks according to the bins
plt.xlabel('flower length'); plt.ylabel('count');
plt.legend();
plt.show()
which gives:
Edit: Solution for pre-binned data inspired from this matplotlib demo. The position of each bar is custom computed. I slightly modified the data by replacing zero values to verify correct alignment.
import numpy as np
import matplotlib.pyplot as plt
binned_data = np.array([[41., 3., 3.], [ 8., 3., 3.], [ 1., 2., 2.], [ 2., 7., 3.],
[ 0., 20., 0.], [ 1., 21., 1.], [ 1., 2., 4.], [ 3., 4., 23.],
[ 0., 0., 9.], [ 3., 1., 14.]]).T
# The shape of the data array have to be:
# (number of categories x number of bins)
print(binned_data.shape) # >> (3, 10)
x_positions = np.array([0.1, 0.34, 0.58, 0.82, 1.06, 1.3, 1.54, 1.78, 2.02, 2.26])
number_of_groups = binned_data.shape[0]
fill_factor = .8 # ratio of the groups width
# relatively to the available space between ticks
bar_width = np.diff(x_positions).min()/number_of_groups * fill_factor
colors = ['red','yellow', 'blue']
labels = ['red flowers', 'yellow flowers', 'blue flowers']
for i, groupdata in enumerate(binned_data):
bar_positions = x_positions - number_of_groups*bar_width/2 + (i + 0.5)*bar_width
plt.bar(bar_positions, groupdata, bar_width,
align='center',
linewidth=1, edgecolor='k',
color=colors[i], alpha=0.7,
label=labels[i])
plt.xticks(x_positions);
plt.legend(); plt.xlabel('flower length'); plt.ylabel('count');
which gives:
Upvotes: 2