How do I normalize a histogram using Matplotlib?

Question

I am trying to generate a histogram using matplotlib. I am reading data from the following file: https://github.com/meghnasubramani/Files/blob/master/class_id.txt

My intent is to generate a histogram with the following bins: 1, 2-5, 5-100, 100-200, 200-1000, >1000.

When I generate the graph it doesn't look nice. I would like to normalize the y axis to (frequency of occurrence in a bin/total items). I tried using the density parameter but whenever I try that my graph ends up completely blank. How do I go about doing this.

How do I get the width's of the bars to be the same, even though the bin ranges are varied?

Is it also possible to specify the ticks on the histogram? I want to have the ticks correspond to the bin ranges.

Graph

import matplotlib.pyplot as plt

FILE_NAME = 'class_id.txt'
class_id = [int(line.rstrip('\n')) for line in open(FILE_NAME)]
num_bins = [1, 2, 5, 100, 200, 1000, max(class_id)]
x = plt.hist(class_id, bins=num_bins, histtype='bar', align='mid', rwidth=0.5, color='b')
print (x)
plt.legend()
plt.xlabel('Items')
plt.ylabel('Frequency')

Wow, those are a lot of questions in a single question. I also think they should all already have an answer somewhere. To bring you on the right track here: (a) to show relative frequency you would need to weight your values by the total number of items using the weights argument. (b) If you want same width bars, you would calculate the histogram first, then plot a plt.bar plot. (c) Ticks can be specified via plt.xticks. — ImportanceOfBeingErnest

Marsu Marsu · Accepted Answer · 2018-11-18T13:01:21

As suggested by importanceofbeingernest, we can use bar charts to plot categorical data and we need to categorize values in bins, for ex with pandas:

import matplotlib.pyplot as plt
import pandas

FILE_NAME = 'class_id.txt'
class_id_file = [int(line.rstrip('\n')) for line in open(FILE_NAME)]

num_bins = [0, 2, 5, 100, 200, 1000, max(class_id_file)]
categories = pandas.cut(class_id_file, num_bins)
df = pandas.DataFrame(class_id_file)
dfg = df.groupby(categories).count()
bins_labels = ["1-2", "2-5", "5-100", "100-200", "200-1000", ">1000"]

plt.bar(range(len(categories.categories)), dfg[0]/len(class_id_file), tick_label=bins_labels)
#plt.bar(range(len(categories.categories)), dfg[0]/len(class_id_file), tick_label=categories.categories)

plt.xlabel('Items')
plt.ylabel('Frequency')

Not what you asked for, but you could also stay with histogram and choose logarithm scale to improve readability:

plt.xscale('log')

How do I normalize a histogram using Matplotlib?

1 Answers