Create a cumulative histogram in Matplotlib
The histogram is a graphical representation of data. We can represent any kind of numeric data in histogram format. In this article, We are going to see how to create a cumulative histogram in Matplotlib
Cumulative frequency: Cumulative frequency analysis is the analysis of the frequency of occurrence of values. It is the total of a frequency and all frequencies so far in a frequency distribution.
Example:
X contains [1,2,3,4,5] then the cumulative frequency for x is [1,3,6,10,15].
Explanation:
[1,1+2,1+2+3,1+2+3+4,1+2+3+4+5]
In Python, we can generate a histogram with dataframe.hist, and cumulative frequency stats.cumfreq() histogram.
Example 1:
Python3
# importing pyplot for getting graphimport matplotlib.pyplot as plt # importing numpy for getting arrayimport numpy as np # importing scientific pythonfrom scipy import stats # list of valuesx = [10, 40, 20, 10, 30, 10, 56, 45] res = stats.cumfreq(x, numbins=4, defaultreallimits=(1.5, 5)) # generating random valuesrng = np.random.RandomState(seed=12345) # normalizingsamples = stats.norm.rvs(size=1000, random_state=rng) res = stats.cumfreq(samples, numbins=25) x = res.lowerlimit + np.linspace(0, res.binsize*res.cumcount.size, res.cumcount.size) # specifying figure sizefig = plt.figure(figsize=(10, 4)) # adding sub plotsax1 = fig.add_subplot(1, 2, 1) # adding sub plotsax2 = fig.add_subplot(1, 2, 2) # getting histogram using hist functionax1.hist(samples, bins=25, color="green") # setting up the titleax1.set_title('Histogram') # cumulative graphax2.bar(x, res.cumcount, width=4, color="blue") # setting up the titleax2.set_title('Cumulative histogram') ax2.set_xlim([x.min(), x.max()]) # display hte figure(histogram)plt.show() |
Output:
Example 2:
Python3
# importing numpy for getting arrayimport numpy as np # importing scientific pythonfrom scipy import stats # list of valuesx = [10, 40, 20, 10, 30, 10, 56, 45] res = stats.cumfreq(x, numbins=4, defaultreallimits=(1.5, 5)) # generating random valuesrng = np.random.RandomState(seed=12345) # normalizingsamples = stats.norm.rvs(size=1000, random_state=rng) res = stats.cumfreq(samples, numbins=25) x = res.lowerlimit + np.linspace(0, res.binsize*res.cumcount.size, res.cumcount.size) fig = plt.figure(figsize=(10, 4)) ax1 = fig.add_subplot(1, 2, 1)ax2 = fig.add_subplot(1, 2, 2) ax1.hist(samples, bins=25, color="green") ax1.set_title('Histogram')ax2.bar(x, x, width=2, color="blue") ax2.set_title('Cumulative histogram')ax2.set_xlim([x.min(), x.max()]) plt.show() |
Output:
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