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Plotting Histogram in Python using Matplotlib

  • Last Updated : 29 Jul, 2021

A histogram is basically used to represent data provided in a form of some groups.It is accurate method for the graphical representation of numerical data distribution.It is a type of bar plot where X-axis represents the bin ranges while Y-axis gives information about frequency.

Creating a Histogram

To create a histogram the first step is to create bin of the ranges, then distribute the whole range of the values into a series of intervals, and count the values which fall into each of the intervals.Bins are clearly identified as consecutive, non-overlapping intervals of variables.The matplotlib.pyplot.hist() function is used to compute and create histogram of x. 

The following table shows the parameters accepted by matplotlib.pyplot.hist() function : 

Attributeparameter
xarray or sequence of array
binsoptional parameter contains integer or sequence or strings
densityoptional parameter contains boolean values
rangeoptional parameter represents upper and lower range of bins
histtypeoptional parameter used to create type of histogram [bar, barstacked, step, stepfilled], default is “bar”
alignoptional parameter controls the plotting of histogram [left, right, mid]
weightsoptional parameter contains array of weights having same dimensions as x
bottomlocation of the basline of each bin
rwidthoptional parameter which is relative width of the bars with respect to bin width
coloroptional parameter used to set color or sequence of color specs
labeloptional parameter string or sequence of string to match with multiple datasets
logoptional parameter used to set histogram axis on log scale

Let’s create a basic histogram of some random values. Below code creates a simple histogram of some random values:  

Python3






from matplotlib import pyplot as plt
import numpy as np
 
 
# Creating dataset
a = np.array([22, 87, 5, 43, 56,
              73, 55, 54, 11,
              20, 51, 5, 79, 31,
              27])
 
# Creating histogram
fig, ax = plt.subplots(figsize =(10, 7))
ax.hist(a, bins = [0, 25, 50, 75, 100])
 
# Show plot
plt.show()

Output : 

Customization of Histogram

Matplotlib provides a range of different methods to customize histogram. 
matplotlib.pyplot.hist() function itself provides many attributes with the help of which we can modify a histogram.The hist() function provide a patches object which gives access to the properties of the created objects, using this we can modify the plot according to our will.

Example 1:  

Python3




import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colors
from matplotlib.ticker import PercentFormatter
 
# Creating dataset
np.random.seed(23685752)
N_points = 10000
n_bins = 20
 
# Creating distribution
x = np.random.randn(N_points)
y = .8 ** x + np.random.randn(10000) + 25
 
# Creating histogram
fig, axs = plt.subplots(1, 1,
                        figsize =(10, 7),
                        tight_layout = True)
 
axs.hist(x, bins = n_bins)
 
# Show plot
plt.show()

Output : 

Example 2: The code below modifies the above histogram for a better view and accurate readings. 

Python3




import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colors
from matplotlib.ticker import PercentFormatter
 
 
# Creating dataset
np.random.seed(23685752)
N_points = 10000
n_bins = 20
 
# Creating distribution
x = np.random.randn(N_points)
y = .8 ** x + np.random.randn(10000) + 25
legend = ['distribution']
 
# Creating histogram
fig, axs = plt.subplots(1, 1,
                        figsize =(10, 7),
                        tight_layout = True)
 
 
# Remove axes splines
for s in ['top', 'bottom', 'left', 'right']:
    axs.spines[s].set_visible(False)
 
# Remove x, y ticks
axs.xaxis.set_ticks_position('none')
axs.yaxis.set_ticks_position('none')
   
# Add padding between axes and labels
axs.xaxis.set_tick_params(pad = 5)
axs.yaxis.set_tick_params(pad = 10)
 
# Add x, y gridlines
axs.grid(b = True, color ='grey',
        linestyle ='-.', linewidth = 0.5,
        alpha = 0.6)
 
# Add Text watermark
fig.text(0.9, 0.15, 'Jeeteshgavande30',
         fontsize = 12,
         color ='red',
         ha ='right',
         va ='bottom',
         alpha = 0.7)
 
# Creating histogram
N, bins, patches = axs.hist(x, bins = n_bins)
 
# Setting color
fracs = ((N**(1 / 5)) / N.max())
norm = colors.Normalize(fracs.min(), fracs.max())
 
for thisfrac, thispatch in zip(fracs, patches):
    color = plt.cm.viridis(norm(thisfrac))
    thispatch.set_facecolor(color)
 
# Adding extra features   
plt.xlabel("X-axis")
plt.ylabel("y-axis")
plt.legend(legend)
plt.title('Customized histogram')
 
# Show plot
plt.show()

Output : 

 

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