histogram2d-example.ipynb

Kernel: Unknown Kernel

Example of 2D-Histograms

For this example, we are going to make a 2D histogram of a randomly generated data of some human population, comprising age and height. For this we are going to use the random module of the library NumPy.

import numpy as np
%pylab inline
import matplotlib.pyplot as plt

Populating the interactive namespace from numpy and matplotlib

Let's assume a set of 50 entries with 3 values each one. The first one is an age, the second one is a height and the thrid one is the number of people who meet the first two conditions.

#Number of entries
Ndata = 50

#Generating ages [from 20 to 80 years old]
ages = np.random.randint( 20, 50, Ndata )
#Generating heights [from 150cm to 190cm]
heights = np.random.randint( 150, 190, Ndata )
#Number of people in each category [from 10 people to 100 people]
people = np.random.randint( 10, 100, Ndata )

The objetive now is to make a 2D histogram of age vs height. The function histogram2d allows to use different bins in each axis.

#Returns:
# 2D histogram with counts (H)
# x coordinate with ranges (x)
# y coordinate with ranges (y)
H, x, y = np.histogram2d( ages, heights, bins=(4,4), weights = people  )
#It is necessary to transpose the 2D Histogram in order to a proper plotting
H = np.transpose( H[:,::-1] )

#Plotting the histogram using the imshow function
plt.figure( figsize=(8,8) )
#Arguments:
# 2D histogram with counts (H)
# extent: ranges in the histogram (xmin,xmax,ymin,ymax)
# interpolation: type of interpolation between cells. "none" is better for this case.
# cmap: color palette used for representing data
plt.imshow( H, extent = (20,60,150,190), interpolation="none", cmap="binary" )
#Generating a colorbar for interpreting the data
plt.colorbar()

#Format of the figure
plt.title( "Number of people for different\n ranges of age and heights", fontsize=16 )
plt.xlabel( "Age [years]", fontsize=16 )
plt.ylabel( "Heights [cm]", fontsize=16 )

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