Data visualization is a very important part of data analysis. It helps to explore data and thus understands the insights of the data.
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import matplotlib.pyplot as plt
We show some very basic plots with matplotlib.
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year = [1950, 1970, 1990, 2010]
pop = [2.519, 3.692, 5.263, 6.972] #Population expressed in Billions
Simplest line chart.
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plt.plot(year,pop)
plt.show()
Scatter plot.
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plt.scatter(year,pop)
plt.show()
In the process of learning data visualization we will try to construct the famous data visualization by Swidish prof hans Rosling about global development. This is a beautiful data visualization that tells its own story.
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#@title
gdp = [974.5803384, 5937.029525999998, 6223.367465, 4797.231267, 12779.37964, 34435.367439999995, 36126.4927, 29796.04834, 1391.253792, 33692.60508, 1441.284873, 3822.137084, 7446.298803, 12569.85177, 9065.800825, 10680.79282, 1217.032994, 430.0706916, 1713.778686, 2042.09524, 36319.23501, 706.016537, 1704.063724, 13171.63885, 4959.114854, 7006.580419, 986.1478792, 277.5518587, 3632.557798, 9645.06142, 1544.750112, 14619.222719999998, 8948.102923, 22833.30851, 35278.41874, 2082.4815670000007, 6025.3747520000015, 6873.262326000001, 5581.180998, 5728.353514, 12154.08975, 641.3695236000002, 690.8055759, 33207.0844, 30470.0167, 13206.48452, 752.7497265, 32170.37442, 1327.60891, 27538.41188, 5186.050003, 942.6542111, 579.2317429999998, 1201.637154, 3548.3308460000007, 39724.97867, 18008.94444, 36180.78919, 2452.210407, 3540.651564, 11605.71449, 4471.061906, 40675.99635, 25523.2771, 28569.7197, 7320.8802620000015, 31656.06806, 4519.461171, 1463.249282, 1593.06548, 23348.139730000006, 47306.98978, 10461.05868, 1569.331442, 414.5073415, 12057.49928, 1044.770126, 759.3499101, 12451.6558, 1042.581557, 1803.151496, 10956.99112, 11977.57496, 3095.7722710000007, 9253.896111, 3820.17523, 823.6856205, 944.0, 4811.060429, 1091.359778, 36797.93332, 25185.00911, 2749.320965, 619.6768923999998, 2013.977305, 49357.19017, 22316.19287, 2605.94758, 9809.185636, 4172.838464, 7408.905561, 3190.481016, 15389.924680000002, 20509.64777, 19328.70901, 7670.122558, 10808.47561, 863.0884639000002, 1598.435089, 21654.83194, 1712.472136, 9786.534714, 862.5407561000002, 47143.17964, 18678.31435, 25768.25759, 926.1410683, 9269.657808, 28821.0637, 3970.095407, 2602.394995, 4513.480643, 33859.74835, 37506.41907, 4184.548089, 28718.27684, 1107.482182, 7458.396326999998, 882.9699437999999, 18008.50924, 7092.923025, 8458.276384, 1056.380121, 33203.26128, 42951.65309, 10611.46299, 11415.80569, 2441.576404, 3025.349798, 2280.769906, 1271.211593, 469.70929810000007]
life_exp = [43.828, 76.423, 72.301, 42.731, 75.32, 81.235, 79.829, 75.635, 64.062, 79.441, 56.728, 65.554, 74.852, 50.728, 72.39, 73.005, 52.295, 49.58, 59.723, 50.43, 80.653, 44.74100000000001, 50.651, 78.553, 72.961, 72.889, 65.152, 46.462, 55.322, 78.782, 48.328, 75.748, 78.273, 76.486, 78.332, 54.791, 72.235, 74.994, 71.33800000000002, 71.878, 51.57899999999999, 58.04, 52.947, 79.313, 80.657, 56.735, 59.448, 79.406, 60.022, 79.483, 70.259, 56.007, 46.38800000000001, 60.916, 70.19800000000001, 82.208, 73.33800000000002, 81.757, 64.69800000000001, 70.65, 70.964, 59.545, 78.885, 80.745, 80.546, 72.567, 82.603, 72.535, 54.11, 67.297, 78.623, 77.58800000000002, 71.993, 42.592, 45.678, 73.952, 59.44300000000001, 48.303, 74.241, 54.467, 64.164, 72.801, 76.195, 66.803, 74.543, 71.164, 42.082, 62.069, 52.90600000000001, 63.785, 79.762, 80.204, 72.899, 56.867, 46.859, 80.196, 75.64, 65.483, 75.53699999999998, 71.752, 71.421, 71.688, 75.563, 78.098, 78.74600000000002, 76.442, 72.476, 46.242, 65.528, 72.777, 63.062, 74.002, 42.56800000000001, 79.972, 74.663, 77.926, 48.159, 49.339, 80.941, 72.396, 58.556, 39.613, 80.884, 81.70100000000002, 74.143, 78.4, 52.517, 70.616, 58.42, 69.819, 73.923, 71.777, 51.542, 79.425, 78.242, 76.384, 73.747, 74.249, 73.422, 62.698, 42.38399999999999, 43.487]
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plt.plot(gdp,life_exp)
plt.show()
That's how NOT to do visualization as it don't make any sense ... Lets try something better.
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plt.scatter(gdp, life_exp)
# Put the x-axis on a logarithmic scale and the correlation will become clear
plt.xscale('log')
plt.show()
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pop = [31.889923, 3.600523, 33.333216, 12.420476, 40.301927, 20.434176, 8.199783, 0.708573, 150.448339, 10.392226, 8.078314, 9.119152, 4.552198, 1.639131, 190.010647, 7.322858, 14.326203, 8.390505, 14.131858, 17.696293, 33.390141, 4.369038, 10.238807, 16.284741, 1318.683096, 44.22755, 0.71096, 64.606759, 3.80061, 4.133884, 18.013409, 4.493312, 11.416987, 10.228744, 5.46812, 0.496374, 9.319622, 13.75568, 80.264543, 6.939688, 0.551201, 4.906585, 76.511887, 5.23846, 61.083916, 1.454867, 1.688359, 82.400996, 22.873338, 10.70629, 12.572928, 9.947814, 1.472041, 8.502814, 7.483763, 6.980412, 9.956108, 0.301931, 1110.396331, 223.547, 69.45357, 27.499638, 4.109086, 6.426679, 58.147733, 2.780132, 127.467972, 6.053193, 35.610177, 23.301725, 49.04479, 2.505559, 3.921278, 2.012649, 3.193942, 6.036914, 19.167654, 13.327079, 24.821286, 12.031795, 3.270065, 1.250882, 108.700891, 2.874127, 0.684736, 33.757175, 19.951656, 47.76198, 2.05508, 28.90179, 16.570613, 4.115771, 5.675356, 12.894865, 135.031164, 4.627926, 3.204897, 169.270617, 3.242173, 6.667147, 28.674757, 91.077287, 38.518241, 10.642836, 3.942491, 0.798094, 22.276056, 8.860588, 0.199579, 27.601038, 12.267493, 10.150265, 6.144562, 4.553009, 5.447502, 2.009245, 9.118773, 43.997828, 40.448191, 20.378239, 42.292929, 1.133066, 9.031088, 7.554661, 19.314747, 23.174294, 38.13964, 65.068149, 5.701579, 1.056608, 10.276158, 71.158647, 29.170398, 60.776238, 301.139947, 3.447496, 26.084662, 85.262356, 4.018332, 22.211743, 11.746035, 12.311143]
col = ['red', 'green', 'blue', 'blue', 'yellow', 'black', 'green', 'red', 'red', 'green', 'blue', 'yellow', 'green', 'blue', 'yellow', 'green', 'blue', 'blue', 'red', 'blue', 'yellow', 'blue', 'blue', 'yellow', 'red', 'yellow', 'blue', 'blue', 'blue', 'yellow', 'blue', 'green', 'yellow', 'green', 'green', 'blue', 'yellow', 'yellow', 'blue', 'yellow', 'blue', 'blue', 'blue', 'green', 'green', 'blue', 'blue', 'green', 'blue', 'green', 'yellow', 'blue', 'blue', 'yellow', 'yellow', 'red', 'green', 'green', 'red', 'red', 'red', 'red', 'green', 'red', 'green', 'yellow', 'red', 'red', 'blue', 'red', 'red', 'red', 'red', 'blue', 'blue', 'blue', 'blue', 'blue', 'red', 'blue', 'blue', 'blue', 'yellow', 'red', 'green', 'blue', 'blue', 'red', 'blue', 'red', 'green', 'black', 'yellow', 'blue', 'blue', 'green', 'red', 'red', 'yellow', 'yellow', 'yellow', 'red', 'green', 'green', 'yellow', 'blue', 'green', 'blue', 'blue', 'red', 'blue', 'green', 'blue', 'red', 'green', 'green', 'blue', 'blue', 'green', 'red', 'blue', 'blue', 'green', 'green', 'red', 'red', 'blue', 'red', 'blue', 'yellow', 'blue', 'green', 'blue', 'green', 'yellow', 'yellow', 'yellow', 'red', 'red', 'red', 'blue', 'blue']
# Import numpy as np
import numpy as np
# Update: set s argument to np_pop
plt.scatter(gdp, life_exp, s = np.array(pop)*2, c= col, alpha=0.6)
# Previous customizations
plt.xscale('log')
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')
plt.xticks([1000, 10000, 100000],['1k', '10k', '100k'])
# Additional customizations
plt.text(1550, 71, 'India')
plt.text(5700, 80, 'China')
# Add grid() call
plt.grid(True)
# Show the plot
plt.show()
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mu, sigma = 100, 15
values = mu + sigma * np.random.randn(10000)
plt.hist(values, bins=50, color='green')
plt.show()
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x = np.random.rand(10)
y = np.random.rand(10)
z = np.sqrt(x**2 + y**2)
plt.subplot(321)
plt.scatter(x, y, s=80, c=z, marker=">")
plt.subplot(322)
plt.scatter(x, y, s=80, c=z, marker=(5, 0))
verts = np.array([[-1, -1], [1, -1], [1, 1], [-1, -1]])
plt.subplot(323)
plt.scatter(x, y, s=80, c=z, marker=verts)
plt.subplot(324)
plt.scatter(x, y, s=80, c=z, marker=(5, 1))
plt.subplot(325)
plt.scatter(x, y, s=80, c=z, marker='+')
plt.subplot(326)
plt.scatter(x, y, s=80, c=z, marker=(5, 2))
plt.suptitle('Scatter Star Ploy')
plt.show()