<jupyter_start><jupyter_text>Case Study - Online Retailer  Example - 1   Bar Chart - Plot sales across each product category- A bar chart uses bars to show comparisons between categories of data.- A bar graph will always have two axis. - One axis will generally have numerical values or measures, - The other will describe the types of categories being compared or dimensions.<jupyter_code>import numpy as np
import matplotlib.pyplot as plt

product_category = np.array(['Furniture', 'Technology', 'Office Supplies'])
sales = np.array ([4110451.90, 4744557.50, 3787492.52] )

plt.bar(product_category, sales)
plt.show()<jupyter_output><empty_output><jupyter_text>Bar Chart - Plot sales across each product category1. Adding labels to Axes2. Reducing the bar width3. Giving Title to the chart4. Modifying the ticks to show information in (million dollars)<jupyter_code>import numpy as np
import matplotlib.pyplot as plt

product_category = np.array(['Furniture', 'Technology', 'Office Supplies'])
sales = np.array ([4110451.90, 4744557.50, 3787492.52] )

# plotting bar chart and setting bar width to 0.5 and aligning it to center
plt.bar(product_category, sales, width= 0.5, align='center', edgecolor='Orange',color='cyan')

# Adding and formatting title
plt.title("Sales Across Product Categories\n", fontdict={'fontsize': 20, 'fontweight' : 5, 'color' : 'Green'})

# Labeling Axes
plt.xlabel("Product Category", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})
plt.ylabel("Sales", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})

# Modifying the ticks to show information in (million dollars)
ticks = np.arange(0, 6000000, 1000000)
labels = ["{}M".format(i//1000000) for i in ticks]
plt.yticks(ticks, labels)

plt.show()<jupyter_output><empty_output><jupyter_text>Example - 2   Scatter Chart - Plot Sales versus Profits across various Countries and Product CategoriesScatter plots are used when you want to show the relationship between two facts or measures.- Profit is measured across each product category<jupyter_code>import numpy as np
import matplotlib.pyplot as plt

# Data
sales = np.array ([1013.14, 8298.48, 875.51, 22320.83, 9251.6, 4516.86, 585.16, 836154.03, 216748.48, 174.2, 27557.79, 563.25, 558.11, 37117.45, 357.36, 2206.96, 709.5, 35064.03, 7230.78, 235.33, 148.32, 3973.27, 11737.8, 7104.63, 83.67, 5569.83, 92.34, 107104.36, 1045.62, 9072.51, 42485.82, 5093.82, 14846.16, 943.92, 684.36, 15012.03, 38196.18, 2448.75, 28881.96, 13912.14, 4507.2, 4931.06, 12805.05, 67912.73, 4492.2, 1740.01, 458.04, 16904.32, 21744.53, 10417.26, 18665.33, 2808.42, 54195.57, 67332.5, 24390.95, 1790.43, 2234.19, 9917.5, 7408.14, 36051.99, 1352.22, 1907.7, 245722.14, 2154.66, 1078.21, 3391.65, 28262.73, 5177.04, 66.51, 2031.34, 1683.72, 1970.01, 6515.82, 1055.31, 1029.48, 5303.4, 1850.96, 1159.41, 39989.13, 1183.87, 96365.09, 8356.68, 7010.24, 23119.23, 46109.28, 146071.84, 242259.03, 9058.95, 1313.67, 31525.06, 2019.94, 703.04, 1868.79, 700.5, 55512.02, 243.5, 2113.18, 11781.81, 262189.49, 3487.29, 513.12, 312050.42, 5000.7, 121.02, 1302.78, 169.92, 124.29, 57366.05, 29445.93, 4614.3, 45009.98, 309.24, 3353.67, 41348.34, 2280.27, 61193.7, 1466.79, 12419.94, 445.12, 25188.65, 263514.92, 12351.23, 1152.3, 26298.81, 9900.78, 5355.57, 2325.66, 6282.81, 127707.92, 1283.1, 3560.15, 3723.84, 13715.01, 4887.9, 3396.89, 33348.42, 625.02, 1665.48, 32486.97, 340212.44, 20516.22, 8651.16, 13590.06, 2440.35, 6462.57, 1770.13, 7527.18, 1433.65, 423.3, 21601.72, 10035.72, 2378.49, 3062.38, 719469.32, 179366.79, 345.17, 30345.78, 300.71, 940.81, 36468.08, 1352.85, 1755.72, 2391.96, 19.98, 19792.8, 15633.88, 7.45, 521.67, 1118.24, 7231.68, 12399.32, 204.36, 23.64, 5916.48, 313.98, 108181.5, 9212.42, 27476.91, 1761.33, 289.5, 780.3, 15098.46, 813.27, 47.55, 8323.23, 22634.64, 1831.02, 28808.1, 10539.78, 588.99, 939.78, 7212.41, 15683.01, 41369.09, 5581.6, 403.36, 375.26, 12276.66, 15393.56, 76.65, 5884.38, 18005.49, 3094.71, 43642.78, 35554.83, 22977.11, 1026.33, 665.28, 9712.49, 6038.52, 30756.51, 3758.25, 4769.49, 2463.3, 160153.16, 967.11, 2311.74, 1414.83, 12764.91, 4191.24, 110.76, 637.34, 1195.12, 2271.63, 804.12, 196.17, 167.67, 131.77, 2842.05, 9969.12, 1784.35, 3098.49, 25005.54, 1300.1, 118697.39, 7920.54, 6471.78, 31707.57, 37636.47, 118777.77, 131170.76, 3980.88, 3339.39, 26563.9, 4038.73, 124.8, 196.65, 2797.77, 29832.76, 184.84, 79.08, 8047.83, 205313.25, 1726.98, 899.73, 224.06, 304763.54, 6101.31, 729.6, 896.07, 17.82, 26.22, 46429.78, 31167.27, 2455.94, 37714.3, 1506.93, 3812.78, 25223.34, 3795.96, 437.31, 41278.86, 2091.81, 6296.61, 468.82, 23629.64, 160435.53, 9725.46, 1317.03, 1225.26, 30034.08, 7893.45, 2036.07, 215.52, 3912.42, 82783.43, 253.14, 966.96, 3381.26, 164.07, 1984.23, 75.12, 25168.17, 3295.53, 991.12, 10772.1, 44.16, 1311.45, 35352.57, 245783.54, 20.49, 13471.06, 8171.16, 14075.67, 611.82, 3925.56, 981.84, 10209.84, 156.56, 243.06, 21287.52, 7300.51, 434.52, 6065.0, 741577.51, 132461.03, 224.75, 28953.6, 757.98, 528.15, 34922.41, 50.58, 2918.48, 1044.96, 22195.13, 3951.48, 6977.64, 219.12, 5908.38, 10987.46, 4852.26, 445.5, 71860.82, 14840.45, 24712.08, 1329.9, 1180.44, 85.02, 10341.63, 690.48, 1939.53, 20010.51, 914.31, 25223.82, 12804.66, 2124.24, 602.82, 2961.66, 15740.79, 74138.35, 7759.39, 447.0, 2094.84, 22358.95, 21734.53, 4223.73, 17679.53, 1019.85, 51848.72, 69133.3, 30146.9, 705.48, 14508.88, 7489.38, 20269.44, 246.12, 668.13, 768.93, 215677.35, 899.16, 2578.2, 4107.99, 20334.57, 366.84, 3249.27, 98.88, 3497.88, 3853.05, 786.75, 1573.68, 458.36, 1234.77, 1094.22, 2300.61, 970.14, 3068.25, 35792.85, 4277.82, 71080.28, 3016.86, 3157.49, 15888.0, 30000.36, 140037.89, 216056.25, 1214.22, 1493.94, 32036.69, 4979.66, 106.02, 46257.68, 1033.3, 937.32, 3442.62, 160633.45, 213.15, 338.88, 242117.13, 9602.34, 2280.99, 73759.08, 23526.12, 6272.74, 43416.3, 576.78, 1471.61, 20844.9, 3497.7, 56382.38, 902.58, 6235.26, 48.91, 32684.24, 276611.58, 13370.38, 10595.28, 4555.14, 10084.38, 267.72, 1012.95, 4630.5, 149433.51, 364.32, 349.2, 4647.56, 504.0, 10343.52, 5202.66, 2786.26, 34135.95, 2654.58, 24699.51, 339239.87, 136.26, 23524.51, 8731.68, 8425.86, 835.95, 11285.19])
profit = np.array([-1213.46, 1814.13, -1485.7, -2286.73, -2872.12, 946.8, 198.48, 145454.95, 49476.1, -245.56, 5980.77, -790.47, -895.72, -34572.08, 117.9, 561.96, 152.85, 1426.05, 1873.17, -251.03, 68.22, 635.11, 3722.4, -3168.63, 27.6, 952.11, 7.38, 20931.13, 186.36, -5395.38, 9738.45, 525.27, 3351.99, 120.78, 266.88, 3795.21, 8615.97, 609.54, 7710.57, 2930.43, 1047.96, -2733.32, 2873.73, -5957.89, -909.6, 163.41, -376.02, -6322.68, -10425.86, 2340.36, -28430.53, 756.12, 12633.33, 7382.54, -14327.69, 436.44, 683.85, -694.91, 1960.56, 10925.82, 334.08, 425.49, 53580.2, 1024.56, 110.93, 632.22, 8492.58, 1418.88, 19.26, -2567.57, 346.26, 601.86, 1318.68, 304.05, 428.37, 1416.24, -2878.18, 283.41, 12611.04, 261.95, -648.43, 1112.88, -2640.29, 6154.32, 11558.79, 15291.4, 56092.65, 1515.39, 342.03, -10865.66, -902.8, 351.52, 364.17, 87.72, 11565.66, 75.4, 289.33, 3129.63, 50795.72, 783.72, 215.46, 29196.89, 1147.26, 53.22, 286.56, 73.02, 42.24, 13914.85, 5754.54, 998.04, -1476.04, 86.58, -1636.35, 10511.91, 647.34, 13768.62, 338.67, 3095.67, 173.84, 5632.93, 64845.11, 3297.33, 338.61, 7246.62, 2255.52, 1326.36, 827.64, 1100.58, 9051.36, 412.23, 1063.91, 940.59, 3891.84, 1599.51, 1129.57, 8792.64, 6.24, 592.77, 8792.85, 47727.5, -4597.68, 2242.56, 3546.45, 321.87, 1536.72, -2463.29, 1906.08, -1916.99, 186.24, 3002.05, -3250.98, 554.7, 830.64, 122612.79, 33894.21, -559.03, 7528.05, -477.67, -1660.25, -33550.96, 481.68, 425.08, 450.3, 9.57, -3025.29, 2924.62, -11.84, 87.36, 26.51, 1727.19, -6131.18, 59.16, 3.06, 1693.47, 74.67, 24729.21, -4867.94, 6705.18, 410.79, 70.74, 101.7, 3264.3, 137.01, 6.18, 2100.21, 5295.24, 520.29, 7205.52, 2602.65, 116.67, 224.91, -5153.93, 3882.69, -6535.24, -1254.1, 84.56, -186.38, -3167.2, -7935.59, 37.02, 1908.06, -27087.84, 829.32, 8727.44, 2011.47, -11629.64, 234.96, 53.1, 1248.14, 1511.07, 7374.24, 1193.28, 1090.23, 553.86, 38483.86, 255.81, 528.54, 326.07, 3924.36, 1018.92, 36.48, 113.24, -1770.05, 527.64, 224.49, 79.53, 64.77, 38.08, 868.08, 2265.06, -2643.62, 833.73, 5100.03, 326.44, 18158.84, 1682.01, -3290.22, 8283.33, 7926.18, 1694.41, 30522.92, 1214.07, 900.6, -6860.8, -865.91, 26.16, 47.22, 863.52, 7061.26, 73.92, 33.12, 1801.23, 38815.44, 431.13, 216.81, 16.5, 53688.2, 1210.32, 236.94, 210.84, 3.18, 2.22, 10265.64, 7212.3, 343.56, 3898.28, 568.11, -1867.85, 5782.38, 697.29, -192.06, 10179.02, 616.32, 1090.47, 165.84, 6138.28, 39723.06, 2085.14, 90.0, 129.93, 7957.53, 2131.86, 562.44, 99.12, 1298.37, 7580.33, 113.73, 139.71, 456.0, 21.24, 292.68, 30.34, 5817.15, 1060.89, 252.9, 3060.61, 6.6, 219.09, 8735.82, 31481.09, 2.85, -3124.72, 2195.94, 3464.7, 141.12, 1125.69, -1752.03, 3281.52, -303.77, 114.18, -2412.63, -5099.61, 146.64, 660.22, 18329.28, 28529.84, -232.27, 7435.41, -1157.94, -746.73, -30324.2, 2.52, 1313.44, 213.72, -5708.95, 930.18, 1663.02, 31.59, 1787.88, -8219.56, 973.92, 4.32, 8729.78, -2529.52, 5361.06, 69.21, 519.3, 13.56, 2236.77, 213.96, 367.98, 5074.2, 206.61, 7620.36, 2093.19, 164.07, 230.01, -815.82, 4226.7, -3635.09, -3344.17, 167.26, 143.79, -8233.57, -4085.21, 919.35, -25232.35, 234.33, 12040.68, 7206.28, -15112.76, 206.04, -2662.49, 2346.81, 4461.36, 93.48, 82.11, 147.87, 10389.53, 395.58, 474.74, 1333.26, 3913.02, 117.36, 858.78, 6.9, -4628.49, 1170.6, 218.55, 539.58, -211.0, 438.87, 317.16, 310.8, -1578.09, 706.56, 6617.4, 803.84, 2475.26, 764.34, -1461.88, 3805.56, 7371.27, -1377.13, 42435.03, 472.47, 315.48, -11755.91, -2418.6, 6.36, 9317.76, 326.88, -287.31, 637.68, 17579.17, 70.83, 47.4, 26143.92, 1548.15, 612.78, 17842.76, 6735.39, 1206.5, -10035.74, 149.4, -777.85, 5566.29, 748.92, 14941.58, 348.93, 1944.06, -5.51, 7026.84, 46114.92, 2361.86, 2613.24, 1277.37, 2587.74, 103.08, 311.43, 1250.58, 13055.21, 18.21, 108.24, 709.44, 115.92, 1863.6, 1873.86, 817.32, 7577.64, 1019.19, 6813.03, 24698.84, 66.24, -10971.39, 2056.47, 2095.35, 246.33, 2797.89])
product_category = np.array(['Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Technology', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Furniture'])
country = np.array(['Zimbabwe', 'Zambia', 'Yemen', 'Vietnam', 'Venezuela', 'Uzbekistan', 'Uruguay', 'United States', 'United Kingdom', 'United Arab Emirates', 'Ukraine', 'Uganda', 'Turkmenistan', 'Turkey', 'Tunisia', 'Trinidad and Tobago', 'Togo', 'Thailand', 'Tanzania', 'Tajikistan', 'Taiwan', 'Syria', 'Switzerland', 'Sweden', 'Swaziland', 'Sudan', 'Sri Lanka', 'Spain', 'South Sudan', 'South Korea', 'South Africa', 'Somalia', 'Singapore', 'Sierra Leone', 'Serbia', 'Senegal', 'Saudi Arabia', 'Rwanda', 'Russia', 'Romania', 'Qatar', 'Portugal', 'Poland', 'Philippines', 'Peru', 'Paraguay', 'Papua New Guinea', 'Panama', 'Pakistan', 'Norway', 'Nigeria', 'Niger', 'Nicaragua', 'New Zealand', 'Netherlands', 'Nepal', 'Namibia', 'Myanmar (Burma)', 'Mozambique', 'Morocco', 'Mongolia', 'Moldova', 'Mexico', 'Mauritania', 'Martinique', 'Mali', 'Malaysia', 'Madagascar', 'Luxembourg', 'Lithuania', 'Libya', 'Liberia', 'Lesotho', 'Lebanon', 'Kyrgyzstan', 'Kenya', 'Kazakhstan', 'Jordan', 'Japan', 'Jamaica', 'Italy', 'Israel', 'Ireland', 'Iraq', 'Iran', 'Indonesia', 'India', 'Hungary', 'Hong Kong', 'Honduras', 'Haiti', 'Guyana', 'Guinea-Bissau', 'Guinea', 'Guatemala', 'Guadeloupe', 'Greece', 'Ghana', 'Germany', 'Georgia', 'Gabon', 'France', 'Finland', 'Ethiopia', 'Estonia', 'Eritrea', 'Equatorial Guinea', 'El Salvador', 'Egypt', 'Ecuador', 'Dominican Republic', 'Djibouti', 'Denmark', 'Democratic Republic of the Congo', 'Czech Republic', 'Cuba', 'Croatia', "Cote d'Ivoire", 'Costa Rica', 'Colombia', 'China', 'Chile', 'Central African Republic', 'Canada', 'Cameroon', 'Cambodia', 'Burkina Faso', 'Bulgaria', 'Brazil', 'Bosnia and Herzegovina', 'Bolivia', 'Benin', 'Belgium', 'Belarus', 'Barbados', 'Bangladesh', 'Bahrain', 'Azerbaijan', 'Austria', 'Australia', 'Argentina', 'Angola', 'Algeria', 'Albania', 'Afghanistan', 'Zimbabwe', 'Zambia', 'Yemen', 'Western Sahara', 'Vietnam', 'Venezuela', 'Uzbekistan', 'Uruguay', 'United States', 'United Kingdom', 'United Arab Emirates', 'Ukraine', 'Uganda', 'Turkmenistan', 'Turkey', 'Tunisia', 'Trinidad and Tobago', 'Togo', 'The Gambia', 'Thailand', 'Tanzania', 'Tajikistan', 'Taiwan', 'Syria', 'Switzerland', 'Sweden', 'Swaziland', 'Suriname', 'Sudan', 'Sri Lanka', 'Spain', 'South Korea', 'South Africa', 'Somalia', 'Slovenia', 'Slovakia', 'Singapore', 'Sierra Leone', 'Serbia', 'Senegal', 'Saudi Arabia', 'Rwanda', 'Russia', 'Romania', 'Republic of the Congo', 'Qatar', 'Portugal', 'Poland', 'Philippines', 'Peru', 'Paraguay', 'Papua New Guinea', 'Panama', 'Pakistan', 'Oman', 'Norway', 'Nigeria', 'Niger', 'Nicaragua', 'New Zealand', 'Netherlands', 'Nepal', 'Namibia', 'Myanmar (Burma)', 'Mozambique', 'Morocco', 'Montenegro', 'Mongolia', 'Moldova', 'Mexico', 'Mauritania', 'Martinique', 'Mali', 'Malaysia', 'Madagascar', 'Macedonia', 'Luxembourg', 'Lithuania', 'Libya', 'Liberia', 'Lesotho', 'Lebanon', 'Laos', 'Kyrgyzstan', 'Kenya', 'Kazakhstan', 'Jordan', 'Japan', 'Jamaica', 'Italy', 'Israel', 'Ireland', 'Iraq', 'Iran', 'Indonesia', 'India', 'Hungary', 'Hong Kong', 'Honduras', 'Haiti', 'Guyana', 'Guinea-Bissau', 'Guinea', 'Guatemala', 'Guadeloupe', 'Greece', 'Ghana', 'Germany', 'Georgia', 'Gabon', 'French Guiana', 'France', 'Finland', 'Ethiopia', 'Estonia', 'Eritrea', 'Equatorial Guinea', 'El Salvador', 'Egypt', 'Ecuador', 'Dominican Republic', 'Djibouti', 'Denmark', 'Democratic Republic of the Congo', 'Czech Republic', 'Cyprus', 'Cuba', 'Croatia', "Cote d'Ivoire", 'Costa Rica', 'Colombia', 'China', 'Chile', 'Chad', 'Central African Republic', 'Canada', 'Cameroon', 'Cambodia', 'Burkina Faso', 'Bulgaria', 'Brazil', 'Botswana', 'Bosnia and Herzegovina', 'Bolivia', 'Bhutan', 'Benin', 'Belize', 'Belgium', 'Belarus', 'Barbados', 'Bangladesh', 'Bahrain', 'Azerbaijan', 'Austria', 'Australia', 'Armenia', 'Argentina', 'Angola', 'Algeria', 'Albania', 'Afghanistan', 'Zimbabwe', 'Zambia', 'Yemen', 'Western Sahara', 'Vietnam', 'Venezuela', 'Uzbekistan', 'Uruguay', 'United States', 'United Kingdom', 'United Arab Emirates', 'Ukraine', 'Uganda', 'Turkmenistan', 'Turkey', 'Tunisia', 'Trinidad and Tobago', 'Togo', 'Thailand', 'Tanzania', 'Taiwan', 'Syria', 'Switzerland', 'Sweden', 'Sudan', 'Sri Lanka', 'Spain', 'South Korea', 'South Africa', 'Somalia', 'Slovenia', 'Slovakia', 'Singapore', 'Sierra Leone', 'Senegal', 'Saudi Arabia', 'Rwanda', 'Russia', 'Romania', 'Republic of the Congo', 'Qatar', 'Portugal', 'Poland', 'Philippines', 'Peru', 'Paraguay', 'Papua New Guinea', 'Panama', 'Pakistan', 'Norway', 'Nigeria', 'Niger', 'Nicaragua', 'New Zealand', 'Netherlands', 'Nepal', 'Myanmar (Burma)', 'Mozambique', 'Morocco', 'Montenegro', 'Mongolia', 'Moldova', 'Mexico', 'Mauritania', 'Martinique', 'Mali', 'Malaysia', 'Malawi', 'Madagascar', 'Macedonia', 'Lithuania', 'Libya', 'Liberia', 'Lebanon', 'Laos', 'Kyrgyzstan', 'Kuwait', 'Kenya', 'Kazakhstan', 'Jordan', 'Japan', 'Jamaica', 'Italy', 'Israel', 'Ireland', 'Iraq', 'Iran', 'Indonesia', 'India', 'Hungary', 'Hong Kong', 'Honduras', 'Haiti', 'Guyana', 'Guatemala', 'Guadeloupe', 'Greece', 'Ghana', 'Germany', 'Georgia', 'Gabon', 'France', 'Finland', 'Estonia', 'El Salvador', 'Egypt', 'Ecuador', 'Dominican Republic', 'Djibouti', 'Denmark', 'Democratic Republic of the Congo', 'Czech Republic', 'Cuba', 'Croatia', "Cote d'Ivoire", 'Costa Rica', 'Colombia', 'China', 'Chile', 'Canada', 'Cameroon', 'Cambodia', 'Burundi', 'Burkina Faso', 'Bulgaria', 'Brazil', 'Botswana', 'Bosnia and Herzegovina', 'Bolivia', 'Benin', 'Belgium', 'Belarus', 'Barbados', 'Bangladesh', 'Azerbaijan', 'Austria', 'Australia', 'Armenia', 'Argentina', 'Angola', 'Algeria', 'Albania', 'Afghanistan'])
# plotting scatter chart

plt.scatter(profit, sales, alpha= 0.7, s = 50 )

# Adding and formatting title
plt.title("Sales versus Profits across various Countries and Product Categories\n", fontdict={'fontsize': 20, 'fontweight' : 5, 'color' : 'Green'})

# Labeling Axes
plt.xlabel("Profit", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})
plt.ylabel("Sales", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})

plt.show()<jupyter_output><empty_output><jupyter_text>Scatter Chart - Plot Sales versus Profits across various Countries and Product Categories- Represent product category using different colors- Adding a Legend to Product Categories<jupyter_code>plt.scatter(profit[product_category == "Technology"], sales[product_category == "Technology"], 
            c= 'Green', alpha= 0.7, s = 150, label="Technology" )

plt.scatter(profit[product_category == "Office Supplies"], sales[product_category == "Office Supplies"], 
            c= 'Yellow', alpha= 0.7, s = 100, label="Office Supplies" )

plt.scatter(profit[product_category == "Furniture"], sales[product_category == "Furniture"], 
            c= 'Cyan', alpha= 0.7, s = 50, label="Furniture" )

# Adding and formatting title
plt.title("Sales versus Profits across various Countries and Product Categories\n", fontdict={'fontsize': 20, 'fontweight' : 5, 'color' : 'Green'})

# Labeling Axes
plt.xlabel("Profit", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})
plt.ylabel("Sales", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})

plt.legend()

plt.show()
plt.scatter(profit[product_category == "Technology"], sales[product_category == "Technology"], 
            c= 'Green', alpha= 0.7, s = 150, label="Technology" )

plt.scatter(profit[product_category == "Office Supplies"], sales[product_category == "Office Supplies"], 
            c= 'Yellow', alpha= 0.7, s = 100, label="Office Supplies" )

plt.scatter(profit[product_category == "Furniture"], sales[product_category == "Furniture"], 
            c= 'Cyan', alpha= 0.7, s = 50, label="Furniture" )

for xy in zip (profit[country == "India"], sales[country == "India"]):
	plt.annotate(s = "India", xy = xy)

# Adding and formatting title
plt.title("Sales versus Profits across various Countries and Product Categories\n", fontdict={'fontsize': 20, 'fontweight' : 5, 'color' : 'Green'})

# Labeling Axes
plt.xlabel("Profit", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})
plt.ylabel("Sales", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})

plt.legend()

plt.show()<jupyter_output><ipython-input-6-f7d9a39aeb24>:11: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.
  plt.annotate(s = "India", xy = xy)<jupyter_text>Example - 3   Line Chart - Plot Sales across 2015A line chart or line plot or line graph or curve chart is a type of chart which displays information as a series of data points called 'markers' connected by straight line segments. Most commonly used with time data.<jupyter_code>import numpy as np
import matplotlib.pyplot as plt

months = np.array(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'])
sales = np.array([241268.56, 184837.36, 263100.77, 242771.86, 288401.05, 401814.06, 258705.68, 456619.94, 481157.24, 422766.63, 555279.03, 503143.69])
plt.plot(months, sales)

# Adding and formatting title
plt.title("Sales across 2015\n", fontdict={'fontsize': 20, 'fontweight' : 5, 'color' : 'Green'})

# Labeling Axes
plt.xlabel("Months", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})
plt.ylabel("Sales", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'} )

ticks = np.arange(0, 600000, 50000)
labels = ["{}K".format(i//1000) for i in ticks]
plt.yticks(ticks, labels)

plt.xticks(rotation=90)

plt.show()<jupyter_output><empty_output><jupyter_text>Line Chart - Plot Sales across 2015- Add labels to marks<jupyter_code>plt.plot(months, sales)

# Adding and formatting title
plt.title("Sales across 2015\n", fontdict={'fontsize': 20, 'fontweight' : 5, 'color' : 'Green'})

# Labeling Axes
plt.xlabel("Months", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})
plt.ylabel("Sales", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'} )

ticks = np.arange(0, 600000, 50000)
labels = ["{}K".format(i//1000) for i in ticks]
plt.yticks(ticks, labels)

plt.xticks(rotation=90)

for xy in zip(months, sales):
    plt.annotate(s = "{}K".format(xy[1]//1000), xy = xy,  textcoords='data')

plt.show()<jupyter_output><ipython-input-9-3b40c7bab4a1>:17: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.
  plt.annotate(s = "{}K".format(xy[1]//1000), xy = xy,  textcoords='data')<jupyter_text>Example - 4   Box and Whisker Chart - Sales across Countries and Product CategoriesA Box and Whisker Plot (or Box Plot) is a convenient way of visually displaying the data distribution through their quartiles. The lines extending parallel from the boxes are known as the “whiskers”, which are used to indicate variability outside the upper and lower quartiles. Outliers are sometimes plotted as individual dots that are in-line with whiskers. Box Plots can be drawn either vertically or horizontally.<jupyter_code>import numpy as np
import matplotlib.pyplot as plt

# Data
sales_technology = np.array ([1013.14, 8298.48, 875.51, 22320.83, 9251.6, 4516.86, 585.16, 174.2, 27557.79, 563.25, 558.11, 37117.45, 357.36, 2206.96, 709.5, 35064.03, 7230.78, 235.33, 148.32, 3973.27, 11737.8, 7104.63, 83.67, 5569.83, 92.34, 1045.62, 9072.51, 42485.82, 5093.82, 14846.16, 943.92, 684.36, 15012.03, 38196.18, 2448.75, 28881.96, 13912.14, 4507.2, 4931.06, 12805.05, 67912.73, 4492.2, 1740.01, 458.04, 16904.32, 21744.53, 10417.26, 18665.33, 2808.42, 54195.57, 67332.5, 24390.95, 1790.43, 2234.19, 9917.5, 7408.14, 36051.99, 1352.22, 1907.7, 2154.66, 1078.21, 3391.65, 28262.73, 5177.04, 66.51, 2031.34, 1683.72, 1970.01, 6515.82, 1055.31, 1029.48, 5303.4, 1850.96, 1159.41, 39989.13, 1183.87, 96365.09, 8356.68, 7010.24, 23119.23, 46109.28, 9058.95, 1313.67, 31525.06, 2019.94, 703.04, 1868.79, 700.5, 55512.02, 243.5, 2113.18, 11781.81, 3487.29, 513.12, 5000.7, 121.02, 1302.78, 169.92, 124.29, 57366.05, 29445.93, 4614.3, 45009.98, 309.24, 3353.67, 41348.34, 2280.27, 61193.7, 1466.79, 12419.94, 445.12, 25188.65, 12351.23, 1152.3, 26298.81, 9900.78, 5355.57, 2325.66, 6282.81, 1283.1, 3560.15, 3723.84, 13715.01, 4887.9, 3396.89, 33348.42, 625.02, 1665.48, 32486.97, 20516.22, 8651.16, 13590.06, 2440.35, 6462.57])
sales_office_supplies = np.array ([1770.13, 7527.18, 1433.65, 423.3, 21601.72, 10035.72, 2378.49, 3062.38, 345.17, 30345.78, 300.71, 940.81, 36468.08, 1352.85, 1755.72, 2391.96, 19.98, 19792.8, 15633.88, 7.45, 521.67, 1118.24, 7231.68, 12399.32, 204.36, 23.64, 5916.48, 313.98, 9212.42, 27476.91, 1761.33, 289.5, 780.3, 15098.46, 813.27, 47.55, 8323.23, 22634.64, 1831.02, 28808.1, 10539.78, 588.99, 939.78, 7212.41, 15683.01, 41369.09, 5581.6, 403.36, 375.26, 12276.66, 15393.56, 76.65, 5884.38, 18005.49, 3094.71, 43642.78, 35554.83, 22977.11, 1026.33, 665.28, 9712.49, 6038.52, 30756.51, 3758.25, 4769.49, 2463.3, 967.11, 2311.74, 1414.83, 12764.91, 4191.24, 110.76, 637.34, 1195.12, 2271.63, 804.12, 196.17, 167.67, 131.77, 2842.05, 9969.12, 1784.35, 3098.49, 25005.54, 1300.1, 7920.54, 6471.78, 31707.57, 37636.47, 3980.88, 3339.39, 26563.9, 4038.73, 124.8, 196.65, 2797.77, 29832.76, 184.84, 79.08, 8047.83, 1726.98, 899.73, 224.06, 6101.31, 729.6, 896.07, 17.82, 26.22, 46429.78, 31167.27, 2455.94, 37714.3, 1506.93, 3812.78, 25223.34, 3795.96, 437.31, 41278.86, 2091.81, 6296.61, 468.82, 23629.64, 9725.46, 1317.03, 1225.26, 30034.08, 7893.45, 2036.07, 215.52, 3912.42, 82783.43, 253.14, 966.96, 3381.26, 164.07, 1984.23, 75.12, 25168.17, 3295.53, 991.12, 10772.1, 44.16, 1311.45, 35352.57, 20.49, 13471.06, 8171.16, 14075.67, 611.82, 3925.56])
sales_furniture = np.array ([981.84, 10209.84, 156.56, 243.06, 21287.52, 7300.51, 434.52, 6065.0, 224.75, 28953.6, 757.98, 528.15, 34922.41, 50.58, 2918.48, 1044.96, 22195.13, 3951.48, 6977.64, 219.12, 5908.38, 10987.46, 4852.26, 445.5, 71860.82, 14840.45, 24712.08, 1329.9, 1180.44, 85.02, 10341.63, 690.48, 1939.53, 20010.51, 914.31, 25223.82, 12804.66, 2124.24, 602.82, 2961.66, 15740.79, 74138.35, 7759.39, 447.0, 2094.84, 22358.95, 21734.53, 4223.73, 17679.53, 1019.85, 51848.72, 69133.3, 30146.9, 705.48, 14508.88, 7489.38, 20269.44, 246.12, 668.13, 768.93, 899.16, 2578.2, 4107.99, 20334.57, 366.84, 3249.27, 98.88, 3497.88, 3853.05, 786.75, 1573.68, 458.36, 1234.77, 1094.22, 2300.61, 970.14, 3068.25, 35792.85, 4277.82, 71080.28, 3016.86, 3157.49, 15888.0, 30000.36, 1214.22, 1493.94, 32036.69, 4979.66, 106.02, 46257.68, 1033.3, 937.32, 3442.62, 213.15, 338.88, 9602.34, 2280.99, 73759.08, 23526.12, 6272.74, 43416.3, 576.78, 1471.61, 20844.9, 3497.7, 56382.38, 902.58, 6235.26, 48.91, 32684.24, 13370.38, 10595.28, 4555.14, 10084.38, 267.72, 1012.95, 4630.5, 364.32, 349.2, 4647.56, 504.0, 10343.52, 5202.66, 2786.26, 34135.95, 2654.58, 24699.51, 136.26, 23524.51, 8731.68, 8425.86, 835.95, 11285.19])

plt.boxplot([sales_technology, sales_office_supplies, sales_furniture])

# Adding and formatting title
plt.title("Sales across Countries and Product Categories\n", fontdict={'fontsize': 20, 'fontweight' : 5, 'color' : 'Green'})

# Labeling Axes
plt.xlabel("Product Category", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})
plt.ylabel("Sales", fontdict={'fontsize': 12, 'fontweight' : 5, 'color' : 'Brown'})

plt.xticks((1,2,3),["Technology", "Office Supplies", "Furniture"])

plt.show()<jupyter_output><empty_output><jupyter_text>Example - 5   Histogram - Plot a histogram for Sales distribution across Countries.A histogram is a plot that lets you discover, and show, the underlying frequency distribution (shape) of a set of continuous data.<jupyter_code>import numpy as np
import matplotlib.pyplot as plt

# Data
profit = np.array([-5428.79, 7001.73, -3706.46, 300.42, -1697.31, -11222.71, 1648.14, 1689.34, -1036.86, 20944.23, -2426.09, -3302.71, 602.1, 2300.48, 816.87, 9.57, -7308.2, 5727.97, -262.87, 1818.6, 693.21, 7237.47, -17519.37, 86.76, 3.06, 3619.5, 86.37, 54390.12, 186.36, -12792.83, 21804.69, 1005.27, 590.04, 115.26, 8853.06, 471.75, 273.06, 6263.4, 18985.41, 1336.44, 22536.45, 7626.27, 280.74, 1502.88, -8703.06, 10983.12, -16128.23, -5507.88, 415.23, -418.61, -17723.45, -22446.65, 37.02, 5167.77, 1819.77, 33401.44, 16600.28, 877.44, 736.95, -2109.26, 5818.44, 22761.42, 1286.76, 1506.42, 1127.22, 1675.95, 1114.21, 2291.55, 16329.96, 117.36, 3296.58, 43.38, 132.5, -8966.12, 2044.5, 1044.9, 1398.21, 908.4, -172.92, 1735.32, 317.16, 3992.1, -7099.9, 1823.7, 24328.47, 1392.23, 19985.68, 3559.23, -7392.38, 18243.21, 26856.24, 15608.68, 3201.93, 1558.11, -29482.37, -4187.31, 384.04, 411.39, 951.24, 27944.69, 476.2, 35.14, 5568.54, 1285.68, 479.67, 16.5, 3905.73, 290.16, 1110.18, 76.2, 44.46, 42023.24, 19702.23, 2548.1, -7613.5, 804.09, -4282.05, 21860.58, 2093.55, -192.06, 38889.22, 1303.92, 6130.2, 334.17, 18798.05, 7744.33, 90.0, 468.54, 17817.39, 5664.75, 4476.54, 103.08, 1238.19, 3649.53, 29686.9, 131.94, 660.18, 2229.35, 21.24, 1349.19, 30.34, 11572.59, 4534.26, 2199.79, 19430.89, 12.84, 1831.05, 24341.7, 69.09, -18693.8, 6494.97, 9106.5, 709.32, 5460.3])

plt.hist(profit, bins = 100,edgecolor='Orange',color='cyan')

plt.show()<jupyter_output><empty_output><jupyter_text>Example - 6   SubPlots - Plot Sales for various markets for years 2012 to 2015<jupyter_code>import numpy as np
import matplotlib.pyplot as plt

years = np.array(['2012', '2013', '2014', '2015'])

sales_africa = np.array([127187.27, 144480.70, 229068.79, 283036.44])

sales_USCA = np.array([492756.60, 486629.30, 627634.98, 757108.13])

sales_LATAM = np.array([385098.15, 464733.29, 608140.77, 706632.93])

sales_Asia_Pacific = np.array([713658.22, 863983.97, 1092231.65, 1372784.40])

sales_Europe = np.array([540750.63, 717611.40, 848670.24, 1180303.95])<jupyter_output><empty_output><jupyter_text>Subplots are shown in the same graph as line charts, identified by different colours<jupyter_code>fig, ax = plt.subplots()

europe, = ax.plot(years, sales_Europe)
europe.set_label("Europe")

usca, = ax.plot(years, sales_USCA)
usca.set_label("USCA")
usca.set_dashes([2, 2, 2, 2])

africa, = ax.plot(years, sales_africa)
africa.set_label("Africa")
africa.set_dashes([2, 2, 5, 2])

asia, = ax.plot(years, sales_Asia_Pacific)
asia.set_label("Asia Pacific")
asia.set_dashes([2, 2, 10, 2])

latam, = ax.plot(years, sales_LATAM)
latam.set_label("LATAM")
latam.set_dashes([2, 5, 5, 2])

plt.legend()

plt.legend(bbox_to_anchor=(1.31,0.4))

plt.show()<jupyter_output><empty_output><jupyter_text>Subplots are shown in seperate graphs. Each chart can be of different types.<jupyter_code>fig, ax = plt.subplots(ncols=3, nrows=2, sharex=True, sharey=True)

europe, = ax[0][0].plot(years, sales_Europe)
europe.set_label("Europe")

usca = ax[0][1].bar(years, sales_USCA)
usca.set_label("USCA")

africa = ax[0][2].scatter(years, sales_africa)
africa.set_label("Africa")

asia, = ax[1][0].plot(years, sales_Asia_Pacific)
asia.set_label("Asia Pacific")
asia.set_dashes([2, 2, 10, 2])

latam, = ax[1][1].plot(years, sales_LATAM)
latam.set_label("LATAM")
latam.set_dashes([2, 5, 5, 2])


plt.show()
fig, ax = plt.subplots(ncols=4, sharey=True)

europe, = ax[0].plot(years, sales_Europe)
europe.set_label("Europe")
europe.set_color("red")
ax[0].set_title('Sales in Europe')

usca = ax[1].bar(years, sales_USCA)
usca.set_label("USCA")
ax[1].set_title('Sales in USCA')

africa = ax[2].scatter(years, sales_africa)
africa.set_label("Africa")
ax[2].set_title('Sales in Africa')

asia = ax[3].bar(years, sales_Asia_Pacific, width = 0.5, color='royalblue')

latam = ax[3].bar(years, sales_LATAM, width = 0.5, color='seagreen')

ax[3].set_title('Sales in Asia Pacific and LATAM')
ax[3].legend( (asia[0], latam[0]), ('Asia Pacific', 'LATAM') )

fig.set_size_inches(20.5, 5.5, forward=True)

plt.show()<jupyter_output><empty_output>
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