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Intermediate py for DS.py
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Intermediate py for DS.py
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import matplotlib.pyplot as plt
year = [1950, 1970, 1990, 2010]
pop = [2.519, 3.692, 5.263, 6.972]
plt.plot(year, pop)
plt.show()
import matplotlib.pyplot as plt
year = [1950, 1970, 1990, 2010]
pop = [2.519, 3.692, 5.263, 6.972]
plt.scatter(year, pop)
plt.show()
import matplotlib.pyplot as plt
year = [1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100]
pop = [2.53, 2.57, 2.62, 2.67, 2.71, 2.76, 2.81, 2.86, 2.92, 2.97, 3.03, 3.08, 3.14, 3.2, 3.26, 3.33, 3.4, 3.47, 3.54, 3.62, 3.69, 3.77, 3.84, 3.92, 4.0, 4.07, 4.15, 4.22, 4.3, 4.37, 4.45, 4.53, 4.61, 4.69, 4.78, 4.86, 4.95, 5.05, 5.14, 5.23, 5.32, 5.41, 5.49, 5.58, 5.66, 5.74, 5.82, 5.9, 5.98, 6.05, 6.13, 6.2, 6.28, 6.36, 6.44, 6.51, 6.59, 6.67, 6.75, 6.83, 6.92, 7.0, 7.08, 7.16, 7.24, 7.32, 7.4, 7.48, 7.56, 7.64, 7.72, 7.79, 7.87, 7.94, 8.01, 8.08, 8.15, 8.22, 8.29, 8.36, 8.42, 8.49, 8.56, 8.62, 8.68, 8.74, 8.8, 8.86, 8.92, 8.98, 9.04, 9.09, 9.15, 9.2, 9.26, 9.31, 9.36, 9.41, 9.46, 9.5, 9.55, 9.6, 9.64, 9.68, 9.73, 9.77, 9.81, 9.85, 9.88, 9.92, 9.96, 9.99, 10.03, 10.06, 10.09, 10.13, 10.16, 10.19, 10.22, 10.25, 10.28, 10.31, 10.33, 10.36, 10.38, 10.41, 10.43, 10.46, 10.48, 10.5, 10.52, 10.55, 10.57, 10.59, 10.61, 10.63, 10.65, 10.66, 10.68, 10.7, 10.72, 10.73, 10.75, 10.77, 10.78, 10.79, 10.81, 10.82, 10.83, 10.84, 10.85]
plt.plot(year, pop)
plt.show()
import matplotlib.pyplot as plt
life_exp = [43.828000000000003, 76.423000000000002, 72.301000000000002, 42.731000000000002, 75.319999999999993, 81.234999999999999, 79.828999999999994, 75.635000000000005, 64.061999999999998, 79.441000000000003, 56.728000000000002, 65.554000000000002, 74.852000000000004, 50.728000000000002, 72.390000000000001, 73.004999999999995, 52.295000000000002, 49.579999999999998, 59.722999999999999, 50.43, 80.653000000000006, 44.741000000000007, 50.651000000000003, 78.552999999999997, 72.960999999999999, 72.888999999999996, 65.152000000000001, 46.462000000000003, 55.322000000000003, 78.781999999999996, 48.328000000000003, 75.748000000000005, 78.272999999999996, 76.486000000000004, 78.331999999999994, 54.790999999999997, 72.234999999999999, 74.994, 71.338000000000022, 71.878, 51.578999999999994, 58.039999999999999, 52.947000000000003, 79.313000000000002, 80.656999999999996, 56.734999999999999, 59.448, 79.406000000000006, 60.021999999999998, 79.483000000000004, 70.259, 56.006999999999998, 46.388000000000012, 60.915999999999997, 70.198000000000008, 82.207999999999998, 73.338000000000022, 81.757000000000005, 64.698000000000008, 70.650000000000006, 70.963999999999999, 59.545000000000002, 78.885000000000005, 80.745000000000005, 80.546000000000006, 72.566999999999993, 82.602999999999994, 72.534999999999997, 54.109999999999999, 67.296999999999997, 78.623000000000005, 77.588000000000022, 71.992999999999995, 42.591999999999999, 45.677999999999997, 73.951999999999998, 59.443000000000012, 48.302999999999997, 74.241, 54.466999999999999, 64.164000000000001, 72.801000000000002, 76.194999999999993, 66.802999999999997, 74.543000000000006, 71.164000000000001, 42.082000000000001, 62.069000000000003, 52.906000000000013, 63.784999999999997, 79.762, 80.203999999999994, 72.899000000000001, 56.866999999999997, 46.859000000000002, 80.195999999999998, 75.640000000000001, 65.483000000000004, 75.536999999999978, 71.751999999999995, 71.421000000000006, 71.688000000000002, 75.563000000000002, 78.097999999999999, 78.746000000000024, 76.441999999999993, 72.475999999999999, 46.241999999999997, 65.528000000000006, 72.777000000000001, 63.061999999999998, 74.001999999999995, 42.568000000000012, 79.971999999999994, 74.662999999999997, 77.926000000000002, 48.158999999999999, 49.338999999999999, 80.941000000000003, 72.396000000000001, 58.555999999999997, 39.613, 80.884, 81.701000000000022, 74.143000000000001, 78.400000000000006, 52.517000000000003, 70.616, 58.420000000000002, 69.819000000000003, 73.923000000000002, 71.777000000000001, 51.542000000000002, 79.424999999999997, 78.242000000000004, 76.384, 73.747, 74.248999999999995, 73.421999999999997, 62.698, 42.383999999999993, 43.487000000000002]
gdp_cap = [974.58033839999996, 5937.0295259999984, 6223.3674650000003, 4797.2312670000001, 12779.379639999999, 34435.367439999995, 36126.492700000003, 29796.048340000001, 1391.253792, 33692.605080000001, 1441.2848730000001, 3822.137084, 7446.2988029999997, 12569.851769999999, 9065.8008250000003, 10680.792820000001, 1217.0329939999999, 430.07069159999998, 1713.7786860000001, 2042.0952400000001, 36319.235009999997, 706.01653699999997, 1704.0637240000001, 13171.638849999999, 4959.1148540000004, 7006.5804189999999, 986.14787920000003, 277.55185870000003, 3632.5577979999998, 9645.06142, 1544.7501119999999, 14619.222719999998, 8948.1029230000004, 22833.308509999999, 35278.418740000001, 2082.4815670000007, 6025.3747520000015, 6873.2623260000009, 5581.1809979999998, 5728.3535140000004, 12154.089749999999, 641.36952360000021, 690.80557590000001, 33207.0844, 30470.0167, 13206.48452, 752.74972649999995, 32170.37442, 1327.6089099999999, 27538.41188, 5186.0500030000003, 942.6542111, 579.23174299999982, 1201.637154, 3548.3308460000007, 39724.978669999997, 18008.944439999999, 36180.789190000003, 2452.210407, 3540.6515639999998, 11605.71449, 4471.0619059999999, 40675.996350000001, 25523.277099999999, 28569.719700000001, 7320.8802620000015, 31656.068060000001, 4519.4611709999999, 1463.249282, 1593.06548, 23348.139730000006, 47306.989780000004, 10461.05868, 1569.3314419999999, 414.5073415, 12057.49928, 1044.7701259999999, 759.34991009999999, 12451.6558, 1042.581557, 1803.151496, 10956.991120000001, 11977.57496, 3095.7722710000007, 9253.896111, 3820.1752299999998, 823.68562050000003, 944.0, 4811.0604290000001, 1091.359778, 36797.933319999996, 25185.009109999999, 2749.3209649999999, 619.67689239999982, 2013.9773049999999, 49357.190170000002, 22316.192869999999, 2605.94758, 9809.1856360000002, 4172.8384640000004, 7408.9055609999996, 3190.4810160000002, 15389.924680000002, 20509.64777, 19328.709009999999, 7670.122558, 10808.47561, 863.08846390000019, 1598.4350890000001, 21654.83194, 1712.4721360000001, 9786.5347139999994, 862.54075610000018, 47143.179640000002, 18678.314350000001, 25768.257590000001, 926.14106830000003, 9269.6578079999999, 28821.063699999999, 3970.0954069999998, 2602.3949950000001, 4513.4806429999999, 33859.748350000002, 37506.419070000004, 4184.5480889999999, 28718.276839999999, 1107.482182, 7458.3963269999977, 882.9699437999999, 18008.509239999999, 7092.9230250000001, 8458.2763840000007, 1056.3801209999999, 33203.261279999999, 42951.65309, 10611.46299, 11415.805689999999, 2441.5764039999999, 3025.3497980000002, 2280.769906, 1271.211593, 469.70929810000007]
# print(life_exp[-1])
# print(gdp_cap[-1])
plt.scatter(gdp_cap, life_exp)
plt.xscale('log')
plt.show()
# Import package
import matplotlib.pyplot as plt
pop = [31.889923, 3.6005229999999999, 33.333216, 12.420476000000001, 40.301926999999999, 20.434176000000001, 8.199783, 0.70857300000000001, 150.448339, 10.392226000000001, 8.0783140000000007, 9.1191519999999997, 4.5521979999999997, 1.6391309999999999, 190.01064700000001, 7.3228580000000001, 14.326203, 8.3905049999999992, 14.131857999999999, 17.696293000000001, 33.390141, 4.3690379999999998, 10.238807, 16.284741, 1318.683096, 44.227550000000001, 0.71096000000000004, 64.606758999999997, 3.8006099999999998, 4.1338840000000001, 18.013408999999999, 4.4933120000000004, 11.416987000000001, 10.228744000000001, 5.4681199999999999, 0.49637399999999998, 9.3196220000000007, 13.75568, 80.264543000000003, 6.9396880000000003, 0.55120100000000005, 4.9065849999999998, 76.511887000000002, 5.2384599999999999, 61.083916000000002, 1.4548669999999999, 1.6883589999999999, 82.400996000000006, 22.873338, 10.706289999999999, 12.572927999999999, 9.9478139999999993, 1.4720409999999999, 8.5028140000000008, 7.4837629999999997, 6.9804120000000003, 9.9561080000000004, 0.301931, 1110.3963309999999, 223.547, 69.453569999999999, 27.499638000000001, 4.1090859999999996, 6.426679, 58.147733000000002, 2.780132, 127.467972, 6.0531930000000003, 35.610177, 23.301725000000001, 49.044789999999999, 2.5055589999999999, 3.921278, 2.0126490000000001, 3.1939419999999998, 6.0369140000000003, 19.167653999999999, 13.327078999999999, 24.821286000000001, 12.031795000000001, 3.2700650000000002, 1.250882, 108.700891, 2.8741270000000001, 0.68473600000000001, 33.757174999999997, 19.951656, 47.761980000000001, 2.0550799999999998, 28.901789999999998, 16.570613000000002, 4.1157709999999996, 5.6753559999999998, 12.894864999999999, 135.03116399999999, 4.6279260000000004, 3.2048969999999999, 169.27061699999999, 3.2421730000000002, 6.6671469999999999, 28.674757, 91.077286999999998, 38.518241000000003, 10.642836000000001, 3.942491, 0.79809399999999997, 22.276056000000001, 8.8605879999999999, 0.19957900000000001, 27.601037999999999, 12.267493, 10.150264999999999, 6.1445619999999996, 4.5530090000000003, 5.4475020000000001, 2.0092449999999999, 9.1187729999999991, 43.997827999999998, 40.448191000000001, 20.378239000000001, 42.292929000000001, 1.1330659999999999, 9.0310880000000004, 7.5546610000000003, 19.314747000000001, 23.174294, 38.13964, 65.068149000000005, 5.7015789999999997, 1.056608, 10.276158000000001, 71.158647000000002, 29.170397999999999, 60.776237999999999, 301.13994700000001, 3.4474960000000001, 26.084662000000002, 85.262355999999997, 4.018332, 22.211742999999998, 11.746034999999999, 12.311143]
life_exp = [43.828000000000003, 76.423000000000002, 72.301000000000002, 42.731000000000002, 75.319999999999993, 81.234999999999999, 79.828999999999994, 75.635000000000005, 64.061999999999998, 79.441000000000003, 56.728000000000002, 65.554000000000002, 74.852000000000004, 50.728000000000002, 72.390000000000001, 73.004999999999995, 52.295000000000002, 49.579999999999998, 59.722999999999999, 50.43, 80.653000000000006, 44.741000000000007, 50.651000000000003, 78.552999999999997, 72.960999999999999, 72.888999999999996, 65.152000000000001, 46.462000000000003, 55.322000000000003, 78.781999999999996, 48.328000000000003, 75.748000000000005, 78.272999999999996, 76.486000000000004, 78.331999999999994, 54.790999999999997, 72.234999999999999, 74.994, 71.338000000000022, 71.878, 51.578999999999994, 58.039999999999999, 52.947000000000003, 79.313000000000002, 80.656999999999996, 56.734999999999999, 59.448, 79.406000000000006, 60.021999999999998, 79.483000000000004, 70.259, 56.006999999999998, 46.388000000000012, 60.915999999999997, 70.198000000000008, 82.207999999999998, 73.338000000000022, 81.757000000000005, 64.698000000000008, 70.650000000000006, 70.963999999999999, 59.545000000000002, 78.885000000000005, 80.745000000000005, 80.546000000000006, 72.566999999999993, 82.602999999999994, 72.534999999999997, 54.109999999999999, 67.296999999999997, 78.623000000000005, 77.588000000000022, 71.992999999999995, 42.591999999999999, 45.677999999999997, 73.951999999999998, 59.443000000000012, 48.302999999999997, 74.241, 54.466999999999999, 64.164000000000001, 72.801000000000002, 76.194999999999993, 66.802999999999997, 74.543000000000006, 71.164000000000001, 42.082000000000001, 62.069000000000003, 52.906000000000013, 63.784999999999997, 79.762, 80.203999999999994, 72.899000000000001, 56.866999999999997, 46.859000000000002, 80.195999999999998, 75.640000000000001, 65.483000000000004, 75.536999999999978, 71.751999999999995, 71.421000000000006, 71.688000000000002, 75.563000000000002, 78.097999999999999, 78.746000000000024, 76.441999999999993, 72.475999999999999, 46.241999999999997, 65.528000000000006, 72.777000000000001, 63.061999999999998, 74.001999999999995, 42.568000000000012, 79.971999999999994, 74.662999999999997, 77.926000000000002, 48.158999999999999, 49.338999999999999, 80.941000000000003, 72.396000000000001, 58.555999999999997, 39.613, 80.884, 81.701000000000022, 74.143000000000001, 78.400000000000006, 52.517000000000003, 70.616, 58.420000000000002, 69.819000000000003, 73.923000000000002, 71.777000000000001, 51.542000000000002, 79.424999999999997, 78.242000000000004, 76.384, 73.747, 74.248999999999995, 73.421999999999997, 62.698, 42.383999999999993, 43.487000000000002]
# Build Scatter plot
plt.scatter(pop, life_exp)
# Show plot
plt.show()
import matplotlib.pyplot as plt
plt.hist(life_exp)
plt.show()
plt.hist(life_exp, bins=5)
plt.show()
plt.hist(life_exp, bins=20)
plt.show()
life_exp1950 = [28.8, 55.23, 43.08, 30.02, 62.48, 69.12, 66.8, 50.94, 37.48, 68.0, 38.22, 40.41, 53.82, 47.62, 50.92, 59.6, 31.98, 39.03, 39.42, 38.52, 68.75, 35.46, 38.09, 54.74, 44.0, 50.64, 40.72, 39.14, 42.11, 57.21, 40.48, 61.21, 59.42, 66.87, 70.78, 34.81, 45.93, 48.36, 41.89, 45.26, 34.48, 35.93, 34.08, 66.55, 67.41, 37.0, 30.0, 67.5, 43.15, 65.86, 42.02, 33.61, 32.5, 37.58, 41.91, 60.96, 64.03, 72.49, 37.37, 37.47, 44.87, 45.32, 66.91, 65.39, 65.94, 58.53, 63.03, 43.16, 42.27, 50.06, 47.45, 55.56, 55.93, 42.14, 38.48, 42.72, 36.68, 36.26, 48.46, 33.68, 40.54, 50.99, 50.79, 42.24, 59.16, 42.87, 31.29, 36.32, 41.72, 36.16, 72.13, 69.39, 42.31, 37.44, 36.32, 72.67, 37.58, 43.44, 55.19, 62.65, 43.9, 47.75, 61.31, 59.82, 64.28, 52.72, 61.05, 40.0, 46.47, 39.88, 37.28, 58.0, 30.33, 60.4, 64.36, 65.57, 32.98, 45.01, 64.94, 57.59, 38.64, 41.41, 71.86, 69.62, 45.88, 58.5, 41.22, 50.85, 38.6, 59.1, 44.6, 43.58, 39.98, 69.18, 68.44, 66.07, 55.09, 40.41, 43.16, 32.55, 42.04, 48.45]
plt.hist(life_exp, bins=15)
plt.show()
plt.hist(life_exp1950, bins=15)
plt.show()
# Basic scatter plot, log scale
plt.scatter(gdp_cap, life_exp)
plt.xscale('log')
# Strings
xlab = 'GDP per Capita [in USD]'
ylab = 'Life Expectancy [in years]'
title = 'World Development in 2007'
# Add axis labels
plt.xlabel(xlab)
plt.ylabel(ylab)
# Add title
plt.title(title)
# After customizing, display the plot
plt.show()
# Scatter plot
plt.scatter(gdp_cap, life_exp)
# Previous customizations
plt.xscale('log')
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')
# Definition of tick_val and tick_lab
tick_val = [1000,10000,100000]
tick_lab = ['1k','10k','100k']
# Adapt the ticks on the x-axis
plt.xticks(tick_val,tick_lab)
# After customizing, display the plot
plt.show()