In [1]:
import matplotlib.pyplot as plt
In [2]:
year=[1950,1970,1990,2010]
In [3]:
pop=[2.519,3.692,5.236,6.972]
In [4]:
plt.plot(year,pop)
Out[4]:
In [5]:
plt.show()
In [6]:
plt.scatter(year,pop)
Out[6]:
In [7]:
plt.show()
In [1]:
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]
In [2]:
pop=[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]
In [3]:
# Print the last item from year and pop
print(year[-1])
print(pop[-1])
# Import matplotlib.pyplot as plt
import matplotlib.pylab as plt
# Make a line plot: year on the x-axis, pop on the y-axis
plt.plot(year,pop)
# Display the plot with plt.show()
plt.show()
In [4]:
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]
In [5]:
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]
In [6]:
# Print the last item of gdp_cap and life_exp
print(gdp_cap[-1])
print(life_exp[-1])
# Make a line plot, gdp_cap on the x-axis, life_exp on the y-axis
plt.plot(gdp_cap,life_exp)
# Display the plot
plt.show()
In [9]:
# Change the line plot below to a scatter plot
plt.scatter(gdp_cap, life_exp)
# Put the x-axis on a logarithmic scale
plt.xscale('log')
# Show plot
plt.show()
In [11]:
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]
In [12]:
# Build Scatter plot
plt.scatter(pop,life_exp)
# Show plot
plt.show()
In [13]:
# Create histogram of life_exp data
plt.hist(life_exp)
# Display histogram
plt.show()
In [14]:
# Build histogram with 5 bins
plt.hist(life_exp,bins=5)
# Show and clean up plot
plt.show()
plt.clf()
# Build histogram with 20 bins
plt.hist(life_exp,bins=20)
# Show and clean up again
plt.show()
plt.clf()
In [15]:
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]
In [16]:
# Histogram of life_exp, 15 bins
plt.hist(life_exp,bins=15)
# Show and clear plot
plt.show()
plt.clf()
# Histogram of life_exp1950, 15 bins
plt.hist(life_exp1950,bins=15)
# Show and clear plot again
plt.show()
plt.clf()
In [17]:
# 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()
In [18]:
# 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()
In [19]:
# Import numpy as np
import numpy as np
# Store pop as a numpy array: np_pop
np_pop=np.array(pop)
# Double np_pop
np_pop*=2
# Update: set s argument to np_pop
plt.scatter(gdp_cap, life_exp, s = np_pop)
# 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'])
# Display the plot
plt.show()
In [20]:
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']
In [21]:
# Specify c and alpha inside plt.scatter()
plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2,c=col,alpha=0.8)
# 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'])
# Show the plot
plt.show()
In [22]:
# Scatter plot
plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2, c = col, alpha = 0.8)
# 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()
In [ ]: