{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Matplotlib\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matplotlib\n", "\n", "Matplotlib is a package for creating data visualizations. It provides tools for creating a wide range of plots, and allows for a high level of customization. \n", "\n", "Python has two distint interfaces than can be used to generate plots. We will primarily by working with the `pyplot` API (Application Programming Interface). By convention, this is typically imported under the alias `plt`, as in the cell below." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import Gapminder Data \n", "\n", "For some of the examples in this lesson, we will be working with the gapminder dataset. This dataset contains socioeconomic data for 184 countries over a period of 219 years, from 1800 to 2018. The data you will be working with is contained in the file `gapminder_data.txt`. Please download this file into the same folder that contains this notebook. \n", "\n", "In the cell below, we use a package called Pandas to import the data. The data is stored in a pandas data type called a `DataFrame`. DataFrames are used to contain tabular data arranged in rows and columns. \n", "\n", "After importinng the data, we select only the records from 2018. Then to get a sense as to what this dataset looks like, we display the first 10 rows of the filtered dataframe. Don't worry about figuring out how this code works just yet. We will cover pandas in detail in a later lesson. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryyearcontinentpopulationlife_expgdp_per_capgini
0Afghanistan2018asia3640000058.7187036.8
1Albania2018europe293000078.01240029.0
2Algeria2018africa4200000077.91370027.6
3Angola2018africa3080000065.2585042.6
4Antigua and Barbuda2018americas10300077.62100040.0
5Argentina2018americas4470000077.01890042.4
6Armenia2018europe293000076.0866032.6
7Australia2018asia2480000082.94580032.3
8Austria2018europe875000081.84460030.5
9Azerbaijan2018europe992000072.31660032.4
\n", "
" ], "text/plain": [ " country year continent population life_exp gdp_per_cap \\\n", "0 Afghanistan 2018 asia 36400000 58.7 1870 \n", "1 Albania 2018 europe 2930000 78.0 12400 \n", "2 Algeria 2018 africa 42000000 77.9 13700 \n", "3 Angola 2018 africa 30800000 65.2 5850 \n", "4 Antigua and Barbuda 2018 americas 103000 77.6 21000 \n", "5 Argentina 2018 americas 44700000 77.0 18900 \n", "6 Armenia 2018 europe 2930000 76.0 8660 \n", "7 Australia 2018 asia 24800000 82.9 45800 \n", "8 Austria 2018 europe 8750000 81.8 44600 \n", "9 Azerbaijan 2018 europe 9920000 72.3 16600 \n", "\n", " gini \n", "0 36.8 \n", "1 29.0 \n", "2 27.6 \n", "3 42.6 \n", "4 40.0 \n", "5 42.4 \n", "6 32.6 \n", "7 32.3 \n", "8 30.5 \n", "9 32.4 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.read_csv('data/gapminder_data.txt', sep='\\t')\n", "df = df.loc[df.year == 2018,:].reset_index(drop=True)\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we do not yet know how to work with DataFrames, we will convert each column of the DataFrame into its own list. In the cell below, we create the following lists: `country`, `continent`, `population`, `life_exp`, `pcgdp`, and `gini`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "country = list(df.country)\n", "continent = list(df.continent)\n", "population = list(df.population)\n", "life_exp = list(df.life_exp)\n", "pcgdp = list(df.gdp_per_cap)\n", "gini = list(df.gini)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The numbers on the left of the displayed DataFrame above are row numbers. Data that was originally stored in row `N` of the DataFrame will now be split across the lists we created, but each value will be stored at index `N` of the relevant list. Let's confirm this by printing the element at index 7 in each list. We can see from the DataFrame above that these values will be for Australia. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Country: Australia\n", "Continent: asia\n", "Population: 24800000\n", "Life Expectancy: 82.9\n", "Per Capita GDP: 45800\n", "Gini Index: 32.3\n" ] } ], "source": [ "N = 7\n", "print('Country: ', country[N])\n", "print('Continent: ', continent[N])\n", "print('Population: ', population[N])\n", "print('Life Expectancy: ', life_exp[N])\n", "print('Per Capita GDP: ', pcgdp[N])\n", "print('Gini Index: ', gini[N])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we understand how our data is stored in the lists, let's move on to see how to visually represent aspects of this data set. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scatter Plots\n", "\n", "Matplotlib contains a function called `scatter()` that can be used to generate scatter plots. This function has two required parameters named `x` and `y`. These parameters are expected to be lists containing the x and y coordinates of the plots to be pointed. \n", "\n", "Plots in Matplotlib are built up as a series of layers. We create a base plot, and then we can add additional plots on top of that, or add things like titles or axis labels. When we are done creating our plot, we can display it using `plt.show()`.\n", "\n", "The cell below creates a simple scatter plot of life expectancy against per capita GDP." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(x=pcgdp, y=life_exp)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Axis Labels and Title\n", "\n", "We can use the `xlabel()`, `ylabel()`, and `title()` functions to add labels to the axes, or a title to the plot. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(x=pcgdp, y=life_exp)\n", "plt.xlabel('Per Capita GDP')\n", "plt.ylabel('Life Expectancy')\n", "plt.title('Life Expectancy vs Per Capita GDP (2018)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Changing the Figure Size\n", "\n", "We can use the `figure()` function to change the size of our plot. We do this by passing in a list of two numbers for the `figsize` parameter. The first number controls the width of the figure while the second number controls the height." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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wa7NNe4ZqJb1lmSgX11JggNdZxiLLY7KfNEafZXGoj1xyLymJrDIgl1RLemtGolzSGPa4pLK4vvzSe+g0eU32k/rpsywW1cgl95Jq0T1mscdXS3pL2jfDLFXNo1bT/OCCAW5cciD3LX0DNy45MLGMSduLLM/DAqU++iyLRYFcci+ptjzpXvf46KREuUn3VH3Y9f7ATXplw3r17UWW12GBUj99lsWiQC65l1SLLq1cVs9KZpWrn8XVjKsF5np/4AYSyt6TsgWgSDSDXOfQZ1ksCuSSe9VmJqtsygY2SdCJS5QrPWZ9Qs04KTBXa5qPC8jTbQEoEs0g1zn0WRaLkt0k99LOTBaXoLP40hXgMLHeN2wrT9qpd3KZxYvmxSbaTbrHJgNVln2G2SbN6nmZNGa6NINc59BnWSzmBeirW7hwoQ8PD7e7GJJz+y+9LnU2+EB/HzcuOXCT4A9RzaNaE/3QyCifvHhFbD936XmT7LLkqk2Go0E0TO2+pW9IVXYR6XxmtszdF6Y5Vk3r0jHqScQpHVvZZ562n73eJvmSbul71BhkkdZR03rBaV3ujaqN2447tqSRecgbne89rmm+0/oeNQZZpLXUtF5gjTQLN/v1q11EtPoiI+58QNTstL7sfvk5SlPGuGOATV6rNIvbQI332ukXX0ldHLW6HURko3qa1hXIC6ydP5i1LiLquchoZmD73NBtnP+7B6f0Q/f2GFtuNpM14xNTnj9NGasdA1Ey0OjY+CZTsSa9ZjdQHoDI9KmPvEu0c9KGWhOjpJ04pdmLmFx/16pNgsjEpLPl5jM3DFErz8itVcZaS5PeuORABvr7Yl9zbHyi44aYpdEteQAieaFAXmDt/MGsdRGR9iKj2VNB1nNxk+bY6RxTrpumt9QYZJHWUiAvsHb+YNa6iEh7kdHsNbzrubhJc+x0jqnULdNbNjISQEQap0BeYK34wUwaRlTrIiLtRUaz1/Cu5+ImzbGNHhOnm5qWK2fcUxCXTpWHoZYaflZwjQydSivNMKKkJLW0M0PFDceqtoZ3rfdaz4xUaY5t5Jj+Wb089fd1G2aTAzUti3SivAy1VNZ6TuVhiFKrsuIr32vSWPAiZT3n4fMTkWxl+RtZT9a6auQ5lJervFZlxVe2KiT95yhS03SWLSUikg95We5VfeQ5U5rHu5mZ3I1qV1a8sp5FpAjyMtRSgTxHSjXxuMU4oPVXee0KqMp6FpEiyEulI9OmdTM7Hng/Ue7SbcB7ge2BHwNzgN8D73b3Z7MsR1HEjaku1+qrvHYuZaim6e6gXAIpsrws95pZspuZDQC/AXZ393Ezuxj4OfB64HJ3/7GZnQmscPczqj1XtyS7JU1tCVGi1+lHztePnHSMdq8VIJJneZqidSbQZ2YzgVnAo8CBwKVh/3nAYMZlKIxqNW5HK0dJZ2n2rH4i3SqzQO7uo8BXgAeJAvgaYBkw5u7rwmEPA7HRycyONbNhMxtetWpVVsXMlcWL5mEJ+wYKlLEtkkZeMn5Fii6zQG5ms4E3AbsAOwBbAofEHBrbmuzuZ7n7QndfOHfu3KyKmSuDCwZ41347bhLM85axnYeZjKT48pLxK1J0WTatvwa4z91XufsEcDnwCqA/NLUDPB94JMMyFM4pg3ty+pHzc5ux3ezVyqR75SXjV6TossxafxDYz8xmAePAQcAwcD1wBFHm+jHATzIsQyFlnbFdT6Zw5bFrn11XdVnPdpRRiikvGb8iRZdZIHf3m8zsUqIhZuuAEeAs4Crgx2Z2Stj2/azKIJuqZ9a4uGOTNLNfMy8z20n2NMxQZPoyHUfu7icCJ1Zs/hOwb5avK8mqZQpX/qDWGtderpn9mvWUUSJqwRDpXpprvcvUkymctpbd7H5NZTPXRy0YIt1NU7R2mXoyhZOO7e/rzTQZT9nM9dF4bJHupkDeZerJFE469qTD9uDGJQdy39I3cOOSA5te61M2c33UgiHS3dS0Xqei90XWkyncrqxiZTPXJ2kNd7VgiHSHzOZab6a8zLXe6rmhi37RIK2hOctFOk+e5lrvKK3si9TEK5KWln0V6W5qWq9DM/oi09ayNQRL6qHx2CLdS4G8DtPti6xnmFC9Fw15bIbPY5lERDqNmtbrMN1s6nqa5usZgpXHZvg8lqlULi34IiKdRDXyOkw3mzqpNj06Ns4uS66if1Yv7rBmfIJt+3rp7TEmJjcmIyZdNOSxGT6PZdLEKSLSiRTI6zSdvsikpnmI1nJdvXZiw/2x8QlmADMM1jv0mPGWfeJfO4/jiPNYpjxeXIiITJea1lsormm+mvVEQRxg0p3Llo3GNgXncSa0PJYpjxcXIiLTpUDeQpXDhOqV1J/e7JnQmtGPXK1M7eqnzuPFhYjIdKlpvcXKm+b3X3pd1aVB48TVHqfTdz80MspJV97B2HjUrL/lZj08u249E6EpoNF+5KQyAW3rp168aF7sxCl5mPpVGf4i0ijN7NZGcTNy1TLQ38eNSw5s2usvvmTFhqDditdNunhp5vuqJo8BUzOztU4eP3+ROPXM7KYaeRuVfkBO/ukdUxLdkiTVHhv9cTrt6pWpgjg0rx+53f3UeZw4RUl4raFRC9Kp1EfeZoMLBhj5wsEctd+Om/Sb9/YY/X29VafdnM547XqCZ7P6kav1UxdpjHczy9rui5tuoeVepVOpRp4TpwzuycKd5tRds55Oba7acLhyzexHTuqnPmC3uYWpLTW7ZqfVy1pDF0zSqVQjz5HBBQN1r/Od5scpqfa4eNE8emdsmj/fM6N2S0Cjkhb4uP6uVYWpLTW7Zqf111tDoxakU6lGXmBDI6PMMGMyJmGx9OOUpvZYnrU+e1YvJx66R6a14Lh+6uMvWh57bB5rS82u2XXr+uutTjzL86gFkelQIC+ooZFRFl+6IjaIAzz9zLoNP5TVmt7zkvxVpOblLMqal8+hVdqReNatF0zS+RTIM5J1bePkn94xZR72SmPjE1WHtuWtpluk2lKRyppX7crU77YLJukOCuQZSFPbmG6gTzNcbXxiEjOIq7TnraZbpNpSkcqaV0o8E2keBfIM1KpttLJZ0R16Z9iU8eJ5rT0WqbZUpLLmUZG6UkTyTlnrGahV22hG1nN/X2/qY7faYuYmWeIKQtJOytQXaR7VyDNQq7bRaLNieXN8/6xeZhCtkFbL2NoJRr5wcIojRVpD3RMizaNAnoFayVCNNCtWNseX+sj7emfw94n17NDfx+qnn2HtxKahXc2VkkfqnhBpDjWtZ2BwwQBv2WeAHosmW+kx4y37bPzRaqRZMa45HuDvE+s5/cj53LjkQL54+D+ruVJEpMuoRp6BoZFRLls2umGM96Q7ly0bZeFOc6bUQtI2Kw6NjCZOperheRp53ixULovaiglmRES6mZYxzcD8k6/ZEMjKNbJUZ5qlTg24b+kb6i1m0yUti9rbY5x2xF4K5iIiKdWzjKma1ptsaGQ0NohDY2Nkk5rUy+WlDzxpWdSJSc/lnOkiIp1ATetNNDQyyicvXpG4v5GAW2t1smp94M2YXa6e56h2oaKJPkREsqFA3iSlJvCkuc+BhpLOehIWRYGoqT4psDZj0pl6n6Pasqh5aTUQEek0alpvklpN4LNn9TbUR1ztwqDaUqdJk8588uIVG5YxraXeiWuSlkXt7bGqrQZxS6yKiEg6qpE3SbWmYwPe8M/bN/S8/X29iYlzjZRn0j11zbzeiWvqXRa1HStgiYh0mswCuZnNAy4q27Qr8AWgH/gAsCps/4y7/zyrcrRKtWZlhynDz9IaGhnl6WfXbbK9d0ZyDTdNedKuMlVr4pqk/vO077FdK2CJiHSSzJrW3X2lu8939/nAPsBa4Iqw+/TSvk4I4hA/yUu5eudSh5AFHrNU6VZbzKwZ6GqVJ03yWbWJa0q16dGxcZyNtel6msa1ApaIyPS1qmn9IOBed3/AbNM+1KKqrJG+ZZ8Brr9rVWJNuN4AlXT8WIolTEuB/pMXr4jtZ69Vqy5/jrj9+y+9btq1aa2AJSIyfa0K5G8HLiy7/xEzOxoYBj7p7qtbVI6mievfvWzZKKcevienXb0yNkCZRZPFrBmfSDUcbLqBrvTcSfO+p+mjTmoqb0Ztutac9CIiUlvmWetmthlwGHBJ2HQG8EJgPvAo8NWExx1rZsNmNrxq1aq4Q9qqWv9uUrP2eoex8YnUTdHNWOpxcMEApx6+Z+wyptNZTjXpYqKe2nS1somISDqtqJEfAvze3R8DKP0LYGbfA34W9yB3Pws4C6IpWltQzrpUq5HWatYuqdUU3ay507OoVTerNq0VsEREpqcVgfwdlDWrm9n27v5ouPtm4PYWlKHpajV7Dy4Y4PiLltd8nlpBM8tAN52m+zws0CIiIhkHcjObBbwW+GDZ5i+b2XyiUVn3V+wrjDQ10mpDwMqPaZfp1qpVmxYRab9MA7m7rwWeU7Ht3Vm+ZqukqZHGBcpKB+w2N/OyJsmyVt2Med5FRKQ2zeyWofJAmVQzv/6u9ibyldeqS8H3+IuWTyv4asY2EZHW0VzrDYqbEOXjFy1n/snXTMlEH1wwwI1LDiRp9HxeJj9pxgQvJdPJhhcRkfookDcoaZGUsfGJ2ADYjOFaWWpm8NWMbSIiraNA3qBqQSkuADZjTHgt01lJrJnBN+8XLSIinUSBvEG1glJlAMx68pPpNo03M/i24qJFREQiSnZrUK2M9LgAmOVwremuJNbM6VI1xlxEpHUUyBPUGj5V+vvkn97B6opFTNpR+5xu03izg2+aixYNURMRmb6uDeTVgkja4VOlYJWHgNSMlcRaOcGLhqiJiDRHV/aR1+pPrjeDuzTE7PQj5wNw/EXL6042m66i9UtriJqISHN0ZSCvFUSSmqNHx8YTA3Qzx2E3omgriWmImohIc3Rl03qtIFJtjvSkJuDpJps1Q5HmPm9GV4CIiHRpjbzWUKuk9cRL4pqAVcOsT9G6AkRE8qorA3mtIFLeTJ2kMkBrEpT6FK0rQEQkr7qyaT3NUKtSM/X+S69L1QRc7zjsPGS6t1uRugJERPKqKwM5pA8iaQP04IIBhh/4Kxfe9BCT7vSY8ZZ94l9DQ69ERKRZzN3bXYaaFi5c6MPDw217/VLteXRsnB6zDYF60p2BUJsGYgN+XHNxUi1/oL+PG5ccmO2bERGR3DOzZe6+MM2xXVsjr0cpEJcH6slwAVSqTW8+c0Zs1vonL14x5Tmg+YlxaqYXEeleCuTB0MgoJ115B2Pj0XSrs2f1cuKhe0zpT0+aV318YjJx36T7Js3mzRx6pWZ6EZHu1pVZ65WGRkZZfMmKDUEcYPXaCRZfumLDhC7TGUZWOVytmUOvNEOaiEh3UyAnCoYT6zfNFZiY9A0BsVZtefas3qpjz8svBOKGt5Wa4T83dFtdZW/l+PXprHcuIiLZUCCnetAr7as2SUxfbw8nHroHpx6+Jz1mscdUXggMLhjggN3mTtk26c6PfvfghmCeJnC2avx6u6egFRGReArkVA96pX2VtehSwC6fyGRwwQBffdteqZvNL7zpodjXvPCmh1IHzlbNkKYmfBGRfFKyG1EwXHzJik2a13t7bEpATDP2vJ51vScThv5NunPSlXekmru92euIJ9EUtCIi+VQzkJvZV4AfuPsdLShP22y5+cwpyW5mU/vI6wmMaSebKY1Fr2QwpSzl4gJnK2ZI0yInIiL5lKZp/S7gLDO7ycyOM7Ntsy5UK5WasCsDZym+ZtkX/I6XvSB2+6zNkpPm2hU4tciJiEg+1Qzk7n62u+8PHA3sDNxqZheY2QFZF64Vqo0PL8mqL/iUwT05ar8dN/S395hx1H47svbZ5PK0K3BqkRMRkXxK1UduZj3AbuH2BLAC+ISZfdDd355h+TKXto83q77gUwb35JTBPadsu/6uVbHN2LNn9bY1cGqRExGR/KlZIzez/wRWAq8Hvuju+7j7l9z9UGBB1gXMSmloV9qZ5lvZpJ3UjH3ioXu0rAzVaDy5iEh+pKmR3w58zt3Xxuzbt8nlaYnKaU1raXVfcKsy0RuhKWFFRPIlTSBfDfSW7phZP/Bqdx9y9zWZlSxD1frF+/t6mZhcz9Ohn7q/r5eTDtuj5UEqr83Y1caT57G8IiKdLk28DEvvAAAejUlEQVQgP9HdryjdcfcxMzsRGMquWNmq1t/9zLr1UwLVM+vWT9nf7SuNaTy5iEi+pBl+FndMoSeSServ7jGLrW2e/NNoCL2mKW3dlLAiIpJOmoA8HBLevg048FFgWaalytjiRfM26SPv6+1JbG5fvXZiQ008ac3x4y9azg79fRyw21yuv2tVW2vsWbYaJJ07jScXEWmPNDXyjwLPAhcBlwB/Bz6cZaGyljQmeqBKrbIUGONMum+oof/odw+2tcaedauBxpOLiOSLecJ833mycOFCHx4ezvx1hkZG+fhFy2P3GcnTlNYy0N/HjUsOnGbp0tl/6XWxZWxlGUREZHrMbJm7L0xzbJpx5C82s7PM7Bozu650m34x82dwwQD9fb2x+0pN1NXWHE/SykQwJaOJiHSXNE3rlwAjwOeAxWW3qsxsnpktL7v9zcw+bmZzzOxXZnZ3+Hf29N5Cc5102B6Jc4qXmpWT1hxP0spEMCWjiYh0lzSBfJ27n+HuN7v7stKt1oPcfaW7z3f3+cA+wFrgCmAJcK27vwi4NtzPjVp9wIMLBlhfR3dEqxPBtLiJiEh3SZO1/lMz+xBREH6mtNHd/1rH6xwE3OvuD5jZm4BXh+3nATcAn67juTJTme19+pHzY5O4kvrKe8x4x8te0Nas9TzPCiciIs1XM9nNzO6L2ezuvmvqFzE7B/i9u3/LzMbcvb9s32p3r9q83opkt7hpW/t6e2Izsus5VkREpF71JLvVrJG7+y7TLMxmwGHACXU+7ljgWIAdd9xxOkVIpZ6pR1XrFRGRvEi7jOlLgN2BLUrb3P2/Ur7GIUS18cfC/cfMbHt3f9TMtgcej3uQu58FnAVRjTzlazWs3mzvvM6FLiIi3SXN8LMTgW+G2wHAl4lq2Gm9A7iw7P6VwDHh72OAn9TxXJlRtreIiBRRmqz1I4iS1f7s7u8F9gI2T/PkZjYLeC1wednmpcBrzezusG9pXSXOSFy2txHNjKY1t0VEJK/SNK2Pu/t6M1tnZtsQNYWnSnQLa5g/p2LbX4guDHKlvN97dGwcI5pYHrTmtoiI5FeaGvlwWIP8e0SLpfweuDnTUrXJ4IIBblxyIAP9fVR2ypcS3yoNjYyy/9Lr2GXJVaq5i4hIy6XJWv9Q+PNMM/slsI2735ptsdorbeJb5TA01dxFRKTV0iS7XVv6293vd/dby7d1oqQEtxlmU2rc1YasiYiItEJiIDezLcxsDrCdmc0Oc6TPMbOdgR1aVcB2SFocZdJ9ypKgzVygRE30IiLSiGo18g8S9YnvFv4t3X4CfDv7orVPtcVRymvczRqylvUa4iIi0rkSA7m7fz3M6vYpd9/V3XcJt73c/VstLGNbVFscpVTjbtYCJWqiFxGRRqXJWl8fstYBCM3sH6r2gE5Rq8Zda6W0tLSGuIiINCrNOPIPuPuGpnR3X21mHwC+k12xWqdyxbPyOdMXL5oXuzhKeY27GVO1Jq2mplnlRESkljQ18hlmGzuLzawH2Cy7IrVOrb7pemvcjSasaQ1xERFpVJoa+dXAxWZ2JtFkZ8cBv8y0VC2SZsWztDXu6Ywp12pqIiLSqDSB/NNEGez/RjT9+DXA2VkWqhWGRkZjm7MhuW+6vBm+f1Yv7rBmfIId+vt4+pl1qZdBjdNIE321bgEREekOaWZ2W29m5wLXuXtHpFEPjYyy+JIVifvj+qYra9yr105s2Jd0QQDZJaxpVjkREYF0M7sdBiwnNKeb2XwzuzLrgmXppCvvYGJ9/NCypL7puGb4NLJKWNOQNRERgXTJbicC+wJjAO6+HNg5wzJlbmx8InFfUjJbIzXrLBPWNGRNREQgXSBf5+5rMi9JTiQ1S6epWc+e1TvtMeVpNWtWORERKbY0yW63m9k7gR4zexHwMeC32RYrW7Nn9U7p4y7fniRuTHm5vt4eTjx0j5b1T6cZ4y4iIp0vTY38o8AewDPAhcDfgI9nWaisnXjoHvT2TJ1HvbfHOPHQPRLHgleOKZ89q5f+vt6W1L7jJI1xB7T4iohIFzFPmE98kwPNtgHc3Z/MtkibWrhwoQ8PDzf1OeOGbgGxtdxWB+lGVWayQ7HKLyIiETNb5u4LUx1bK5Cb2UuBc4Ctw6Y1wPvcfdm0SlmHLAJ5uVJQTxpGNntWLyNfODiz12+W/ZdeF/seBvr7uHHJgW0okYiINKKeQJ6maf37wIfcfWd33xn4MPCDaZQvV8qnaU2yeu1ErpuoS90B9U5wIyIixZcmkD/p7r8u3XH33wAtb17PStrx4Xkdn53mQkSZ7CIinStN1vrNZvZdokQ3B44EbjCzvQHc/fcZli9zaWurea3V1roQUSa7iEhnSxPI54d/T6zY/gqiwF7oztekJUQrOVEfdN7mM692gTGg+ddFRDpemrnWD2hFQdpl8aJ5fPyi5amOzeN85kkXIkpwExHpDmnmWv+hmW1bdn8nM7s222K1RilbvR55m89ca5mLiHS3NE3rvwFuMrNPAAPAYuCTmZaqBeLGXJfr6+1J3Jen/nKtZS4i0t3SNK1/18zuAK4HngAWuPufMy9ZxqoliZX6lpPGluctC7yRtcxFRKQz1AzkZvZu4PPA0cA/Az83s/e6e/KC3gWQVKs2mNK3XFlr751hrH12HbssuarQtd+4me2K+D5ERLpdmqb1twD/4u6PAxea2RXAeWzMZi+kpCSx8tp2ZbP1tn29PP3sug0LruQx+S2Nym6For4PERFJkezm7oMhiJfu30y0PnmhVUsSK1845bSrV7J40TzuW/oGttx8JhOTU6e0TZP8lrQQS7vEdSvkLYlPRETSSayRm9nF7v628PeX3P3TZbt/BuR/8vEqkpLEgMTaalJzfLXkt7ja7+JLVnDyT+9gbO1EW5q1G3kfIiKST9Wa1l9U9vdrgfJAPjeb4rRWXJLY/kuvS6ytpmmOrxRX+51Y721tnm/kfYiISD5Va1qvtixaurVPCyTNwiONjNlOU8ttdbO2xp6LiHSOajXyWWa2gCjY94W/LdwKW3VLuw55pR36+xoas512CthWNmtr7LmISOdIXI/czK6v9sBWTt3arPXI4yaB6evtYYveGRuauuP09fZw6uF7NhToak08U6IpVUVEpKSe9cgTa+SdOMd6UrZ2tSAbt/BIPWOwk4awlWe/q1lbREQalWYcecPMrB84G3gJUb/6+4BFwAeAVeGwz7j7z7MsR0m9zddxteRGxmBXJtVpMhYREWmWTAM58HXgl+5+hJltBswiCuSnu/tXMn7tTaTtr4bkWnK1Mdhpg7GmVBURkWapOSFMo8xsG+BVwPcB3P1Zdx/L6vXSiMvWjtNjltgnrjHYIiKSJ2mWMTUzO8rMvhDu72hmaWZ225Wo+fwHZjZiZmeb2ZZh30fM7FYzO8fMZie87rFmNmxmw6tWrYo7pG6DCwY49fA9Gejvw6oct949scacNNZaY7BFRKQd0tTIvwO8HHhHuP8k8O0Uj5sJ7A2c4e4LgKeBJcAZwAuJ5mp/FPhq3IPd/Sx3X+juC+fObd78M4MLBli8aF7VwFttn8Zgi4hInqTpI3+Zu+9tZiMA7r469HfX8jDwsLvfFO5fCixx98dKB5jZ94ime22ZNOuQVwvKGoMtIiJ5kiaQT5hZD2E2NzObC6yv9SB3/7OZPWRm89x9JXAQ8Acz297dHw2HvRm4vcGyN6TaOuQGvGWf2oloSlYTEZG8SBPIvwFcATzXzP4PcATwuZTP/1Hg/FCD/xPwXuAbZjaf6MLgfuCD9RZ6OqolpTlw4U0Pcf7vHlRNW0RECqHa6me7uPt97n6+mS0jqlEbMOjud6Z5cndfDlTOTPPuhkvbBLWGoE2Gme60RreIiBRBtWS3SwHM7Fp3v8vdv+3u30obxPMq7RA00BrdIiKSf9Wa1meY2YnAi83sE5U73f0/sytWdkq165OuvIOx8eT51Usqm+I1K5uIiORJtRr524G/EwX7rWNuhTW4YIDlJx7M146cv2FMeY/FjywvH4pWyngfHRvH2dj8PjQy2pqCi4iIVKi2aMpK4Etmdqu7/6KFZWqZ8uzzpJXRyoeiNWN6VhERkWaqlux2lLv/CNjdzP6pcn9Rm9YrlTeVb9vXyxa9MxhbOxHbbK7pWUVEJG+q9ZGXplPdKmZf/CLmBVNZCx8bn6Cvt4fTj5wfW8NOynhv1vSs6n8XEZF6VWta/2749+TKfWb28SwL1QpDI6N88uIVG4ablVRrKl+8aF7N5vfplKfe5VFFREQaXf1skyz2IikFzcogXpLUVF656MpAf1/iKmn1qtb/LiIikqTR9cirLR6We9WmaYXqTeVZTc+q/ncREWlEozXyQveRV5vZrV0rmWl5VBERaURiIDezJ83sbzG3J4EdWljGpksaMw7pFk3JgpZHFRGRRlRLdiv0pC/VJPWNA1x080Ms3GlOy4O5lkcVEZFGNNpHXmgDVRZOmVjvbZvgRcujiohIvRrtIy+0Ws3VSjATEZGi6LpAXpp0pRolmImISFF0VdP60Mgoiy9dwcRkch957wxTgpmIiBRGV9XIT/7pHVWDeH9fL6e9dS/1U4uISGF0VY189drk9ccHlCUuIiIF1FU18mq0triIiBRRVwXy/r7eqvs1t7mIiBRNVwXykw7bo+Yb1tAzEREpkq4K5IMLBth2VvVauYaeiYhIkXRVIAcYq5Lw1tujoWciIlIsXRfIq9a4C72mm4iIdKOuC+Rxq4yVlOZZFxERKYquGkcOG1cZ+/hFy2P3j46Ns//S6xgdG6fHjEl3jTEXEZHc6qpAXppnPWnls5LS/tJyp6Ux5oCCuYiI5ErXNK0PjYxywuW31QziSTTGXERE8qhrAvlpV69kfGJyWs+hMeYiIpI3XRPImxGENcZcRETypmsC+XSDcF9vj8aYi4hI7nRNIK827KyWgf4+Tj18TyW6iYhI7nRN1nopCJ905R2MjSfP7tZjxnp3dtCQMxERKYCuCeQQBfPBBQMMjYwmjiOfdOf+pW9occlEREQa0zVN6+UGFwzQYxa7L36riIhIPnVlIIeNk71UcqIx5yIiIkWQaSA3s34zu9TM7jKzO83s5WY2x8x+ZWZ3h39nZ1mGJP19ycuZauIXEREpiqxr5F8HfunuuwF7AXcCS4Br3f1FwLXhfksNjYzy9LPrEvdr4hcRESmKzAK5mW0DvAr4PoC7P+vuY8CbgPPCYecBg1mVIclpV69kYjJ5zVJN/CIiIkWRZY18V2AV8AMzGzGzs81sS+B57v4oQPj3uXEPNrNjzWzYzIZXrVrV1IJVq3Fr4hcRESmSLAP5TGBv4Ax3XwA8TR3N6O5+lrsvdPeFc+fObWrB+mfF94/PMDTxi4iIFEqW48gfBh5295vC/UuJAvljZra9uz9qZtsDj2dYhlgJCetss0XvhiBeWvL0kbFxTQ4jIiK5lVmN3N3/DDxkZqV26oOAPwBXAseEbccAP8mqDEnWJMzsVtpevuSps3E9cg1LExGRvMk6a/2jwPlmdiswH/gisBR4rZndDbw23G+ppGS20va4JU+1HrmIiORRplO0uvtyYGHMroOyfN1aFi+axwmX3zYlWJcnuSUlw2lYmoiI5E1Xzuw2uGCAUw/fk4H+PoxNVzerVWMXERHJi65aNKVcaQGVOLVq7CIiInnRtYG8mlKAV9a6iIjkXVcH8mpDzKrV2EVERPKiawN5aYhZqfm8NMQMaDiAa+y5iIi0Wlcmu0Hzh5hp7LmIiLRD1wbyZg8x09hzERFph64N5M0eYqax5yIi0g5dG8gXL5pHX2/PlG3TGWKmseciItIOXZXsVpmM9pZ9Brj+rlVNSU7T2HMREWmHrgnkcVnqly0bbdqypRp7LiIi7dA1gbxaMlqzgq3GnouISKt1TR+5ktFERKQTdU0gVzKaiIh0oq4J5M3OUhcREcmDrukjVzKaiIh0oq4J5KBkNBER6Txd07QuIiLSiRTIRURECkyBXEREpMAUyEVERApMgVxERKTAFMhFREQKTIFcRESkwBTIRURECkyBXEREpMAUyEVERApMgVxERKTAFMhFREQKTIFcRESkwBTIRURECkyBXEREpMAUyEVERApMgVxERKTAFMhFREQKTIFcRESkwDIN5GZ2v5ndZmbLzWw4bDvJzEbDtuVm9vosyyAiItLJZrbgNQ5w9ycqtp3u7l9pwWuLiIh0NDWti4iIFFjWgdyBa8xsmZkdW7b9I2Z2q5mdY2az4x5oZsea2bCZDa9atSrjYoqIiBRT1oF8f3ffGzgE+LCZvQo4A3ghMB94FPhq3APd/Sx3X+juC+fOnZtxMUVERIop00Du7o+Efx8HrgD2dffH3H3S3dcD3wP2zbIMIiIinSyzQG5mW5rZ1qW/gYOB281s+7LD3gzcnlUZREREOl2WWevPA64ws9LrXODuvzSzH5rZfKL+8/uBD2ZYBhERkY6WWSB39z8Be8Vsf3dWrykiItJtNPxMRESkwBTIRURECkyBXEREpMAUyEVERApMgVxERKTAFMhFREQKTIFcRESkwBTIRURECkyBXEREpMAUyEVERApMgVxERKTAFMhFREQKTIFcRESkwBTIRURECkyBXEREpMAUyEVERApMgVxERKTAFMhFREQKTIFcRESkwBTIRURECkyBXEREpMBmtrsArTQ0MsppV6/kkbFxdujvY/GieQwuGGh3sURERBrWNYF8aGSUEy6/jfGJSQBGx8Y54fLbABTMRUSksLqmaf20q1duCOIl4xOTnHb1yjaVSEREZPq6JpA/MjZe13YREZEi6JpAvkN/X13bRUREiqBrAvniRfPo6+2Zsq2vt4fFi+ZtuD80Msr+S69jlyVXsf/S6xgaGW11MUVEROrSNclupYS2pKx1JcOJiEgRdU0ghyggJwXlaslwCuQiIpJXXdO0XouS4UREpIgUyAMlw4mISBEpkAdpkuFERETypqv6yKuplQwnIiKSRwrkZaolw4mIiOSRmtZFREQKLNMauZndDzwJTALr3H2hmc0BLgJ2Bu4H3ubuq7Msh4iISKdqRY38AHef7+4Lw/0lwLXu/iLg2nBfREREGtCOpvU3AeeFv88DBttQBhERkY6QdSB34BozW2Zmx4Ztz3P3RwHCv8/NuAwiIiIdK+us9f3d/REzey7wKzO7K+0DQ+A/FmDHHXfMqnwiIiKFlmmN3N0fCf8+DlwB7As8ZmbbA4R/H0947FnuvtDdF86dOzfLYoqIiBRWZoHczLY0s61LfwMHA7cDVwLHhMOOAX6SVRlEREQ6XZZN688DrjCz0utc4O6/NLNbgIvN7H8BDwJvzbAMIiIiHS2zQO7ufwL2itn+F+CgrF5XRESkm5i7t7sMNZnZKuCBJjzVdsATTXieTqHzsZHOxUY6F1PpfGykczFVludjJ3dPlSBWiEDeLGY2XDYxTdfT+dhI52IjnYupdD420rmYKi/nQ3Oti4iIFJgCuYiISIF1WyA/q90FyBmdj410LjbSuZhK52MjnYupcnE+uqqPXEREpNN0W41cRESko3RNIDez15nZSjO7x8w6ZulUM3uBmV1vZnea2R1m9r/D9jlm9iszuzv8OztsNzP7RjgPt5rZ3mXPdUw4/m4zO6Zs+z5mdlt4zDcszPKTV2bWY2YjZvazcH8XM7spvK+LzGyzsH3zcP+esH/nsuc4IWxfaWaLyrYX5ntkZv1mdqmZ3RW+Hy/v8u/F8eH/yO1mdqGZbdFN3w0zO8fMHjez28u2Zf59SHqNdko4F6eF/yu3mtkVZtZftq+uz7yR79W0uHvH34Ae4F5gV2AzYAWwe7vL1aT3tj2wd/h7a+CPwO7Al4ElYfsS4Evh79cDvwAM2A+4KWyfA/wp/Ds7/D077LsZeHl4zC+AQ9r9vmuck08AFwA/C/cvBt4e/j4T+Lfw94eAM8PfbwcuCn/vHr4jmwO7hO9OT9G+R0TLBL8//L0Z0N+t3wtgALgP6Cv7Trynm74bwKuAvYHby7Zl/n1Ieo0cnouDgZnh7y+VnYu6P/N6v1fTfj/tPqEt+tBeDlxddv8E4IR2lyuj9/oT4LXASmD7sG17YGX4+7vAO8qOXxn2vwP4btn274Zt2wN3lW2fclzebsDzgWuBA4GfhR+VJ8r+g274LgBXAy8Pf88Mx1nl96N0XJG+R8A2RIHLKrZ36/diAHiIKADNDN+NRd323QB2Zmrwyvz7kPQa7b5VnouKfW8Gzo/7LGt95o385kz3vXRL03rpP3HJw2FbRwnNNAuAm0he9z3pXFTb/nDM9rz6GvDvwPpw/znAmLuvC/fLy7/hPYf9a8Lx9Z6jPNoVWAX8wKJuhrMtWryoK78X7j4KfIVofYdHiT7rZXTnd6NcK74PSa+RZ+8jalWA+s9FI78509ItgTyu766j0vXNbCvgMuDj7v63aofGbPMGtueOmb0ReNzdl5VvjjnUa+wr/LkgutrfGzjD3RcATxM1aybp5HNB6Jd9E1HT6A7AlsAhMYd2w3cjja59/2b2WWAdcH5pU8xhjZ6LTM5TtwTyh4EXlN1/PvBIm8rSdGbWSxTEz3f3y8PmpHXfk85Fte3Pj9meR/sDh5nZ/cCPiZrXvwb0m1lpgaDy8m94z2H/tsBfqf8c5dHDwMPuflO4fylRYO/G7wXAa4D73H2Vu08AlwOvoDu/G+Va8X1Ieo3cCcl7bwTe5aH9m/rPxRPU/72alm4J5LcALwqZhJsRJRlc2eYyNUXIDP0+cKe7/2fZrqR1368Ejg5ZqfsBa0Jz19XAwWY2O9ReDibq13kUeNLM9guvdTQ5XUPe3U9w9+e7+85En/F17v4u4HrgiHBY5bkonaMjwvEetr89ZJjuAryIKJGnMN8jd/8z8JCZzQubDgL+QBd+L4IHgf3MbFYob+l8dN13o0Irvg9Jr5ErZvY64NPAYe6+tmxXXZ95+J7U+72annYnHLTqRpSF+UeiLMPPtrs8TXxf/0LUNHMrsDzcXk/U73ItcHf4d0443oBvh/NwG7Cw7LneB9wTbu8t274QuD085ls0ITmjBefl1WzMWt81/Me7B7gE2Dxs3yLcvyfs37Xs8Z8N73clZdnYRfoeAfOB4fDdGCLKMu7a7wVwMnBXKPMPibKQu+a7AVxIlB8wQVQz/F+t+D4kvUYOz8U9RP3Xpd/RMxv9zBv5Xk3nppndRERECqxbmtZFREQ6kgK5iIhIgSmQi4iIFJgCuYiISIEpkIuIiBSYArlITpjZpJktt2h1rkvMbNY0n+9TYTWn281shZkd3eDzHFZa2cnMBs1s9wae43VmdnMoz/KwAtSOYd+5ZnZfKOMfzey/zGyg7LH3W7Sq1gozu8bM/qGR9yHSqRTIRfJj3N3nu/tLgGeB49I+0Mx6Ku4fR7R4zr7h+V5F/PSQNbn7le6+NNwdJFoNKjUzewnwTeAYd9/N3ecTTX+5c9lhi919L2AeMAJcX1r6MTgg7B8GPtPI+xDpVArkIvn0a+AfAczsqFCbXW5m3y0FbTN7ysz+w8xuIlphqdxngA95mHff3de4+3nhcV8ws1tCTf2sMBMXZnaDmX3NzH4b9u0btr/HzL5lZq8ADgNOC2V5oZl9IDzXCjO7LKEV4dPAF939ztKGcHHwP5UHeuR04M/Ez4X+P6XzIiIRBXKRnAlzMB8C3GZm/wQcCewfarKTwLvCoVsSLcP4Mnf/Tdnjtwa2dvd7E17iW+7+0lBT7yOaW7pkS3d/BdG6yeeUP8jdf0s0xeTi0HJwL3B5eK69gDuJZsiqtAfw+3rOQTh+t5jtbySaaUxEAgVykfzoM7PlRM3HDxLNoX8QsA9wS9h3ENH0jxAF9ctinseovqLSAWZ2k5ndRrSwzB5l+y4ECLXlbcysv0aZX2Jmvw7P9a6K59q0YGbPCbX5P5rZp6odWnH/+vD+twFOrVEmka4ys/YhItIi46HWvUFo9j7P3U+IOf7v7j5ZudHd/2ZmT5vZru7+p4rn2wL4DtHc2Q+Z2UlE8z9veHjl09Uo87nAoLuvMLP3EM1xX+kOopXXVrj7X4D5IYhvVeV5FxDNy11ygLs/UaMsIl1JNXKRfLsWOMLMngtgZnPMbKcUjzsV+LaZbRMet42ZHcvGoP2ERWvYH1HxuCPD8f9CtOLVmor9TwJbl93fGnjUoqV030W8LwOfDd0EJbEZ+WG1rY8B2wO/rPYGRSSiGrlIjrn7H8zsc8A1ZjaDaLWmDwMP1HjoGUQ13lvMbCI87qvuPmZm3yPqZ76faCnGcqvN7LdETdjvi3neHwPfC8H2CODzwE2hPLcxNciX3sNtZva/gf8K/fd/Ieo6OLHssNPM7PNEAf53RDXwZ2u8RxEBrX4mIhEzuwH4lLsPt7ssIpKemtZFREQKTDVyERGRAlONXEREpMAUyEVERApMgVxERKTAFMhFREQKTIFcRESkwBTIRURECuz/B/VRqaE3TP1oAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=[8,6])\n", "plt.scatter(x=pcgdp, y=life_exp)\n", "plt.xlabel('Per Capita GDP')\n", "plt.ylabel('Life Expectancy')\n", "plt.title('Life Expectancy vs Per Capita GDP (2018)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting Point Properties\n", "\n", "There are several optional parameters that we can provide to `scatter()` to control how points are displayed. A few common examples are:\n", "\n", "* **`s`** - Controls the size of the points. \n", "* **`color`** - Sets the fill color of the points. \n", "* **`edgecolor`** - Sets the border color of the points.\n", "* **`alpha`** - Controls the opacity of the points. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=[8,6])\n", "plt.scatter(x=pcgdp, y=life_exp, s=80, alpha=0.7, \n", " color='orchid', edgecolor='black')\n", "plt.xlabel('Per Capita GDP')\n", "plt.ylabel('Life Expectancy')\n", "plt.title('Life Expectancy vs Per Capita GDP (2018)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that in the cell above, we set the fill color of the points to `'orchid'` and the border color to `'black'`. These are examples of \"named colors\", or strings that `matplotlib` recognizes as specific colors. You can find a partial list of named colors recognized by `matplotlib` on the following page: [Matplotlib Named Colors](https://matplotlib.org/3.1.0/gallery/color/named_colors.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results by Continent\n", "\n", "In the next few example, we will group together countries by their continents. So that we can work with each continent separately, we will create 10 new lists. For each continent, we will create a list containing the per capita GDP for countries in that continent, as well as a list containing the life expectancies for countries in that continent. \n", "\n", "We will use list comprehensions for this task, but the same results could have been obtained with loops. \n", "\n", "**Note:** In the next lesson, we will cover a package called NumPy. As we will see, NumPy provides more convenient tools for the type of list filtering that we are doing in the cell below. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "life_exp_by_cont = {'africa':[], 'americas':[], 'asia':[], 'europe':[]}\n", "pcgdp_by_cont = {'africa':[], 'americas':[], 'asia':[],'europe':[]}\n", "\n", "for i in range(len(country)):\n", " cont = continent[i]\n", " life_exp_by_cont[cont].append(life_exp[i])\n", " pcgdp_by_cont[cont].append(pcgdp[i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting Multiple Sets of Points\n", "\n", "We can used multiple `scatter()` functions to plot several different point sets within the same plot. In the plot below, we add the countries to the plot one continent at a time, and will color the points based on the continents. We also add a legend to the plot." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "continent_list = ['africa', 'asia', 'americas', 'europe']\n", "color_list = ['orange', 'lightgreen', 'steelblue', 'lightcoral']\n", "\n", "plt.figure(figsize=[8,6])\n", "\n", "for i in range(4):\n", " c = continent_list[i]\n", " plt.scatter(x=pcgdp_by_cont[c], y=life_exp_by_cont[c], s=60, alpha=0.7,\n", " color=color_list[i], edgecolor='black', label=c.title())\n", "\n", "plt.xlabel('Per Capita GDP')\n", "plt.ylabel('Life Expectancy')\n", "plt.title('Life Expectancy vs Per Capita GDP (2018)')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Subplots\n", "\n", "We can use the `subplot()` function from Matplotlib to create a grid of plots. The `subplot` function requires three arguments. The first is the number of rows in the grid, and the second is the number of columns. The third argument is the number of plot that we are currently creating, with the first plot being numbered as 1 (as opposed to 0).\n", "\n", "In the cell below, we use a loop to create a figure containing six subplots arranged in a 2x3 grid. We add a scatterplot of `life_exp` against `pcgdp` in each subplot, although you will generally be displaying different plots in each subplot. To help you understand how the plots are numbered, we have also used `plt.text()` to add a number to each plot. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=[12,6])\n", "\n", "for i in range(1,7):\n", " \n", " plt.subplot(2,3,i)\n", "\n", " plt.scatter(x=pcgdp, y=life_exp, alpha=0.3)\n", " plt.text(x=60000, y=65, s=str(i), fontsize=20 )\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the figure below, we create a grid of four scatter plots with each subplot displaying the points corresponding to countries in one of the four continental regions. You should compare this code with the code we used to create the previous scatter plot in which we assigned a different color to each of the continental regions. The changes we have made are as follows:\n", "\n", "* We have added a call to `plt.subplot()` as the first line of the loop. Note that the third argument is `i+1` so that the values for this argument range from 1 to 4. \n", "* We changed the figure so that individual plots would not be cramped. \n", "* The calls to `xlabel()`, `ylabel()`, and `title()` have been moved into loop so that each subplot will get these elements. \n", "* We have removed the legend and are instead using the titles of the subplots to indicate which continental region is represented in each plot. \n", "* By default, matplotlib will automatically select axis limits for us. However, this would result in each subplot having different limits, which could make it difficult to compare subplots. We use the functions `xlim()` and `ylim()` to set the axis limits to the same ranges for each of the subplots. \n", "* We have included a call to `tight_layout()` just before calling `plt.show()`. This is often necessary to keep subplot elements from overlapping. \n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "continent_list = ['africa', 'asia', 'americas', 'europe']\n", "color_list = ['orange', 'lightgreen', 'steelblue', 'lightcoral']\n", "\n", "plt.figure(figsize=[10,8])\n", "\n", "for i in range(4):\n", " c = continent_list[i]\n", " \n", " plt.subplot(2,2,i+1)\n", " \n", " plt.scatter(x=pcgdp_by_cont[c], y=life_exp_by_cont[c], s=60, alpha=0.7,\n", " color=color_list[i], edgecolor='black', label=c.title())\n", " \n", " plt.xlim([-8000, 135000])\n", " plt.ylim([45, 90])\n", " plt.xlabel('Per Capita GDP')\n", " plt.ylabel('Life Expectancy')\n", " plt.title('Life Expectancy vs Per Capita GDP (' + c.title() + ', 2018)')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Line Plots\n", "\n", "We can use the `plot()` function in Matplotlib to create simple line plots. We will illustrate the use of this function by creating plots illustration the percentage growth in the populations of several countries over the last decade. The cell below creates lists containing our data. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "year_range = list(range(2009, 2019))\n", "China = [0.49, 0.48, 0.48, 0.5, 0.49, 0.52, 0.5, 0.59, 0.53, 0.38]\n", "Germany = [-0.27, -0.24, -0.53, 0.14, 0.24, 0.31, 0.53, 1.21, 0.41, 0.4]\n", "Greece = [0.27, 0.27, 0, -0.27, -0.81, -0.64, -0.64, -0.74, -0.09, -0.28]\n", "India = [1.39, 1.7, 2.07, 0.23, 0.84, 1.25, 1.22, 1.19, 1.15, 1.12]\n", "Japan = [-0.14, 0.43, -0.2, -0.22, -0.17, -0.17, 0.02, -0.13, -0.17, -0.36]\n", "United_States = [0.88, 0.83, 0.73, 0.73, 0.7, 0.74, 0.74, 0.73, 0.64, 0.62]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the cell below, we plot the data for China." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(year_range, China)\n", "plt.scatter(year_range, China)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Line Plot Parameters\n", "\n", "We can use the `ls` parameter to specify a linestyle for the plot. We can use `lw` to specify the line width. As with `scatter()`, the parameter `color` can be used to select a color." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(year_range, China, lw=2, ls='--', color='magenta')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we are adding multiple plots to a figure and don't specify the colors, Matplotlib will select colors for us. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=[8,8])\n", "\n", "plt.plot(year_range, China, lw=2, label='China')\n", "plt.plot(year_range, Germany, lw=2, label='Germany')\n", "plt.plot(year_range, Greece, lw=2, label='Greece')\n", "plt.plot(year_range, India, lw=2, label='India')\n", "plt.plot(year_range, Japan, lw=2, label='Japan')\n", "plt.plot(year_range, United_States, lw=2, label='United States')\n", "\n", "plt.plot([2009, 2018], [0,0], ls='--', color='black')\n", "\n", "plt.legend()\n", "plt.xlabel('Year')\n", "plt.ylabel('Population Growth (in %)')\n", "plt.title('Population Growth by Year')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bar Charts\n", "\n", "We can use the `bar()` function to create bar charts in Matplotlib. When creating a bar chart, we must provide values for two pararamtes: `x` and `height`. The parameter `x` should be a list (or list-like object) that contains the labels to be displayed under each bar. The `height` parameter should be a list-like object that contains the desired heights for the bars. We can use the optional `color` and `edgecolor` parameters to set the fill and border colors of the bars. \n", "\n", "A simple example of a bar chart is provided below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "labels = ['A', 'B', 'C', 'D', 'E']\n", "heights = [18, 13, 26, 22, 5]\n", "\n", "plt.bar(x=labels, height=heights, color='cornflowerblue', edgecolor='black')\n", "plt.xlabel('Categories')\n", "plt.ylabel('Counts')\n", "plt.title('Bar Chart')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Proportional Bar Charts\n", "\n", "The heights of the bars in a bar chart can be floats as well as ints. In the example below, we divide each of the heights by the sum of the heights. We then use this to crate a bar chart that represents proprotions, rather than counts. The heights of the bars in this chart will sum to 1. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "total = sum(heights)\n", "props = []\n", "for h in heights:\n", " props.append(h / total)\n", "\n", "plt.bar(x=labels, height=props, color='cornflowerblue', edgecolor='black')\n", "plt.xlabel('Categories')\n", "plt.ylabel('Proportions')\n", "plt.title('Bar Chart')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stacked Bar Charts\n", "\n", "Assume that we have a dataset in which the observations can be grouped together into categories according to two (or more) different categorical variables. Suppose we would like to know the distributions of one of these categorical variables within the groups defined by a seperate categorical variable. A **stacked bar chart** is a convenient visualization for representing this type of information. A stocked bar chart can be created by setting the `bottom` argument of any bars in the chart that you do not want to be based at 0. \n", "\n", "The example below shows an example of creating a stacked bar chart. Notice that we have also set the `bbox_to_anchor` parameter of `plt.legend()`. This allows us to reposition the legened, and even to move it outside of the bounding box for the plot. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "labels = ['A', 'B', 'C']\n", "\n", "prop_1 = [0.2, 0.6, 0.3]\n", "prop_2 = [0.8, 0.4, 0.7]\n", "\n", "plt.bar(labels, prop_1, label='Group 1', color='cornflowerblue', edgecolor='black')\n", "plt.bar(labels, prop_2, label='Group 2', color='Salmon', edgecolor='black', bottom=prop_1)\n", "\n", "plt.legend(bbox_to_anchor=(1.02, 0.7))\n", "plt.ylabel('Proportion')\n", "plt.title('Stacked Bar Chart')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now work on creating a stacked bar chart displaying the proportion of countries in each continential region that have a life expectancy less than, or greater than, 70 years. We start by creating lists containing the desired proportions. This is accomplished in the following cell. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "continent_list = ['africa', 'americas', 'asia', 'europe']\n", "\n", "prop_life_exp_under_70 = []\n", "prop_life_exp_over_70 = []\n", "\n", "for cont in continent_list:\n", " \n", " count_total = 0\n", " count_low_life_exp = 0\n", " \n", " for i in range(len(country)):\n", " if continent[i] == cont:\n", " count_total += 1\n", " if life_exp[i] < 70:\n", " count_low_life_exp += 1\n", " \n", " prop = count_low_life_exp / count_total\n", "\n", " prop_life_exp_under_70.append(prop)\n", " prop_life_exp_over_70.append(1 - prop)\n", " \n", "print(prop_life_exp_under_70)\n", "print(prop_life_exp_over_70)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now create a stack bar chart to visually represent the information contained in the lists above. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "continent_list_cap = [c.title() for c in continent_list]\n", "\n", "plt.bar(continent_list_cap, prop_life_exp_over_70, label='Life Exp Over 70', \n", " color='cornflowerblue', edgecolor='black')\n", "\n", "plt.bar(continent_list_cap, prop_life_exp_under_70, label='Life Exp Under 70', \n", " bottom=prop_life_exp_over_70, color='Salmon', edgecolor='black')\n", "\n", "plt.legend(loc=\"center left\", bbox_to_anchor=(1.02,0.75))\n", "plt.xlabel('Continent')\n", "plt.ylabel('Proportion')\n", "plt.title('Proportion of Countries with Life Expectancy Greater than 70')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Histograms\n", "\n", "A **histogram** is similar to a bar chart, but is use to represent continuous data rather than categorical data. A histogram is created from a sample by dividing the range occupied by values within that sample into smaller subintervals, or **bins**. Each bin has a bar associated with it. The height of a particular bar is determined by the number of observations that fall within that bin. \n", "\n", "We can use `plt.hist()` to create histograms. The only required parameter for `hist()` is `x`, which should be a list-like object containing values representing a sample. Matplotlib will attempt to automatically determined a reasonable number of bins to use, but we will typically want to set this ourselves using the optional `bins` parameter. The `hist()` function also has optional `edgecolor` and `color` parameters that perform the same functions as in `bar`. \n", "\n", "In the example below, we create a histogram with 15 bins to display the distribution of values in `life_exp`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(life_exp, edgecolor='black', color='cornflowerblue', bins=15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As shown above, we can set the `bins` parameter to an integer to specify the number of bins that we would like to use. Alternately, we can provide this parameter with a list of numbers identifying the values use to define the bins. We show an example of this in the cell below. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(life_exp, edgecolor='k', color='cornflowerblue', \n", " bins=[50, 55, 60, 65, 70, 75, 80, 85])\n", "plt.title('Histogram of Life Expectancy')\n", "plt.xlabel('Life Expectancy')\n", "plt.ylabel('Count')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the example below, we use `subplot()` to display a two histograms. One shows the distribution for life expectancy in 2018, while the other shows the distribution of per capita GDP in 2018." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=[12,4])\n", "\n", "plt.subplot(1, 2, 1)\n", "plt.hist(life_exp, edgecolor='k', color='cornflowerblue', \n", " bins=[50, 55, 60, 65, 70, 75, 80, 85])\n", "plt.title('Histogram of Life Expectancy')\n", "plt.xlabel('Life Expectancy')\n", "plt.ylabel('Count')\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.hist(pcgdp, edgecolor='k', color='salmon', \n", " bins=10)\n", "plt.title('Histogram of Per Capita GDP')\n", "plt.xlabel('Per Capita GDP')\n", "plt.ylabel('Count')\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }