Datacamp_Matplotlib_CH2.ipynb

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      "name": "Datacamp Matplotlib CH2.ipynb",
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        "<a href=\"https://colab.research.google.com/github/mart-anthony-stark/Machine-Learning-and-Deep-Learning/blob/main/Datacamp_Matplotlib_CH2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Visualizing correlation between GDP and Life Expectancy"
      ],
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        "id": "aoTT4T6G7bn6"
      }
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    {
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        "id": "EGisO87KzEUt"
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('gapminder.csv', index_col=1)\n",
        "df.head()"
      ],
      "metadata": {
        "id": "hV2VNChF2WHO",
        "colab": {
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          "height": 215
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        "outputId": "d9294d61-d9ef-44a2-9299-6535305c1a2c"
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      "execution_count": 19,
      "outputs": [
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              "             Unnamed: 0  year  population      cont  life_exp       gdp_cap\n",
              "country                                                                    \n",
              "Afghanistan          11  2007  31889923.0      Asia    43.828    974.580338\n",
              "Albania              23  2007   3600523.0    Europe    76.423   5937.029526\n",
              "Algeria              35  2007  33333216.0    Africa    72.301   6223.367465\n",
              "Angola               47  2007  12420476.0    Africa    42.731   4797.231267\n",
              "Argentina            59  2007  40301927.0  Americas    75.320  12779.379640"
            ],
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              "      <th>Afghanistan</th>\n",
              "      <td>11</td>\n",
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              "      <td>3600523.0</td>\n",
              "      <td>Europe</td>\n",
              "      <td>76.423</td>\n",
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              "      <th>Algeria</th>\n",
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              "      <td>Africa</td>\n",
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              "      <th>Angola</th>\n",
              "      <td>47</td>\n",
              "      <td>2007</td>\n",
              "      <td>12420476.0</td>\n",
              "      <td>Africa</td>\n",
              "      <td>42.731</td>\n",
              "      <td>4797.231267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>59</td>\n",
              "      <td>2007</td>\n",
              "      <td>40301927.0</td>\n",
              "      <td>Americas</td>\n",
              "      <td>75.320</td>\n",
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      "source": [
        "df.shape"
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          "data": {
            "text/plain": [
              "(142, 7)"
            ]
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          "metadata": {},
          "execution_count": 17
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      "source": [
        "df.isnull().sum()"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0    0\n",
              "country       0\n",
              "year          0\n",
              "population    0\n",
              "cont          0\n",
              "life_exp      0\n",
              "gdp_cap       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "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]\n",
        "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]\n"
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      "metadata": {
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      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plotting Year and Population"
      ],
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        "id": "eYh9t1ZL7rqm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(year, pop)\n",
        "plt.show()"
      ],
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          "height": 265
        },
        "id": "_rz6ribb30y4",
        "outputId": "05758561-8ce0-4788-d5cc-ac9f7712ab11"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Scatterplot GDP Capita and Life expectancy"
      ],
      "metadata": {
        "id": "vBI84BnE7xGb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gdp_cap = df['gdp_cap'].tolist()\n",
        "life_exp = df['life_exp'].tolist()"
      ],
      "metadata": {
        "id": "m-8vaxmt6VQA"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(gdp_cap, life_exp)\n",
        "# Put the x-axis on a logarithmic scale\n",
        "plt.xscale('log')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "z1oJRv6q7IhX",
        "outputId": "44e592aa-f5e2-48a7-dfe3-f2e478092ad9"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Scatterplot Population and Life expectancy"
      ],
      "metadata": {
        "id": "-ugSbxnY79ec"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build Scatter plot\n",
        "pop = [31.889923, 3.600523, 33.333216, 12.420476, 40.301927, 20.434176, 8.199783, 0.708573, 150.448339, 10.392226, 8.078314, 9.119152, 4.552198, 1.639131, 190.010647, 7.322858, 14.326203, 8.390505, 14.131858, 17.696293, 33.390141, 4.369038, 10.238807, 16.284741, 1318.683096, 44.22755, 0.71096, 64.606759, 3.80061, 4.133884, 18.013409, 4.493312, 11.416987, 10.228744, 5.46812, 0.496374, 9.319622, 13.75568, 80.264543, 6.939688, 0.551201, 4.906585, 76.511887, 5.23846, 61.083916, 1.454867, 1.688359, 82.400996, 22.873338, 10.70629, 12.572928, 9.947814, 1.472041, 8.502814, 7.483763, 6.980412, 9.956108, 0.301931, 1110.396331, 223.547, 69.45357, 27.499638, 4.109086, 6.426679, 58.147733, 2.780132, 127.467972, 6.053193, 35.610177, 23.301725, 49.04479, 2.505559, 3.921278, 2.012649, 3.193942, 6.036914, 19.167654, 13.327079, 24.821286, 12.031795, 3.270065, 1.250882, 108.700891, 2.874127, 0.684736, 33.757175, 19.951656, 47.76198, 2.05508, 28.90179, 16.570613, 4.115771, 5.675356, 12.894865, 135.031164, 4.627926, 3.204897, 169.270617, 3.242173, 6.667147, 28.674757, 91.077287, 38.518241, 10.642836, 3.942491, 0.798094, 22.276056, 8.860588, 0.199579, 27.601038, 12.267493, 10.150265, 6.144562, 4.553009, 5.447502, 2.009245, 9.118773, 43.997828, 40.448191, 20.378239, 42.292929, 1.133066, 9.031088, 7.554661, 19.314747, 23.174294, 38.13964, 65.068149, 5.701579, 1.056608, 10.276158, 71.158647, 29.170398, 60.776238, 301.139947, 3.447496, 26.084662, 85.262356, 4.018332, 22.211743, 11.746035, 12.311143]\n",
        "plt.scatter(pop,life_exp)\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "8Ml5KLas8EYG",
        "outputId": "07edc594-17ff-494e-8ff3-7b4cb7653943"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Histogram"
      ],
      "metadata": {
        "id": "DKn9ABBG8Xt9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(life_exp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "n7G4r5Gf8Y36",
        "outputId": "705b1d0d-2659-40e0-d982-234d3c068753"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 8.,  7., 10., 10., 10.,  8.,  5., 33., 23., 28.]),\n",
              " array([39.613, 43.912, 48.211, 52.51 , 56.809, 61.108, 65.407, 69.706,\n",
              "        74.005, 78.304, 82.603]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build histogram with 5 bins\n",
        "plt.hist(life_exp, bins=5)\n",
        "\n",
        "# Show and clean up plot\n",
        "plt.show()\n",
        "plt.clf()\n",
        "\n",
        "# Build histogram with 20 bins\n",
        "plt.hist(life_exp, bins=20)\n",
        "\n",
        "# Show and clean up again\n",
        "plt.show()\n",
        "plt.clf()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "Zc2DbGIO8vSl",
        "outputId": "50f605be-90cb-43e5-f3da-ea82f7e6b69f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Histogram of life_exp, 15 bins\n",
        "plt.hist(life_exp, bins=15)\n",
        "\n",
        "# Show and clear plot\n",
        "plt.show()\n",
        "plt.clf()\n",
        "\n",
        "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]\n",
        "# Histogram of life_exp1950, 15 bins\n",
        "plt.hist(life_exp1950, bins=15)\n",
        "\n",
        "# Show and clear plot again\n",
        "plt.show()\n",
        "plt.clf()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "LW-AwuB78zuj",
        "outputId": "d29e63fa-24ed-4904-bfa7-175c83a61dbb"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}