Connected-scatterplot-Gini-PIB.ipynb

connected-scatterplot-gini-pib.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Connected-scatterplot-Gini-PIB.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/alonsosilvaallende/f4b413c70b644ab0256ba1fb4be07076/connected-scatterplot-gini-pib.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1Q2w-5ZTzkC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "891f9262-1b25-484e-cc18-f0b8011a9814"
      },
      "source": [
        "!pip install -U wbdata"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: wbdata in /usr/local/lib/python3.7/dist-packages (0.3.0)\n",
            "Requirement already satisfied, skipping upgrade: requests>=2.0 in /usr/local/lib/python3.7/dist-packages (from wbdata) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: decorator>=4.0 in /usr/local/lib/python3.7/dist-packages (from wbdata) (4.4.2)\n",
            "Requirement already satisfied, skipping upgrade: appdirs<2.0,>=1.4 in /usr/local/lib/python3.7/dist-packages (from wbdata) (1.4.4)\n",
            "Requirement already satisfied, skipping upgrade: tabulate>=0.8.5 in /usr/local/lib/python3.7/dist-packages (from wbdata) (0.8.9)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0->wbdata) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0->wbdata) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0->wbdata) (2021.5.30)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0->wbdata) (2.10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cky3BsLK5zhW"
      },
      "source": [
        "!pip install -q adjustText"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qWMO0JpbtRsr"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eu-1KK8HQJ5v"
      },
      "source": [
        "import wbdata"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qusd6qgw529Q"
      },
      "source": [
        "from adjustText import adjust_text"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3CFuWb1tAw3"
      },
      "source": [
        "countries = [\"CL\"]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WsaIfVgbtD26"
      },
      "source": [
        "indicators = {'NY.GDP.PCAP.PP.KD':'GDP per capita (constant 2010 US$)'}"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1KSYitPtGHI"
      },
      "source": [
        "df = wbdata.get_dataframe(indicators, country=countries, convert_date=False)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgossWO5tKYT"
      },
      "source": [
        "df = df.dropna()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBAXdtCwB85z"
      },
      "source": [
        "df = df.sort_index()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "mzv-v3OgFOrO",
        "outputId": "d953dcba-a90b-421c-d004-e55549e01b0b"
      },
      "source": [
        "# Datos de Gini\n",
        "# Página 7 del documento: https://t.co/6DLrno88yU?amp=1\n",
        "# Página 22 del documento: http://observatorio.ministeriodesarrollosocial.gob.cl/storage/docs/casen/2020/Resumen_de_resultados_de_Pobreza_por_Ingresos_y_Distribucion_de_Ingresos.pdf\n",
        "data = pd.DataFrame()\n",
        "data[\"date\"] = ['1990', '1996', '1998', '2000', '2003', '2006', '2009', '2011', '2013', '2015', '2017', '2020']\n",
        "data[\"Gini\"] = [52.1, 52.2, 53.4, 54.9, 52.8, 49.9, 49.9, 49.2, 49.1, 48.2, 48.8, 51]\n",
        "data = data.set_index(\"date\")\n",
        "data.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gini</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>52.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>52.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>53.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>54.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>52.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Gini\n",
              "date      \n",
              "1990  52.1\n",
              "1996  52.2\n",
              "1998  53.4\n",
              "2000  54.9\n",
              "2003  52.8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9gZehlSGksY"
      },
      "source": [
        "df[\"Gini Index\"] = data"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVkQAP50Hsrv"
      },
      "source": [
        "df[\"Indicator\"] = df[\"Gini Index\"].notna()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZbO_nQVLGocq"
      },
      "source": [
        "df = df.interpolate(method=\"linear\")"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "eZd9Lj4-IFPU",
        "outputId": "a614a133-05b5-4ce8-86e9-4aec53427bb5"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GDP per capita (constant 2010 US$)</th>\n",
              "      <th>Gini Index</th>\n",
              "      <th>Indicator</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>9849.648557</td>\n",
              "      <td>52.100000</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991</th>\n",
              "      <td>10444.751086</td>\n",
              "      <td>52.116667</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992</th>\n",
              "      <td>11421.038182</td>\n",
              "      <td>52.133333</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993</th>\n",
              "      <td>11977.025592</td>\n",
              "      <td>52.150000</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994</th>\n",
              "      <td>12382.883298</td>\n",
              "      <td>52.166667</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      GDP per capita (constant 2010 US$)  Gini Index  Indicator\n",
              "date                                                           \n",
              "1990                         9849.648557   52.100000       True\n",
              "1991                        10444.751086   52.116667      False\n",
              "1992                        11421.038182   52.133333      False\n",
              "1993                        11977.025592   52.150000      False\n",
              "1994                        12382.883298   52.166667      False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kz5uzLCkRF3F"
      },
      "source": [
        "plt.rcParams.update({'xtick.labelsize' : 14,\n",
        "                     'ytick.labelsize' : 14})"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCkNqOv3sLry"
      },
      "source": [
        "plt.style.use(\"ggplot\")"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "veozIKXYydcG",
        "outputId": "083f4249-dd39-4a30-9568-9f0026aaa13e"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(8,8))\n",
        "plt.plot(df[\"GDP per capita (constant 2010 US$)\"].iloc[0:5], df[\"Gini Index\"].iloc[0:5], marker='.', color=\"C0\")\n",
        "plt.plot(df[\"GDP per capita (constant 2010 US$)\"].iloc[4:11], df[\"Gini Index\"].iloc[4:11], marker='.', color=\"C1\")\n",
        "plt.plot(df[\"GDP per capita (constant 2010 US$)\"].iloc[10:17], df[\"Gini Index\"].iloc[10:17], marker='.', color=\"C0\")\n",
        "plt.plot(df[\"GDP per capita (constant 2010 US$)\"].iloc[16:21], df[\"Gini Index\"].iloc[16:21], marker='.', color=\"C1\")\n",
        "plt.plot(df[\"GDP per capita (constant 2010 US$)\"].iloc[20:25], df[\"Gini Index\"].iloc[20:25], marker='.', color=\"C0\")\n",
        "plt.plot(df[\"GDP per capita (constant 2010 US$)\"].iloc[24:29], df[\"Gini Index\"].iloc[24:29], marker='.', color=\"C1\")\n",
        "plt.plot(df[\"GDP per capita (constant 2010 US$)\"].iloc[28:], df[\"Gini Index\"].iloc[28:], marker='.', color=\"C0\")\n",
        "plt.scatter(df[\"GDP per capita (constant 2010 US$)\"].loc[df[\"Indicator\"]], df[\"Gini Index\"].loc[df[\"Indicator\"]], label=\"Valores del índice de Gini exactos\",marker=\"o\", color=\"black\")\n",
        "ax.legend(loc=\"lower left\",fontsize=12)\n",
        "plt.annotate(\"Aylwin\", (df[\"GDP per capita (constant 2010 US$)\"].iloc[1]+1000, df[\"Gini Index\"].iloc[1]+.25),va=\"bottom\",ha='center',color=\"C0\",fontsize=15)\n",
        "plt.annotate(\"Frei\", (df[\"GDP per capita (constant 2010 US$)\"].iloc[8]-1000, df[\"Gini Index\"].iloc[8]),va=\"bottom\",ha='center',color=\"C1\",fontsize=15)\n",
        "plt.annotate(\"Lagos\", (df[\"GDP per capita (constant 2010 US$)\"].iloc[13]+1500, df[\"Gini Index\"].iloc[13]-.25),va=\"bottom\",ha='center',color=\"C0\",fontsize=15)\n",
        "plt.annotate(\"Bachelet I\", (df[\"GDP per capita (constant 2010 US$)\"].iloc[17]-1500, df[\"Gini Index\"].iloc[17]-.5),va=\"bottom\",ha='center',color=\"C1\",fontsize=15)\n",
        "plt.annotate(\"Piñera I\", (df[\"GDP per capita (constant 2010 US$)\"].iloc[21]-1500, df[\"Gini Index\"].iloc[21]-.4),va=\"bottom\",ha='center',color=\"C0\",fontsize=15)\n",
        "plt.annotate(\"Bachelet II\", (df[\"GDP per capita (constant 2010 US$)\"].iloc[25]-1700, df[\"Gini Index\"].iloc[25]),va=\"bottom\",ha='center',color=\"C1\",fontsize=15)\n",
        "plt.annotate(\"Piñera II\", (df[\"GDP per capita (constant 2010 US$)\"].iloc[-1], df[\"Gini Index\"].iloc[-1]+.3),va=\"bottom\",ha='center',color=\"C0\",fontsize=15)\n",
        "texts = [plt.annotate(df.index[i],(df[\"GDP per capita (constant 2010 US$)\"].iloc[i], df[\"Gini Index\"].iloc[i]),va='bottom',ha='center') for i in np.arange(0,df.shape[0],1)]\n",
        "adjust_text(texts)\n",
        "ax.set_xticks([10000+2000*i for i in range(8)])\n",
        "plt.arrow(24000,54.5,1200,0, length_includes_head=True,\n",
        "          head_width=0.08, head_length=500, color=\"black\")\n",
        "plt.arrow(23500,54.2,0,-0.5, length_includes_head=True,\n",
        "          head_width=200, head_length=0.2, color=\"black\")\n",
        "plt.annotate(\"Mayor crecimiento\",(24000,54.8), va='bottom', ha='center', fontsize=12)\n",
        "plt.annotate(\"Menor desigualdad\",(21000,53.9), va='bottom', ha='center', fontsize=12)\n",
        "ax.set_xticklabels([f\"{10000+2000*i:,.0f}\".replace(\",\",\".\") for i in range(8)])\n",
        "ax.set_xlabel(\"PIB per cápita (USD ajustados por inflación a valor 2010)\", fontsize=15)\n",
        "ax.set_ylabel(\"Índice de Gini\", fontsize=15)\n",
        "ax.set_xlim(9000,26000)\n",
        "plt.annotate('Fuente: Elaboración propia en base a datos de:\\nÍndice de Gini del ingreso monetario: Entre 1990 y 2003 cálculos del PNUD,\\ndesde 2006 Ministerio de Desarrollo Social\\nPIB per cápita: Banco mundial\\nAutor: @alonsosilva', (0,0), (-40,-60), xycoords='axes fraction', textcoords='offset points', va='top', fontsize=15)\n",
        "plt.savefig(f\"{pd.to_datetime('today').strftime('%Y-%m-%d')}-Gini-PIB.svg\", bbox_inches='tight', format='svg')\n",
        "#plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E98R780wXFHK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "88eecf96-12c5-4772-d499-90485efc485e"
      },
      "source": [
        "from google.colab import files\n",
        "files.download(f\"{pd.to_datetime('today').strftime('%Y-%m-%d')}-Gini-PIB.svg\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_aa6c33e4-e8ea-430e-9156-2c441aa2f1fa\", \"2021-07-06-Gini-PIB.svg\", 93467)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}