Zero approach in python pandas

zero_approach.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.style.use(\"dark_background\")\n",
        "\n",
        "count = 1000\n",
        "df = pd.DataFrame({\n",
        "    \"zero_approach\": np.concatenate((\n",
        "        # Approach to zero from left side\n",
        "        np.arange(-count, 0),\n",
        "\n",
        "        # Positive and negative infinity\n",
        "        [-0.0, +0.0],\n",
        "        \n",
        "        # Approach to zero from right side\n",
        "        np.arange(0, count)\n",
        "    )) / count\n",
        "})\n",
        "\n",
        "df.zero_approach = 1.0 / df.zero_approach\n",
        "\n",
        "df.plot()\n",
        "\n",
        "print(\"Min value is\", df.zero_approach.min())\n",
        "print(\"Max value is\", df.zero_approach.max())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min value is -inf\n",
            "Max value is inf\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-08-15T07:43:57.460Z",
          "iopub.execute_input": "2020-08-15T07:43:57.466Z",
          "iopub.status.idle": "2020-08-15T07:43:57.662Z",
          "shell.execute_reply": "2020-08-15T07:43:57.666Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "python",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.23.1"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}