eda.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "eda.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGPg0Fao-mZT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from numpy import sqrt\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9pLtXLW-nJN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "de746c33-cbb5-4910-8277-9acd39b1d4ea"
      },
      "source": [
        "np.random.randn(100000) "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.72759449, -0.45209369, -1.29502829, ...,  0.00997866,\n",
              "        0.53168992, -0.89257863])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jhg-Y9px-0zp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vol = .030\n",
        "lag = 300\n",
        "df = pd.DataFrame(np.random.randn(1000000) * sqrt(vol) * sqrt(1 / 252.)).cumsum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0hLyqz_cHcG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "40390d21-0838-44c0-f60f-18a8a167827d"
      },
      "source": [
        "plt.plot(df[0].tolist())\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIpNiAiAiMikmACIik/p/jgN7o/jd+2UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tEv_nPIcQOQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(np.random.randn(100000) * sqrt(vol) * sqrt(1 / 252.))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHy3wQz5_kaa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "68a0c3cc-cb71-4359-8d8b-d903ee9fc20e"
      },
      "source": [
        "plt.plot(df[0].tolist())\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY+WStwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NT52yRLyAGdR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "date_rng = pd.date_range(start='1/1/2018', end='1/08/2018', freq='H')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQEG99at_lyM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(date_rng, columns=['date'])\n",
        "df['data'] = np.random.randint(0,100,size=(len(date_rng)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ztru6KTWAHaT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "46ddc869-66a4-4927-bd99-429a52012c3a"
      },
      "source": [
        "df['datetime'] = pd.to_datetime(df['date'])\n",
        "df = df.set_index('datetime')\n",
        "df.drop(['date'], axis=1, inplace=True)\n",
        "df.head()"
      ],
      "execution_count": 27,
      "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>data</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01 00:00:00</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 01:00:00</th>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 02:00:00</th>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 03:00:00</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 04:00:00</th>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     data\n",
              "datetime                 \n",
              "2018-01-01 00:00:00    60\n",
              "2018-01-01 01:00:00    66\n",
              "2018-01-01 02:00:00    73\n",
              "2018-01-01 03:00:00    14\n",
              "2018-01-01 04:00:00    90"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUbwcby-AIOK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "53c0d3df-e3ac-41c6-fcfa-2d7f7a0ec122"
      },
      "source": [
        "string_date_rng = [str(x) for x in date_rng]\n",
        "string_date_rng"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['2018-01-01 00:00:00',\n",
              " '2018-01-01 01:00:00',\n",
              " '2018-01-01 02:00:00',\n",
              " '2018-01-01 03:00:00',\n",
              " '2018-01-01 04:00:00',\n",
              " '2018-01-01 05:00:00',\n",
              " '2018-01-01 06:00:00',\n",
              " '2018-01-01 07:00:00',\n",
              " '2018-01-01 08:00:00',\n",
              " '2018-01-01 09:00:00',\n",
              " '2018-01-01 10:00:00',\n",
              " '2018-01-01 11:00:00',\n",
              " '2018-01-01 12:00:00',\n",
              " '2018-01-01 13:00:00',\n",
              " '2018-01-01 14:00:00',\n",
              " '2018-01-01 15:00:00',\n",
              " '2018-01-01 16:00:00',\n",
              " '2018-01-01 17:00:00',\n",
              " '2018-01-01 18:00:00',\n",
              " '2018-01-01 19:00:00',\n",
              " '2018-01-01 20:00:00',\n",
              " '2018-01-01 21:00:00',\n",
              " '2018-01-01 22:00:00',\n",
              " '2018-01-01 23:00:00',\n",
              " '2018-01-02 00:00:00',\n",
              " '2018-01-02 01:00:00',\n",
              " '2018-01-02 02:00:00',\n",
              " '2018-01-02 03:00:00',\n",
              " '2018-01-02 04:00:00',\n",
              " '2018-01-02 05:00:00',\n",
              " '2018-01-02 06:00:00',\n",
              " '2018-01-02 07:00:00',\n",
              " '2018-01-02 08:00:00',\n",
              " '2018-01-02 09:00:00',\n",
              " '2018-01-02 10:00:00',\n",
              " '2018-01-02 11:00:00',\n",
              " '2018-01-02 12:00:00',\n",
              " '2018-01-02 13:00:00',\n",
              " '2018-01-02 14:00:00',\n",
              " '2018-01-02 15:00:00',\n",
              " '2018-01-02 16:00:00',\n",
              " '2018-01-02 17:00:00',\n",
              " '2018-01-02 18:00:00',\n",
              " '2018-01-02 19:00:00',\n",
              " '2018-01-02 20:00:00',\n",
              " '2018-01-02 21:00:00',\n",
              " '2018-01-02 22:00:00',\n",
              " '2018-01-02 23:00:00',\n",
              " '2018-01-03 00:00:00',\n",
              " '2018-01-03 01:00:00',\n",
              " '2018-01-03 02:00:00',\n",
              " '2018-01-03 03:00:00',\n",
              " '2018-01-03 04:00:00',\n",
              " '2018-01-03 05:00:00',\n",
              " '2018-01-03 06:00:00',\n",
              " '2018-01-03 07:00:00',\n",
              " '2018-01-03 08:00:00',\n",
              " '2018-01-03 09:00:00',\n",
              " '2018-01-03 10:00:00',\n",
              " '2018-01-03 11:00:00',\n",
              " '2018-01-03 12:00:00',\n",
              " '2018-01-03 13:00:00',\n",
              " '2018-01-03 14:00:00',\n",
              " '2018-01-03 15:00:00',\n",
              " '2018-01-03 16:00:00',\n",
              " '2018-01-03 17:00:00',\n",
              " '2018-01-03 18:00:00',\n",
              " '2018-01-03 19:00:00',\n",
              " '2018-01-03 20:00:00',\n",
              " '2018-01-03 21:00:00',\n",
              " '2018-01-03 22:00:00',\n",
              " '2018-01-03 23:00:00',\n",
              " '2018-01-04 00:00:00',\n",
              " '2018-01-04 01:00:00',\n",
              " '2018-01-04 02:00:00',\n",
              " '2018-01-04 03:00:00',\n",
              " '2018-01-04 04:00:00',\n",
              " '2018-01-04 05:00:00',\n",
              " '2018-01-04 06:00:00',\n",
              " '2018-01-04 07:00:00',\n",
              " '2018-01-04 08:00:00',\n",
              " '2018-01-04 09:00:00',\n",
              " '2018-01-04 10:00:00',\n",
              " '2018-01-04 11:00:00',\n",
              " '2018-01-04 12:00:00',\n",
              " '2018-01-04 13:00:00',\n",
              " '2018-01-04 14:00:00',\n",
              " '2018-01-04 15:00:00',\n",
              " '2018-01-04 16:00:00',\n",
              " '2018-01-04 17:00:00',\n",
              " '2018-01-04 18:00:00',\n",
              " '2018-01-04 19:00:00',\n",
              " '2018-01-04 20:00:00',\n",
              " '2018-01-04 21:00:00',\n",
              " '2018-01-04 22:00:00',\n",
              " '2018-01-04 23:00:00',\n",
              " '2018-01-05 00:00:00',\n",
              " '2018-01-05 01:00:00',\n",
              " '2018-01-05 02:00:00',\n",
              " '2018-01-05 03:00:00',\n",
              " '2018-01-05 04:00:00',\n",
              " '2018-01-05 05:00:00',\n",
              " '2018-01-05 06:00:00',\n",
              " '2018-01-05 07:00:00',\n",
              " '2018-01-05 08:00:00',\n",
              " '2018-01-05 09:00:00',\n",
              " '2018-01-05 10:00:00',\n",
              " '2018-01-05 11:00:00',\n",
              " '2018-01-05 12:00:00',\n",
              " '2018-01-05 13:00:00',\n",
              " '2018-01-05 14:00:00',\n",
              " '2018-01-05 15:00:00',\n",
              " '2018-01-05 16:00:00',\n",
              " '2018-01-05 17:00:00',\n",
              " '2018-01-05 18:00:00',\n",
              " '2018-01-05 19:00:00',\n",
              " '2018-01-05 20:00:00',\n",
              " '2018-01-05 21:00:00',\n",
              " '2018-01-05 22:00:00',\n",
              " '2018-01-05 23:00:00',\n",
              " '2018-01-06 00:00:00',\n",
              " '2018-01-06 01:00:00',\n",
              " '2018-01-06 02:00:00',\n",
              " '2018-01-06 03:00:00',\n",
              " '2018-01-06 04:00:00',\n",
              " '2018-01-06 05:00:00',\n",
              " '2018-01-06 06:00:00',\n",
              " '2018-01-06 07:00:00',\n",
              " '2018-01-06 08:00:00',\n",
              " '2018-01-06 09:00:00',\n",
              " '2018-01-06 10:00:00',\n",
              " '2018-01-06 11:00:00',\n",
              " '2018-01-06 12:00:00',\n",
              " '2018-01-06 13:00:00',\n",
              " '2018-01-06 14:00:00',\n",
              " '2018-01-06 15:00:00',\n",
              " '2018-01-06 16:00:00',\n",
              " '2018-01-06 17:00:00',\n",
              " '2018-01-06 18:00:00',\n",
              " '2018-01-06 19:00:00',\n",
              " '2018-01-06 20:00:00',\n",
              " '2018-01-06 21:00:00',\n",
              " '2018-01-06 22:00:00',\n",
              " '2018-01-06 23:00:00',\n",
              " '2018-01-07 00:00:00',\n",
              " '2018-01-07 01:00:00',\n",
              " '2018-01-07 02:00:00',\n",
              " '2018-01-07 03:00:00',\n",
              " '2018-01-07 04:00:00',\n",
              " '2018-01-07 05:00:00',\n",
              " '2018-01-07 06:00:00',\n",
              " '2018-01-07 07:00:00',\n",
              " '2018-01-07 08:00:00',\n",
              " '2018-01-07 09:00:00',\n",
              " '2018-01-07 10:00:00',\n",
              " '2018-01-07 11:00:00',\n",
              " '2018-01-07 12:00:00',\n",
              " '2018-01-07 13:00:00',\n",
              " '2018-01-07 14:00:00',\n",
              " '2018-01-07 15:00:00',\n",
              " '2018-01-07 16:00:00',\n",
              " '2018-01-07 17:00:00',\n",
              " '2018-01-07 18:00:00',\n",
              " '2018-01-07 19:00:00',\n",
              " '2018-01-07 20:00:00',\n",
              " '2018-01-07 21:00:00',\n",
              " '2018-01-07 22:00:00',\n",
              " '2018-01-07 23:00:00',\n",
              " '2018-01-08 00:00:00']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBdcBOgACVYK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "1016db4a-676e-4b3a-f6bb-aa8fec1258e6"
      },
      "source": [
        "timestamp_date_rng = pd.to_datetime(string_date_rng, infer_datetime_format=True)\n",
        "timestamp_date_rng"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 01:00:00',\n",
              "               '2018-01-01 02:00:00', '2018-01-01 03:00:00',\n",
              "               '2018-01-01 04:00:00', '2018-01-01 05:00:00',\n",
              "               '2018-01-01 06:00:00', '2018-01-01 07:00:00',\n",
              "               '2018-01-01 08:00:00', '2018-01-01 09:00:00',\n",
              "               ...\n",
              "               '2018-01-07 15:00:00', '2018-01-07 16:00:00',\n",
              "               '2018-01-07 17:00:00', '2018-01-07 18:00:00',\n",
              "               '2018-01-07 19:00:00', '2018-01-07 20:00:00',\n",
              "               '2018-01-07 21:00:00', '2018-01-07 22:00:00',\n",
              "               '2018-01-07 23:00:00', '2018-01-08 00:00:00'],\n",
              "              dtype='datetime64[ns]', length=169, freq=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvA34j96C8rZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from datetime import datetime"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_G6y64IBCeMG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "8fc9250a-d489-4d61-8069-4b1c8a994f72"
      },
      "source": [
        "string_date_rng_2 = ['June-01-2018', 'June-02-2018', 'June-03-2018']\n",
        "timestamp_date_rng_2 = [datetime.strptime(x,'%B-%d-%Y') for x in string_date_rng_2]\n",
        "timestamp_date_rng_2"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[datetime.datetime(2018, 6, 1, 0, 0),\n",
              " datetime.datetime(2018, 6, 2, 0, 0),\n",
              " datetime.datetime(2018, 6, 3, 0, 0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4mes50EIChFy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "901b8cd0-234f-4fb9-8049-dd3044583685"
      },
      "source": [
        "df2 = pd.DataFrame(timestamp_date_rng_2, columns=['date'])\n",
        "df2"
      ],
      "execution_count": 33,
      "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>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-06-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-06-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-06-03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date\n",
              "0 2018-06-01\n",
              "1 2018-06-02\n",
              "2 2018-06-03"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eEdzlGH-DBYI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 787
        },
        "outputId": "df28a821-6acf-4151-e7f3-4dd3f390f7d5"
      },
      "source": [
        "df[df.index.day == 2]"
      ],
      "execution_count": 34,
      "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>data</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-02 00:00:00</th>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 01:00:00</th>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 02:00:00</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 03:00:00</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 04:00:00</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 05:00:00</th>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 06:00:00</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 07:00:00</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 08:00:00</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 09:00:00</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 10:00:00</th>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 11:00:00</th>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 12:00:00</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 13:00:00</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 14:00:00</th>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 15:00:00</th>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 16:00:00</th>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 17:00:00</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 18:00:00</th>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 19:00:00</th>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 20:00:00</th>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 21:00:00</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 22:00:00</th>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02 23:00:00</th>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     data\n",
              "datetime                 \n",
              "2018-01-02 00:00:00    93\n",
              "2018-01-02 01:00:00    91\n",
              "2018-01-02 02:00:00    80\n",
              "2018-01-02 03:00:00     2\n",
              "2018-01-02 04:00:00     9\n",
              "2018-01-02 05:00:00    76\n",
              "2018-01-02 06:00:00     9\n",
              "2018-01-02 07:00:00     5\n",
              "2018-01-02 08:00:00     2\n",
              "2018-01-02 09:00:00    20\n",
              "2018-01-02 10:00:00    59\n",
              "2018-01-02 11:00:00    69\n",
              "2018-01-02 12:00:00    16\n",
              "2018-01-02 13:00:00    11\n",
              "2018-01-02 14:00:00    71\n",
              "2018-01-02 15:00:00    65\n",
              "2018-01-02 16:00:00    39\n",
              "2018-01-02 17:00:00    18\n",
              "2018-01-02 18:00:00    90\n",
              "2018-01-02 19:00:00    96\n",
              "2018-01-02 20:00:00    59\n",
              "2018-01-02 21:00:00    22\n",
              "2018-01-02 22:00:00    73\n",
              "2018-01-02 23:00:00    47"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4n_gmrYeDF8A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "df10b483-daae-4736-9f8e-e5f7ed7edca9"
      },
      "source": [
        "df.resample('D').mean()"
      ],
      "execution_count": 35,
      "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>data</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>48.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02</th>\n",
              "      <td>46.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03</th>\n",
              "      <td>36.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04</th>\n",
              "      <td>53.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05</th>\n",
              "      <td>47.791667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-06</th>\n",
              "      <td>62.208333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-07</th>\n",
              "      <td>58.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08</th>\n",
              "      <td>61.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 data\n",
              "datetime             \n",
              "2018-01-01  48.000000\n",
              "2018-01-02  46.750000\n",
              "2018-01-03  36.125000\n",
              "2018-01-04  53.166667\n",
              "2018-01-05  47.791667\n",
              "2018-01-06  62.208333\n",
              "2018-01-07  58.041667\n",
              "2018-01-08  61.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tx20JVO5DK_v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "1642b92a-498f-46cf-ef18-5b392fe94f1f"
      },
      "source": [
        "df['rolling_sum'] = df.rolling(3).sum()\n",
        "df.head(10)"
      ],
      "execution_count": 36,
      "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>data</th>\n",
              "      <th>rolling_sum</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01 00:00:00</th>\n",
              "      <td>60</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 01:00:00</th>\n",
              "      <td>66</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 02:00:00</th>\n",
              "      <td>73</td>\n",
              "      <td>199.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 03:00:00</th>\n",
              "      <td>14</td>\n",
              "      <td>153.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 04:00:00</th>\n",
              "      <td>90</td>\n",
              "      <td>177.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 05:00:00</th>\n",
              "      <td>60</td>\n",
              "      <td>164.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 06:00:00</th>\n",
              "      <td>86</td>\n",
              "      <td>236.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 07:00:00</th>\n",
              "      <td>93</td>\n",
              "      <td>239.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 08:00:00</th>\n",
              "      <td>8</td>\n",
              "      <td>187.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 09:00:00</th>\n",
              "      <td>18</td>\n",
              "      <td>119.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     data  rolling_sum\n",
              "datetime                              \n",
              "2018-01-01 00:00:00    60          NaN\n",
              "2018-01-01 01:00:00    66          NaN\n",
              "2018-01-01 02:00:00    73        199.0\n",
              "2018-01-01 03:00:00    14        153.0\n",
              "2018-01-01 04:00:00    90        177.0\n",
              "2018-01-01 05:00:00    60        164.0\n",
              "2018-01-01 06:00:00    86        236.0\n",
              "2018-01-01 07:00:00    93        239.0\n",
              "2018-01-01 08:00:00     8        187.0\n",
              "2018-01-01 09:00:00    18        119.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YX41s3bMDffd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "106ae864-8aad-494d-cf22-bb63ecd0b50c"
      },
      "source": [
        "df['rolling_sum_backfilled'] = df['rolling_sum'].fillna(method='backfill')\n",
        "df.head(10)"
      ],
      "execution_count": 37,
      "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>data</th>\n",
              "      <th>rolling_sum</th>\n",
              "      <th>rolling_sum_backfilled</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01 00:00:00</th>\n",
              "      <td>60</td>\n",
              "      <td>NaN</td>\n",
              "      <td>199.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 01:00:00</th>\n",
              "      <td>66</td>\n",
              "      <td>NaN</td>\n",
              "      <td>199.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 02:00:00</th>\n",
              "      <td>73</td>\n",
              "      <td>199.0</td>\n",
              "      <td>199.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 03:00:00</th>\n",
              "      <td>14</td>\n",
              "      <td>153.0</td>\n",
              "      <td>153.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 04:00:00</th>\n",
              "      <td>90</td>\n",
              "      <td>177.0</td>\n",
              "      <td>177.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 05:00:00</th>\n",
              "      <td>60</td>\n",
              "      <td>164.0</td>\n",
              "      <td>164.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 06:00:00</th>\n",
              "      <td>86</td>\n",
              "      <td>236.0</td>\n",
              "      <td>236.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 07:00:00</th>\n",
              "      <td>93</td>\n",
              "      <td>239.0</td>\n",
              "      <td>239.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 08:00:00</th>\n",
              "      <td>8</td>\n",
              "      <td>187.0</td>\n",
              "      <td>187.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 09:00:00</th>\n",
              "      <td>18</td>\n",
              "      <td>119.0</td>\n",
              "      <td>119.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     data  rolling_sum  rolling_sum_backfilled\n",
              "datetime                                                      \n",
              "2018-01-01 00:00:00    60          NaN                   199.0\n",
              "2018-01-01 01:00:00    66          NaN                   199.0\n",
              "2018-01-01 02:00:00    73        199.0                   199.0\n",
              "2018-01-01 03:00:00    14        153.0                   153.0\n",
              "2018-01-01 04:00:00    90        177.0                   177.0\n",
              "2018-01-01 05:00:00    60        164.0                   164.0\n",
              "2018-01-01 06:00:00    86        236.0                   236.0\n",
              "2018-01-01 07:00:00    93        239.0                   239.0\n",
              "2018-01-01 08:00:00     8        187.0                   187.0\n",
              "2018-01-01 09:00:00    18        119.0                   119.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Srt0nq8NDoIM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bfe4dcb0-6efa-4b24-f630-7d4c5d9981a5"
      },
      "source": [
        "epoch_t = 1529272655\n",
        "real_t = pd.to_datetime(epoch_t, unit='s')\n",
        "real_t"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2018-06-17 21:57:35')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVVZkLSiFOFt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1e660de4-6565-44a9-bd75-261b1a710b13"
      },
      "source": [
        "real_t.tz_localize('UTC').tz_convert('US/Pacific')\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2018-06-17 14:57:35-0700', tz='US/Pacific')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULv1Hl7EFk5K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "d04492a0-5e9d-4a20-b8d4-3a1f9ce33647"
      },
      "source": [
        "df['data'].plot()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c29d39b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEhCAYAAACTNXDdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmubTChsG7_m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "5a62bcad-0bf9-4199-98fe-70eb7b624a1a"
      },
      "source": [
        "plt.plot(df.index, df.data)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8c29ccdda0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8ArBw8PDwwMA8P8B4tN8BYpKIr4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8aXpBnLHCr-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}