{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Reshaping data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import pandas as pd\nimport numpy as np\n\n\ntuples = list(\n    zip(\n        *[\n            [\"bar\", \"bar\", \"baz\", \"baz\", \"foo\", \"foo\", \"qux\", \"qux\"],\n            [\"one\", \"two\", \"one\", \"two\", \"one\", \"two\", \"one\", \"two\"],\n        ]\n    )\n)\nindex = pd.MultiIndex.from_tuples(tuples, names=[\"first\", \"second\"])\ndataFrame = pd.DataFrame(np.random.randn(8, 2), index=index, columns=[\"A\", \"B\"])\ndataFrame2 = dataFrame[:4]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Stack**\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "stacked = dataFrame2.stack()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "stacked.unstack()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "stacked.unstack(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "stacked.unstack(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Pivot tables**\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "dataFrame = pd.DataFrame(\n    {\n        \"A\": [\"one\", \"one\", \"two\", \"three\"] * 3,\n        \"B\": [\"A\", \"B\", \"C\"] * 4,\n        \"C\": [\"foo\", \"foo\", \"foo\", \"bar\", \"bar\", \"bar\"] * 2,\n        \"D\": np.random.randn(12),\n        \"E\": np.random.randn(12),\n    }\n)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "pd.pivot_table(dataFrame, values=\"D\", index=[\"A\", \"B\"], columns=[\"C\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Time series**\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "indexData = pd.date_range(\"1/5/2022\", periods=100, freq=\"S\")\ntimeStemps = pd.Series(np.random.randint(0, 500, len(indexData)), index=indexData)\ntimeStemps.resample(\"5Min\").sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "timeStempsUTC = timeStemps.tz_localize(\"UTC\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "timeStempsUTC.tz_convert(\"US/Eastern\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ps = timeStemps.to_period()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ps.to_timestamp()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "prng = pd.period_range(\"1990Q1\", \"2000Q4\", freq=\"Q-NOV\")\nts = pd.Series(np.random.randn(len(prng)), prng)\nts.index = (prng.asfreq(\"M\", \"e\") + 1).asfreq(\"H\", \"s\") + 9"
      ]
    }
  ],
  "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": "3.8.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}