lectures/05_week_lab.ipynb

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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
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    "## COMP 3122 - Artificial Intelligence with Python\n",
    "__Week 5 lab__\n",
    "\n",
    "### [github.com/kamrik/ML1](https://github.com/kamrik/ML1)"
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   "source": [
    "## The plan for today\n",
    " * Home assignment (due next week)\n",
    " * Working with date & time and more fun with Pandas and plotting"
   ]
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    "## Home assignment is due next week (Tuesday night)\n",
    " * The grader bot is up and responding - you have up to 5 attempts to submit, the best counts\n",
    " * If you haven't yet started working on it, start today during this lab\n",
    " * Any questions - ask them here / on Slack / via email"
   ]
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    "## Pandas exercise\n",
    " * Why dwell on this so much - this is by far the most important part of the course\n",
    " * The videos (and coressponding notebook) is the place to go for more info\n",
    " * Good detailed description of datetime in Pandas in [book section 3.11](https://jakevdp.github.io/PythonDataScienceHandbook/03.11-working-with-time-series.html)"
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    "## Datetime in Pandas\n",
    "  * `pd.reads_csv(..., parse_dates=['Column1', 'Column2'])`\n",
    "  * `pd.to_datetime()`\n",
    "  * `pd.to_timedelta()`\n"
   ]
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  {
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   "source": [
    "## Timestamp and Timedelta objects"
   ]
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     "data": {
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       "Timestamp('2018-10-01 22:23:29.710977')"
      ]
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     "execution_count": 31,
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   "source": [
    "pd.Timestamp.now()"
   ]
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    {
     "data": {
      "text/plain": [
       "Timedelta('2 days 00:00:00')"
      ]
     },
     "execution_count": 32,
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   "source": [
    "dt = pd.to_timedelta(2, 'D')\n",
    "dt"
   ]
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    {
     "data": {
      "text/plain": [
       "Timestamp('2018-10-03 22:23:42.329758')"
      ]
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     "execution_count": 33,
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   "source": [
    "pd.Timestamp.now() + dt"
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   "execution_count": 38,
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     "data": {
      "text/plain": [
       "TimedeltaIndex(['1 days', '3 days', '5 days'], dtype='timedelta64[ns]', freq=None)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pd.to_timedelta(np.arange(1,7,2), 'D')"
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   "execution_count": null,
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   "execution_count": null,
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   "source": [
    "## Visualizing Seattle Bicycle Counts (from the book) \n",
    "https://jakevdp.github.io/PythonDataScienceHandbook/03.11-working-with-time-series.html#Example:-Visualizing-Seattle-Bicycle-Counts"
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