{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# These pandas tips will save you hours of head scratching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To Step Up Your Pandas Game, read:\n",
    "- [5 lesser-known pandas tricks](https://towardsdatascience.com/5-lesser-known-pandas-tricks-e8ab1dd21431)\n",
    "- [Exploratory Data Analysis with pandas](https://towardsdatascience.com/exploratory-data-analysis-with-pandas-508a5e8a5964)\n",
    "- [How NOT to write pandas code](https://towardsdatascience.com/how-not-to-write-pandas-code-ef88599c6e8f)\n",
    "- [These pandas tips will save you hours of headscratching](https://)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "os name: posix\n",
      "system: Darwin\n",
      "release: 18.7.0\n",
      "\n",
      "Python\n",
      "version: 3.7.3\n",
      "\n",
      "Python Packages\n",
      "jupterlab==1.1.5\n",
      "pandas==0.25.3\n",
      "numpy==1.17.4\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import platform\n",
    "from platform import python_version\n",
    "\n",
    "import jupyterlab\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "print(\"System\")\n",
    "print(\"os name: %s\" % os.name)\n",
    "print(\"system: %s\" % platform.system())\n",
    "print(\"release: %s\" % platform.release())\n",
    "print()\n",
    "print(\"Python\")\n",
    "print(\"version: %s\" % python_version())\n",
    "print()\n",
    "print(\"Python Packages\")\n",
    "print(\"jupterlab==%s\" % jupyterlab.__version__)\n",
    "print(\"pandas==%s\" % pd.__version__)\n",
    "print(\"numpy==%s\" % np.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.37454012, 0.95071431, 0.73199394, 0.59865848, 0.15601864,\n",
       "       0.15599452, 0.05808361, 0.86617615, 0.60111501, 0.70807258])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random_sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02058449, 0.96990985, 0.83244264, 0.21233911, 0.18182497,\n",
       "       0.18340451, 0.30424224, 0.52475643, 0.43194502, 0.29122914])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random_sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens when we set the same seed again?\n",
    "We reset the seed and we get the same sequence of numbers as above.\n",
    "This makes deterministic pseudorandom number generator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.37454012, 0.95071431, 0.73199394, 0.59865848, 0.15601864,\n",
       "       0.15599452, 0.05808361, 0.86617615, 0.60111501, 0.70807258])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random_sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
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       "      <th>2</th>\n",
       "      <td>b</td>\n",
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       "      <td>c</td>\n",
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       "      <th>4</th>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col1\n",
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "4    d"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(index=[1, 2, 3, 4, 4], data={\"col1\": [\"a\", \"b\", \"c\", \"d\", \"d\"]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Dataframe has duplicates",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-bb57873a8d0e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mduplicated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Dataframe has duplicates\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m: Dataframe has duplicates"
     ]
    }
   ],
   "source": [
    "assert len(df[df.index.duplicated()]) == 0, \"Dataframe has duplicates\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df.join(df, lsuffix='_l', rsuffix='_r')\n",
    "df_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1_l</th>\n",
       "      <th>col1_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>a</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>b</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col1_l col1_r\n",
       "1      a      a\n",
       "2      b      b\n",
       "3      c      c\n",
       "4      d      d\n",
       "4      d      d\n",
       "4      d      d\n",
       "4      d      d"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</table>\n",
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      ],
      "text/plain": [
       "  col1\n",
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "4    d"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "  col1\n",
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "4    d"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "df = pd.read_clipboard(sep='\\s\\s+')\n",
    "df"
   ]
  }
 ],
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