2.2 Pandas Dataframes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Dataframes\n",
    "\n",
    "Boise State University\\\n",
    "Department of Computer Science\\\n",
    "CS 334 Algorithms of Machine Learning\\\n",
    "Conrad Kennington\\\n",
    "Fall 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a simple Python list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_list = [2, 4, 6, 8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert to a Pandas data frame Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_series = pd.Series(my_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try out some functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "3    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.581988897471611"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.000000\n",
       "mean     5.000000\n",
       "std      2.581989\n",
       "min      2.000000\n",
       "25%      3.500000\n",
       "50%      5.000000\n",
       "75%      6.500000\n",
       "max      8.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read a CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, generate some random data -- random names in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# Generate random names\n",
    "names = [\"Albert\",\"John\",\"Richard\",\"Henry\",\"William\"]\n",
    "surnames = [\"Goodman\",\"Black\",\"White\",\"Green\",\"Joneson\"]\n",
    "salaries = [500*random.randint(10,30) for _ in range(10)]\n",
    "\n",
    "def generate_random_person(names, surnames, salaries):\n",
    "    return {\"name\":random.sample(names,1)[0],\n",
    "            \"surname\":random.sample(surnames,1)[0],\n",
    "            \"salary\":random.sample(salaries,1)[0]}\n",
    "\n",
    "def generate_people(k):\n",
    "    return [generate_random_person(names, surnames, salaries) for _ in range(k)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a data frame, and write them to a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(generate_people(50),\n",
    "                  columns=[\"name\",\"surname\",\"salary\"])\n",
    "\n",
    "df.to_csv(\"random_people.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the CSV back into a data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"random_people.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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>name</th>\n",
       "      <th>surname</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Richard</td>\n",
       "      <td>Joneson</td>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albert</td>\n",
       "      <td>Black</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>William</td>\n",
       "      <td>Black</td>\n",
       "      <td>10500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>William</td>\n",
       "      <td>Black</td>\n",
       "      <td>12500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>William</td>\n",
       "      <td>Black</td>\n",
       "      <td>12500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  surname  salary\n",
       "0  Richard  Joneson    9500\n",
       "1   Albert    Black    9000\n",
       "2  William    Black   10500\n",
       "3  William    Black   12500\n",
       "4  William    Black   12500"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # so pretty!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write queries around the dataframe -- almost like MySQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_low = df.loc[df[\"salary\"]<6000,\"salary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     5500\n",
       "11    5500\n",
       "15    5500\n",
       "25    5500\n",
       "26    5500\n",
       "27    5500\n",
       "41    5500\n",
       "44    5500\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_low"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot a histogram from a column. A very useful tool for data science!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[\"salary\"].plot.hist(bins=12, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}