ds_workshop.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "40077d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (10, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf3e0987",
   "metadata": {},
   "source": [
    "Hey this is my markdown cell!! \n",
    "<h1>Hellooo</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61dfa21e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><h2>CWH Data Science Workshop!🥳</h2></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf518ca",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What is data science? 🤔"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2145c39",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What is data science?\n",
    "\n",
    "<br>\n",
    "\n",
    "<center><img src='imgs/what-is-data-science.png' width=60%></center>\n",
    "\n",
    "<center>Everyone seems to have their own definition of what data science is.</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efabcfc5",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Let's look at definitions of data science."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1efb892b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What is data science?\n",
    "\n",
    "<center><img src=\"imgs/image_0.png\"></center>\n",
    "\n",
    "In 2010, Drew Conway published his famous [Data Science Venn Diagram](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d0b484f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What is data science?\n",
    "\n",
    "There isn't agreement on which \"Venn Diagram\" is correct!\n",
    "\n",
    "<center><img src=\"imgs/image_1.png\" width=500></center>\n",
    "\n",
    "- **Why not?** The field is new and rapidly developing.\n",
    "- Make sure you're solid on the fundamentals, then find a niche that you enjoy.\n",
    "- Read Kolassa, [Battle of the Data Science Venn Diagrams](http://www.prooffreader.com/2016/09/battle-of-data-science-venn-diagrams.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd48a8ec",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What does a _data scientist_ do?\n",
    "\n",
    "The chart below is taken from the [2016 Data Science Salary Survey](https://www.oreilly.com/radar/2016-data-science-salary-survey-results/), administered by O'Reilly. They asked respondents what they spend their time doing on a daily basis. What do you notice? <br>\n",
    "\n",
    "<center><img src='imgs/survey.png' width=40%></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e164e0c6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The chart below is taken from the followup [2021 Data/AI Salary Survey](https://www.oreilly.com/radar/2021-data-ai-salary-survey/), also administered by O'Reilly. They asked respondents:\n",
    "\n",
    "> What technologies will have the biggest effect on compensation in the coming year?\n",
    "\n",
    "<center><img src='imgs/2021-most-relevant-skill.png' width=45%></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd1a883b",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "As you take more courses, you train to ask questions whose answers are **ambiguous** – this uncertainly is what makes data science challenging!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2f8d88",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Let's look at some examples of data science in practice."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86af436e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Analyzing Wordle trends\n",
    "\n",
    "<center><img src='imgs/wordle-moving-average.png' width=70%></center>\n",
    "    \n",
    "Moving average of the average number of guesses taken for each Wordle word, based on patterns shared on Twitter. ([source](https://observablehq.com/@rlesser/wordle-twitter-exploration))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69df9b57",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Is Wordle Dying? The Data Weighs In\n",
    "\n",
    "<center><img src=\"imgs/wordle-drop.png\" width=40%></center>\n",
    "\n",
    "> Compared to the peak of 350,000 shares in mid-February, a typical day in September 2022 only saw about 32,000 Wordle shares on Twitter. That’s a 91 percent drop in a span of seven months. \n",
    "([source](https://wordfinder.yourdictionary.com/blog/is-wordle-dying-the-data-weighs-in/))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd37733",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Data science involves _people_ 🧍\n",
    "\n",
    "The decisions that we make as data scientists have the potential to impact the livelihoods of other people.\n",
    "\n",
    "- COVID case forecasting.\n",
    "- Admissions and hiring.\n",
    "- Hyper-personalized ad recommendations.\n",
    "- Criminal sentencing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfdae8ce",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Warning!\n",
    "\n",
    "- Good data analysis is not:\n",
    "    - A simple application of a statistics formula.\n",
    "    - A simple application of statistical software.\n",
    "\n",
    "- There are many tools out there for data science, but they are merely tools. **They don’t do any of the important thinking – that's where you come in!**\n",
    "\n",
    "> _“The purpose of computing is insight, not numbers.”_ - R. Hamming. Numerical Methods for Scientists and Engineers (1962)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3bb2a04",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The data science lifecycle 🚴"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd51458",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The scientific method\n",
    "\n",
    "You learned about the scientific method in elementary school. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621c38e0",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<center><img src=\"imgs/image_3.png\" width=500></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56ee9fdd",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "However, it hides a lot of complexity.\n",
    "- Where did the hypothesis come from?\n",
    "- What data are you modeling? Is the data sufficient?\n",
    "- Under which conditions are the conclusions valid?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a45f8c4",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The data science lifecycle\n",
    "\n",
    "<center><img src=\"imgs/DSLC.png\" width=\"40%\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb7af57",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**All steps lead to more questions!** We'll refer back to the data science lifecycle repeatedly throughout the quarter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f94c4317",
   "metadata": {},
   "source": [
    "# Let's start looking at actual data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fb2e04f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(626, 8)\n"
     ]
    },
    {
     "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>state</th>\n",
       "      <th>numcol</th>\n",
       "      <th>yieldpercol</th>\n",
       "      <th>totalprod</th>\n",
       "      <th>stocks</th>\n",
       "      <th>priceperlb</th>\n",
       "      <th>prodvalue</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1136000.0</td>\n",
       "      <td>159000.0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>818000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>60</td>\n",
       "      <td>3300000.0</td>\n",
       "      <td>1485000.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2112000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>65</td>\n",
       "      <td>3445000.0</td>\n",
       "      <td>1688000.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2033000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>83</td>\n",
       "      <td>37350000.0</td>\n",
       "      <td>12326000.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>23157000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CO</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>72</td>\n",
       "      <td>1944000.0</td>\n",
       "      <td>1594000.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1361000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state    numcol  yieldpercol   totalprod      stocks  priceperlb  \\\n",
       "0    AL   16000.0           71   1136000.0    159000.0        0.72   \n",
       "1    AZ   55000.0           60   3300000.0   1485000.0        0.64   \n",
       "2    AR   53000.0           65   3445000.0   1688000.0        0.59   \n",
       "3    CA  450000.0           83  37350000.0  12326000.0        0.62   \n",
       "4    CO   27000.0           72   1944000.0   1594000.0        0.70   \n",
       "\n",
       "    prodvalue  year  \n",
       "0    818000.0  1998  \n",
       "1   2112000.0  1998  \n",
       "2   2033000.0  1998  \n",
       "3  23157000.0  1998  \n",
       "4   1361000.0  1998  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframe with info\n",
    "df = pd.read_csv(\"data/honeyproduction.csv\")\n",
    "print (df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7f077839",
   "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>state</th>\n",
       "      <th>numcol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>SC</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>OK</td>\n",
       "      <td>27000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MD</td>\n",
       "      <td>28000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KY</td>\n",
       "      <td>66000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>VT</td>\n",
       "      <td>85000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state   numcol\n",
       "33    SC  16000.0\n",
       "30    OK  27000.0\n",
       "15    MD  28000.0\n",
       "13    KY  66000.0\n",
       "39    VT  85000.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_state_col = df.groupby(\"state\")[\"numcol\"].sum().reset_index().sort_values(by=\"numcol\")\n",
    "df_state_col.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4d132d6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 44 artists>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df_state_col[\"state\"], df_state_col[\"numcol\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b1e70d67",
   "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>state</th>\n",
       "      <th>numcol</th>\n",
       "      <th>yieldpercol</th>\n",
       "      <th>totalprod</th>\n",
       "      <th>stocks</th>\n",
       "      <th>priceperlb</th>\n",
       "      <th>prodvalue</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1136000.0</td>\n",
       "      <td>159000.0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>818000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>60</td>\n",
       "      <td>3300000.0</td>\n",
       "      <td>1485000.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2112000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>65</td>\n",
       "      <td>3445000.0</td>\n",
       "      <td>1688000.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2033000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>83</td>\n",
       "      <td>37350000.0</td>\n",
       "      <td>12326000.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>23157000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CO</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>72</td>\n",
       "      <td>1944000.0</td>\n",
       "      <td>1594000.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1361000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state    numcol  yieldpercol   totalprod      stocks  priceperlb  \\\n",
       "0    AL   16000.0           71   1136000.0    159000.0        0.72   \n",
       "1    AZ   55000.0           60   3300000.0   1485000.0        0.64   \n",
       "2    AR   53000.0           65   3445000.0   1688000.0        0.59   \n",
       "3    CA  450000.0           83  37350000.0  12326000.0        0.62   \n",
       "4    CO   27000.0           72   1944000.0   1594000.0        0.70   \n",
       "\n",
       "    prodvalue  year  \n",
       "0    818000.0  1998  \n",
       "1   2112000.0  1998  \n",
       "2   2033000.0  1998  \n",
       "3  23157000.0  1998  \n",
       "4   1361000.0  1998  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "23aae75b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ND'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"prodvalue\", ascending=False)[\"state\"].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "858f7cf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(2010)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.sort_values(by=\"prodvalue\", ascending=False)[\"year\"].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "258844d2",
   "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>state</th>\n",
       "      <th>numcol</th>\n",
       "      <th>yieldpercol</th>\n",
       "      <th>totalprod</th>\n",
       "      <th>stocks</th>\n",
       "      <th>priceperlb</th>\n",
       "      <th>prodvalue</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>83</td>\n",
       "      <td>37350000.0</td>\n",
       "      <td>12326000.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>23157000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>CA</td>\n",
       "      <td>465000.0</td>\n",
       "      <td>60</td>\n",
       "      <td>27900000.0</td>\n",
       "      <td>9486000.0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>15345000.0</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>CA</td>\n",
       "      <td>440000.0</td>\n",
       "      <td>70</td>\n",
       "      <td>30800000.0</td>\n",
       "      <td>11396000.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>18172000.0</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>CA</td>\n",
       "      <td>460000.0</td>\n",
       "      <td>61</td>\n",
       "      <td>28060000.0</td>\n",
       "      <td>7857000.0</td>\n",
       "      <td>0.69</td>\n",
       "      <td>19361000.0</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>CA</td>\n",
       "      <td>470000.0</td>\n",
       "      <td>50</td>\n",
       "      <td>23500000.0</td>\n",
       "      <td>3525000.0</td>\n",
       "      <td>1.32</td>\n",
       "      <td>31020000.0</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>CA</td>\n",
       "      <td>480000.0</td>\n",
       "      <td>67</td>\n",
       "      <td>32160000.0</td>\n",
       "      <td>6432000.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>44702000.0</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>CA</td>\n",
       "      <td>390000.0</td>\n",
       "      <td>45</td>\n",
       "      <td>17550000.0</td>\n",
       "      <td>5792000.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>18428000.0</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>CA</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>75</td>\n",
       "      <td>30000000.0</td>\n",
       "      <td>9300000.0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>25800000.0</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>CA</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>52</td>\n",
       "      <td>19760000.0</td>\n",
       "      <td>7706000.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>19167000.0</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>CA</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>40</td>\n",
       "      <td>13600000.0</td>\n",
       "      <td>3672000.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>14144000.0</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>CA</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>51</td>\n",
       "      <td>18360000.0</td>\n",
       "      <td>4039000.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>25520000.0</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>CA</td>\n",
       "      <td>355000.0</td>\n",
       "      <td>33</td>\n",
       "      <td>11715000.0</td>\n",
       "      <td>2109000.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>16284000.0</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>CA</td>\n",
       "      <td>410000.0</td>\n",
       "      <td>67</td>\n",
       "      <td>27470000.0</td>\n",
       "      <td>6318000.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>42579000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>CA</td>\n",
       "      <td>370000.0</td>\n",
       "      <td>48</td>\n",
       "      <td>17760000.0</td>\n",
       "      <td>3730000.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>29304000.0</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>CA</td>\n",
       "      <td>330000.0</td>\n",
       "      <td>35</td>\n",
       "      <td>11550000.0</td>\n",
       "      <td>3119000.0</td>\n",
       "      <td>1.94</td>\n",
       "      <td>22407000.0</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state    numcol  yieldpercol   totalprod      stocks  priceperlb  \\\n",
       "3      CA  450000.0           83  37350000.0  12326000.0        0.62   \n",
       "46     CA  465000.0           60  27900000.0   9486000.0        0.55   \n",
       "89     CA  440000.0           70  30800000.0  11396000.0        0.59   \n",
       "132    CA  460000.0           61  28060000.0   7857000.0        0.69   \n",
       "176    CA  470000.0           50  23500000.0   3525000.0        1.32   \n",
       "220    CA  480000.0           67  32160000.0   6432000.0        1.39   \n",
       "264    CA  390000.0           45  17550000.0   5792000.0        1.05   \n",
       "305    CA  400000.0           75  30000000.0   9300000.0        0.86   \n",
       "346    CA  380000.0           52  19760000.0   7706000.0        0.97   \n",
       "387    CA  340000.0           40  13600000.0   3672000.0        1.04   \n",
       "428    CA  360000.0           51  18360000.0   4039000.0        1.39   \n",
       "469    CA  355000.0           33  11715000.0   2109000.0        1.39   \n",
       "509    CA  410000.0           67  27470000.0   6318000.0        1.55   \n",
       "549    CA  370000.0           48  17760000.0   3730000.0        1.65   \n",
       "589    CA  330000.0           35  11550000.0   3119000.0        1.94   \n",
       "\n",
       "      prodvalue  year  \n",
       "3    23157000.0  1998  \n",
       "46   15345000.0  1999  \n",
       "89   18172000.0  2000  \n",
       "132  19361000.0  2001  \n",
       "176  31020000.0  2002  \n",
       "220  44702000.0  2003  \n",
       "264  18428000.0  2004  \n",
       "305  25800000.0  2005  \n",
       "346  19167000.0  2006  \n",
       "387  14144000.0  2007  \n",
       "428  25520000.0  2008  \n",
       "469  16284000.0  2009  \n",
       "509  42579000.0  2010  \n",
       "549  29304000.0  2011  \n",
       "589  22407000.0  2012  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"state\"]==\"CA\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c23073",
   "metadata": {},
   "source": [
    "## THANK YOU! EMAIL ME IF YOU HAVE ANY QUESTIONS lina@codewithher.org"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25120206",
   "metadata": {},
   "source": [
    "<a href=\"https://docs.google.com/presentation/d/12sVjg4JE2vmizg_64lVnUQeuSmwSi41-6pxdtnfagFY/edit?usp=sharing\">Data Science Career Paths Slides!</a> <br>\n",
    "<a href=\"https://www.geeksforgeeks.org/introduction-to-pandas-in-python/\">Learn more about pandas!</a> <br>\n",
    "<a href=\"https://jimmypang.medium.com/data-science-in-a-nutshell-db351fa41c64\">Data Science in a nutshell</a> \n"
   ]
  }
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