{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gwDkBYVEqm5N"
   },
   "source": [
    "# DATA 311 - Lecture 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YTajC-alqoaU"
   },
   "source": [
    "## Announcements\n",
    "* Our first Data Ethics assignment is out; due Wednesday before class. You will:\n",
    "  * Download all the data that some tech company has on you\n",
    "  * Poke around\n",
    "  * Write a short reflection\n",
    "  * Discuss in class on Wednesday 4/15\n",
    "* Quiz 1 is Friday, covering material through today.\n",
    "* Today's lecture: you may want to follow along in our own copy of the notebook. You can grab the ipynb I'm working with from the course webpage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I0u1te4zNVlO"
   },
   "source": [
    "## Goals\n",
    "* Know how to use integer and boolean indexing in numpy.\n",
    "* Understand the fundamental data structures and concepts of the `pandas` library, and how they relate to each other:\n",
    "  * Series, DataFrame, index\n",
    "\n",
    "* Know enough about pandas to be able to do, or look up how to do, the following basic data manipulation tasks:\n",
    "  * Show the first or last k rows\n",
    "  * Drop columns from a DataFrame\n",
    "  * Get the dimensions of the table\n",
    "  * Extract a single column\n",
    "  * Extract multiple columns\n",
    "  * Extract a single column as a DataFrame\n",
    "  * Sort the table on a column\n",
    "  * Get a custom slice of rows\n",
    "  * Count number of rows with each value in a categorical column\n",
    "  * Plot a column as a line graph\n",
    "  * Scatterplot 2 columns\n",
    "  * Group by a categorical variable and apply reductions to each group\n",
    "  * Get only rows that meet some condition\n",
    "  * Show summary statistics of a DataFrame\n",
    "\n",
    "We may not get to all of these today, but by the time you complete Lab 2, you should be able to accomplish the above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lIDbmsS7ucjM"
   },
   "source": [
    "### Advantage of Jupyter:\n",
    "\n",
    "* **Reproducibility**. Important rule of data science: **Always start with the raw data**, and **document all processing and analysis** so anyone can reproduce your findings (and understand what assumptions / decisions you made. Notebooks help you do this seamlessly.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy, Continued\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array(range(12)).reshape((4, 3))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:2,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fancy indexing\n",
    "* Integer indexing: `a[ list or ndarray of integer indices ]`\n",
    "* Boolean indexing: `a[ list or ndarray of booleans ]` where the list/ndarray's shape matches a's\n",
    "\n",
    "\n",
    "See <https://numpy.org/doc/stable/user/basics.indexing.html> for much more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Integer indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array(range(10, 20))\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing with a list or array of integers pulls out only the elements at those indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 12, 14])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the first, third, and fifth elements:\n",
    "a[np.array([0, 2, 4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 11, 11])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the fourth, second, and second elements (!):\n",
    "a[np.array([3, 1, 1])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean Indexing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.ones((2, 2))\n",
    "b[0,0] = 2\n",
    "b[1,1] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Quick quiz: what does b look like now?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 1.],\n",
       "       [1., 0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a \"mask\" of booleans that's the same shape as `b`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True, False],\n",
       "       [False,  True]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = np.array([\n",
    "    [True, False],\n",
    "    [False, True]\n",
    "])\n",
    "mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Index b with a boolean mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 0.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common pattern - boolean operators to generate a mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 1., 1.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pull out all elements of b that are greater than zero:\n",
    "b[b > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tips for multidimensional arrays\n",
    "\n",
    "* I never display anything that's more than 2D.\n",
    "* I never try to visualize anything that's more than 3D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2],\n",
       "        [ 3,  4,  5],\n",
       "        [ 6,  7,  8],\n",
       "        [ 9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14],\n",
       "        [15, 16, 17],\n",
       "        [18, 19, 20],\n",
       "        [21, 22, 23]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.array(range(24)).reshape(2, 4, 3)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  3,  6,  9],\n",
       "       [12, 15, 18, 21]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take one 2D slice\n",
    "c[:,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  4,  5],\n",
       "       [15, 16, 17]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take another 2D slice along a different axis\n",
    "c[:, 1, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3 Play with my cat\n",
    "\n",
    "**In pairs** (seriously - one computer open per pair please): In this exercise, we'll manipulate an image as a 2D array.\n",
    "\n",
    "We'll start by loading a picture of my cat, Beans:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imageio.v3 as imageio\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "beans = imageio.imread(\"/cluster/academic/DATA311/202620/beans_gray.jpeg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use `plt.imshow` to visualize the image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14ee7d243260>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(beans, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 200)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beans.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. What is the dtype of the resulting array? What are the minimum and maximum values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beans.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Display a binary image showing which pixels are greater than half the maximum pixel intensity (127)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14ee7cf047a0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(beans>127, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. What is the average value of pixels that have intensity value above 127?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(160.46054636482367)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pixels_of_interest = beans[beans > 127]\n",
    "pixels_of_interest.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.uint64(4149006)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(beans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Which column of the image has the highest average pixel value?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(193)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beans.mean(axis=0).argmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "enudqw5wuIzd"
   },
   "source": [
    "## Pandas: a library for working with tabular data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Orvj8o3tFnY"
   },
   "source": [
    "### How to Learn Pandas (and other tools we'll use in this class):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Orvj8o3tFnY"
   },
   "source": [
    "<img src=\"https://facultyweb.cs.wwu.edu/~wehrwes/courses/data311_23w/lectures/L02/changing.jpg\" style=\"height: 300px\"/>\n",
    "<img src=\"https://facultyweb.cs.wwu.edu/~wehrwes/courses/data311_23w/lectures/L02/googling.jpg\" style=\"height: 300px\"/>\n",
    "<img src=\"https://facultyweb.cs.wwu.edu/~wehrwes/courses/data311_23w/lectures/L02/copying.jpg\" style=\"height: 300px\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Orvj8o3tFnY"
   },
   "source": [
    "#### But seriously\n",
    "\n",
    "I won't teach you every little thing you need to use. I will expect you to be able to find and use functionality that gets the job done. The Lab 2 handout has some suggestions for how to go about searching and learning process. I also won't quiz/test you on syntactic minutia, though the basics are fair game."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fYQ1yjSqwRjB"
   },
   "source": [
    "### Pandas: basic data structures/concepts\n",
    "\n",
    "* Series\n",
    "* Index\n",
    "* DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1673460403229,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "MIHWgtOmwV7y"
   },
   "outputs": [],
   "source": [
    "from pandas import Series, DataFrame\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1T9HELgYw7dr"
   },
   "source": [
    "#### Series - a 1D list-like thing (think of it as a column with labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1673460403229,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "mUfgrUiiwROa",
    "outputId": "eb9adb7b-eec5-4c05-a6ef-68db8d8ec6f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9\n",
       "1    6\n",
       "2    8\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = Series([9,6,8,4])\n",
    "s\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values are the items in the Series themselves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 6, 8, 4])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the values:\n",
    "s.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The indices are the \"labels\" - the left column in the display above. We didn't provide labels, so they defaulted to sequential integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the index:\n",
    "s.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Square bracket indexing pulls out the value at an index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(8)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the third value:\n",
    "s[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D071NzluxBO5"
   },
   "source": [
    "We can customize the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1673460403230,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "J6RiKnmNwwjp",
    "outputId": "311fb03a-827a-41b0-9474-0124707b81f7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "win    9\n",
       "spr    6\n",
       "sum    8\n",
       "fal    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = Series([9,6,8,4],index=['win','spr','sum','fal'])\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 6, 8, 4])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the values:\n",
    "s2.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['win', 'spr', 'sum', 'fal'], dtype='str')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the indices:\n",
    "s2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(9)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the value at index \"win\":\n",
    "s2[\"win\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if I want the second thing? Don't do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/miniforge/lib/python3.12/site-packages/pandas/core/indexes/base.py:3641\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3640\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3641\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   3642\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:168\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m168\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:176\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m176\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:583\u001b[39m, in \u001b[36mpandas._libs.index.StringObjectEngine._check_type\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m583\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n",
      "\u001b[31mKeyError\u001b[39m: 1",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[33]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m s2[\u001b[32m1\u001b[39m]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/miniforge/lib/python3.12/site-packages/pandas/core/series.py:959\u001b[39m, in \u001b[36mSeries.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m    954\u001b[39m     key = unpack_1tuple(key)\n\u001b[32m    956\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[32m    957\u001b[39m     \u001b[38;5;66;03m# Note: GH#50617 in 3.0 we changed int key to always be treated as\u001b[39;00m\n\u001b[32m    958\u001b[39m     \u001b[38;5;66;03m#  a label, matching DataFrame behavior.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m959\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    961\u001b[39m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[32m    962\u001b[39m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[32m    963\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/miniforge/lib/python3.12/site-packages/pandas/core/series.py:1046\u001b[39m, in \u001b[36mSeries._get_value\u001b[39m\u001b[34m(self, label, takeable)\u001b[39m\n\u001b[32m   1043\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._values[label]\n\u001b[32m   1045\u001b[39m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1046\u001b[39m loc = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1048\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[32m   1049\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._values[loc]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/miniforge/lib/python3.12/site-packages/pandas/core/indexes/base.py:3648\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3643\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   3644\u001b[39m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc.Iterable)\n\u001b[32m   3645\u001b[39m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[32m   3646\u001b[39m     ):\n\u001b[32m   3647\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3648\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m   3649\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m   3650\u001b[39m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[32m   3651\u001b[39m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[32m   3652\u001b[39m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[32m   3653\u001b[39m     \u001b[38;5;28mself\u001b[39m._check_indexing_error(key)\n",
      "\u001b[31mKeyError\u001b[39m: 1"
     ]
    }
   ],
   "source": [
    "s2[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "instead do this:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(6)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the second thing using iloc\n",
    "s2.iloc[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`iloc` allows you to use numerical (numpy-like) indexing into a Series or dataframe even when its index has different labels.\n",
    "\n",
    "Notice that `iloc` is, weirdly, not a function - it's `.iloc[ind]`, not `.iloc(ind)`.\n",
    "\n",
    "Slicing works too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spr    6\n",
       "sum    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the second and third things:\n",
    "s2.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "19bEuGE2xCin"
   },
   "source": [
    "We can create a Series from a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1673460403230,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "S1ut7NKxw6M5",
    "outputId": "b5a5f963-d1e5-4f59-9e23-a2d7e2fa8ab7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "win    9\n",
       "spr    6\n",
       "sum    8\n",
       "fal    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {}\n",
    "d['win'] = 9\n",
    "d['spr'] = 6\n",
    "d['sum'] = 8\n",
    "d['fal'] = 4\n",
    "s3 = Series(d)\n",
    "s3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jr4jvxkLxKJQ"
   },
   "source": [
    "Many things that work on dictionaries and lists work on Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1673460403230,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "Iq_Y-oICxJqJ",
    "outputId": "bb7147e9-d005-4ae8-d9bb-6fcc08100277"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# is 'fal' a key in s3? using the in keyword\n",
    "'fal' in s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# is 'jan' a key in s3?\n",
    "'jan' in s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# is 6 a value in s3?\n",
    "6 in s3.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrames\n",
    "DataFrames represent 2D tables.\n",
    "\n",
    "Create a DataFrame from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1673460403230,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "xYf_-B4vxZEn",
    "outputId": "6ef88314-feb0-4172-f5f0-148acff756fb"
   },
   "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>city</th>\n",
       "      <th>pop</th>\n",
       "      <th>tax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>787</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spokane</td>\n",
       "      <td>230</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tacoma</td>\n",
       "      <td>222</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vancouver</td>\n",
       "      <td>189</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  pop    tax\n",
       "0    Seattle  787  10.25\n",
       "1    Spokane  230   9.00\n",
       "2     Tacoma  222  10.30\n",
       "3  Vancouver  189   8.50"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'city': ['Seattle','Spokane','Tacoma','Vancouver'],\n",
    "        'pop': [787,230,222,189], # units are in thousands\n",
    "        'tax': [10.25,9.0,10.3,8.5]}\n",
    "df = DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each column is a Series. Indexing the DataFrame by the column name extracts the Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1673460403231,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "67rM5s8wxfBH",
    "outputId": "3f669fac-f405-4e52-dcfa-39cb0e6cb3f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Seattle\n",
       "1      Spokane\n",
       "2       Tacoma\n",
       "3    Vancouver\n",
       "Name: city, dtype: str"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the city column using square brackets:\n",
    "df[\"city\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to access a column; generally prefer the square brackets, since column names can have spaces and other weirdness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Seattle\n",
       "1      Spokane\n",
       "2       Tacoma\n",
       "3    Vancouver\n",
       "Name: city, dtype: str"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the city column using property accessor:\n",
    "df.city"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This might not always be preferred, since column names are not always valid python identifiers (e.g. they can have spaces, dashes, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C1cbz69sypf1"
   },
   "source": [
    "Elementwise arithmetic works on Series (they are based on numpy arrays):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1673460403231,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "LhYZBMvhymtd",
    "outputId": "214241d9-e6ab-43b6-80c5-6a73911110ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.1025\n",
       "1    0.0900\n",
       "2    0.1030\n",
       "3    0.0850\n",
       "Name: tax, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# divide the tax column by 100:\n",
    "df[\"tax\"] / 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b5AW94xSxpZv"
   },
   "source": [
    "Add a column to an existing DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1673460403821,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "RcQqrF9Yxmg4",
    "outputId": "1c9b92b8-b79b-4cdb-9fcc-4818f6f5aded"
   },
   "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>city</th>\n",
       "      <th>pop</th>\n",
       "      <th>tax</th>\n",
       "      <th>visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>787</td>\n",
       "      <td>10.25</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spokane</td>\n",
       "      <td>230</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tacoma</td>\n",
       "      <td>222</td>\n",
       "      <td>10.30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vancouver</td>\n",
       "      <td>189</td>\n",
       "      <td>8.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  pop    tax  visits\n",
       "0    Seattle  787  10.25      20\n",
       "1    Spokane  230   9.00       2\n",
       "2     Tacoma  222  10.30       5\n",
       "3  Vancouver  189   8.50       4"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['visits'] = [20,2,5,4]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More pandas, now with Avengers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oBoYkup7g_Vj"
   },
   "source": [
    "For demo purposes, we'll use a dataset downloaded from [FiveThirtyEight](https://data.fivethirtyeight.com/), which compiled it for a 2015 article entitled [Joining The Avengers Is As Deadly As Jumping Off A Four-Story Building](https://fivethirtyeight.com/features/avengers-death-comics-age-of-ultron/). It catalogs information about all of the characters from the Marvel comic books that were ever members of the Avengers. You can find some meta-information about the dataset including a description of what each column means in the accompanying [readme file](https://fw.cs.wwu.edu/~wehrwes/courses/data311_23w/data/avengers/README.md) (it's in Markdown format; one easy way to display it nicely would be to paste its contents into a Markdown cell in a notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1673460402152,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "5ZP2IihCuNUt"
   },
   "outputs": [],
   "source": [
    "data_url = '/cluster/academic/DATA311/202620/avengers/avengers.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 1080,
     "status": "ok",
     "timestamp": 1673460403229,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "Rz0A3bxYun8u",
    "outputId": "67ffeac0-bb46-46eb-b993-11fe3d676228"
   },
   "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>URL</th>\n",
       "      <th>Name/Alias</th>\n",
       "      <th>Appearances</th>\n",
       "      <th>Current?</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Probationary Introl</th>\n",
       "      <th>Full/Reserve Avengers Intro</th>\n",
       "      <th>Year</th>\n",
       "      <th>Years since joining</th>\n",
       "      <th>Honorary</th>\n",
       "      <th>...</th>\n",
       "      <th>Return1</th>\n",
       "      <th>Death2</th>\n",
       "      <th>Return2</th>\n",
       "      <th>Death3</th>\n",
       "      <th>Return3</th>\n",
       "      <th>Death4</th>\n",
       "      <th>Return4</th>\n",
       "      <th>Death5</th>\n",
       "      <th>Return5</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://marvel.wikia.com/Henry_Pym_(Earth-616)</td>\n",
       "      <td>Henry Jonathan \"Hank\" Pym</td>\n",
       "      <td>1269</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Merged with Ultron in Rage of Ultron Vol. 1. A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://marvel.wikia.com/Janet_van_Dyne_(Earth-...</td>\n",
       "      <td>Janet van Dyne</td>\n",
       "      <td>1165</td>\n",
       "      <td>YES</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dies in Secret Invasion V1:I8. Actually was se...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://marvel.wikia.com/Anthony_Stark_(Earth-616)</td>\n",
       "      <td>Anthony Edward \"Tony\" Stark</td>\n",
       "      <td>3068</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Death: \"Later while under the influence of Imm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://marvel.wikia.com/Robert_Bruce_Banner_(E...</td>\n",
       "      <td>Robert Bruce Banner</td>\n",
       "      <td>2089</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dies in Ghosts of the Future arc. However \"he ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://marvel.wikia.com/Thor_Odinson_(Earth-616)</td>\n",
       "      <td>Thor Odinson</td>\n",
       "      <td>2402</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dies in Fear Itself brought back because that'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>http://marvel.wikia.com/Eric_Brooks_(Earth-616)#</td>\n",
       "      <td>Eric Brooks</td>\n",
       "      <td>198</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13-Nov</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>http://marvel.wikia.com/Adam_Brashear_(Earth-6...</td>\n",
       "      <td>Adam Brashear</td>\n",
       "      <td>29</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14-Jan</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>http://marvel.wikia.com/Victor_Alvarez_(Earth-...</td>\n",
       "      <td>Victor Alvarez</td>\n",
       "      <td>45</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14-Jan</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>http://marvel.wikia.com/Ava_Ayala_(Earth-616)#</td>\n",
       "      <td>Ava Ayala</td>\n",
       "      <td>49</td>\n",
       "      <td>YES</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14-Jan</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>http://marvel.wikia.com/Kaluu_(Earth-616)#</td>\n",
       "      <td>Kaluu</td>\n",
       "      <td>35</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15-Jan</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   URL  \\\n",
       "0        http://marvel.wikia.com/Henry_Pym_(Earth-616)   \n",
       "1    http://marvel.wikia.com/Janet_van_Dyne_(Earth-...   \n",
       "2    http://marvel.wikia.com/Anthony_Stark_(Earth-616)   \n",
       "3    http://marvel.wikia.com/Robert_Bruce_Banner_(E...   \n",
       "4     http://marvel.wikia.com/Thor_Odinson_(Earth-616)   \n",
       "..                                                 ...   \n",
       "168   http://marvel.wikia.com/Eric_Brooks_(Earth-616)#   \n",
       "169  http://marvel.wikia.com/Adam_Brashear_(Earth-6...   \n",
       "170  http://marvel.wikia.com/Victor_Alvarez_(Earth-...   \n",
       "171     http://marvel.wikia.com/Ava_Ayala_(Earth-616)#   \n",
       "172         http://marvel.wikia.com/Kaluu_(Earth-616)#   \n",
       "\n",
       "                      Name/Alias  Appearances Current?  Gender  \\\n",
       "0      Henry Jonathan \"Hank\" Pym         1269      YES    MALE   \n",
       "1                 Janet van Dyne         1165      YES  FEMALE   \n",
       "2    Anthony Edward \"Tony\" Stark         3068      YES    MALE   \n",
       "3            Robert Bruce Banner         2089      YES    MALE   \n",
       "4                   Thor Odinson         2402      YES    MALE   \n",
       "..                           ...          ...      ...     ...   \n",
       "168                  Eric Brooks          198      YES    MALE   \n",
       "169                Adam Brashear           29      YES    MALE   \n",
       "170               Victor Alvarez           45      YES    MALE   \n",
       "171                    Ava Ayala           49      YES  FEMALE   \n",
       "172                        Kaluu           35      YES    MALE   \n",
       "\n",
       "    Probationary Introl Full/Reserve Avengers Intro  Year  \\\n",
       "0                   NaN                      Sep-63  1963   \n",
       "1                   NaN                      Sep-63  1963   \n",
       "2                   NaN                      Sep-63  1963   \n",
       "3                   NaN                      Sep-63  1963   \n",
       "4                   NaN                      Sep-63  1963   \n",
       "..                  ...                         ...   ...   \n",
       "168                 NaN                      13-Nov  2013   \n",
       "169                 NaN                      14-Jan  2014   \n",
       "170                 NaN                      14-Jan  2014   \n",
       "171                 NaN                      14-Jan  2014   \n",
       "172                 NaN                      15-Jan  2015   \n",
       "\n",
       "     Years since joining Honorary  ... Return1 Death2 Return2 Death3 Return3  \\\n",
       "0                     52     Full  ...      NO    NaN     NaN    NaN     NaN   \n",
       "1                     52     Full  ...     YES    NaN     NaN    NaN     NaN   \n",
       "2                     52     Full  ...     YES    NaN     NaN    NaN     NaN   \n",
       "3                     52     Full  ...     YES    NaN     NaN    NaN     NaN   \n",
       "4                     52     Full  ...     YES    YES      NO    NaN     NaN   \n",
       "..                   ...      ...  ...     ...    ...     ...    ...     ...   \n",
       "168                    2     Full  ...     NaN    NaN     NaN    NaN     NaN   \n",
       "169                    1     Full  ...     NaN    NaN     NaN    NaN     NaN   \n",
       "170                    1     Full  ...     NaN    NaN     NaN    NaN     NaN   \n",
       "171                    1     Full  ...     NaN    NaN     NaN    NaN     NaN   \n",
       "172                    0     Full  ...     NaN    NaN     NaN    NaN     NaN   \n",
       "\n",
       "    Death4 Return4 Death5 Return5  \\\n",
       "0      NaN     NaN    NaN     NaN   \n",
       "1      NaN     NaN    NaN     NaN   \n",
       "2      NaN     NaN    NaN     NaN   \n",
       "3      NaN     NaN    NaN     NaN   \n",
       "4      NaN     NaN    NaN     NaN   \n",
       "..     ...     ...    ...     ...   \n",
       "168    NaN     NaN    NaN     NaN   \n",
       "169    NaN     NaN    NaN     NaN   \n",
       "170    NaN     NaN    NaN     NaN   \n",
       "171    NaN     NaN    NaN     NaN   \n",
       "172    NaN     NaN    NaN     NaN   \n",
       "\n",
       "                                                 Notes  \n",
       "0    Merged with Ultron in Rage of Ultron Vol. 1. A...  \n",
       "1    Dies in Secret Invasion V1:I8. Actually was se...  \n",
       "2    Death: \"Later while under the influence of Imm...  \n",
       "3    Dies in Ghosts of the Future arc. However \"he ...  \n",
       "4    Dies in Fear Itself brought back because that'...  \n",
       "..                                                 ...  \n",
       "168                                                NaN  \n",
       "169                                                NaN  \n",
       "170                                                NaN  \n",
       "171                                                NaN  \n",
       "172                                                NaN  \n",
       "\n",
       "[173 rows x 21 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avengers = pd.read_csv(data_url, encoding='latin-1')\n",
    "avengers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display only the first 2 rows of the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1673460403821,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "VbFX4b2m-2xC",
    "outputId": "5358543f-edb1-4bb7-cf6b-09eece934fc5"
   },
   "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>URL</th>\n",
       "      <th>Name/Alias</th>\n",
       "      <th>Appearances</th>\n",
       "      <th>Current?</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Probationary Introl</th>\n",
       "      <th>Full/Reserve Avengers Intro</th>\n",
       "      <th>Year</th>\n",
       "      <th>Years since joining</th>\n",
       "      <th>Honorary</th>\n",
       "      <th>...</th>\n",
       "      <th>Return1</th>\n",
       "      <th>Death2</th>\n",
       "      <th>Return2</th>\n",
       "      <th>Death3</th>\n",
       "      <th>Return3</th>\n",
       "      <th>Death4</th>\n",
       "      <th>Return4</th>\n",
       "      <th>Death5</th>\n",
       "      <th>Return5</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://marvel.wikia.com/Henry_Pym_(Earth-616)</td>\n",
       "      <td>Henry Jonathan \"Hank\" Pym</td>\n",
       "      <td>1269</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Merged with Ultron in Rage of Ultron Vol. 1. A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://marvel.wikia.com/Janet_van_Dyne_(Earth-...</td>\n",
       "      <td>Janet van Dyne</td>\n",
       "      <td>1165</td>\n",
       "      <td>YES</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dies in Secret Invasion V1:I8. Actually was se...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 URL  \\\n",
       "0      http://marvel.wikia.com/Henry_Pym_(Earth-616)   \n",
       "1  http://marvel.wikia.com/Janet_van_Dyne_(Earth-...   \n",
       "\n",
       "                  Name/Alias  Appearances Current?  Gender  \\\n",
       "0  Henry Jonathan \"Hank\" Pym         1269      YES    MALE   \n",
       "1             Janet van Dyne         1165      YES  FEMALE   \n",
       "\n",
       "  Probationary Introl Full/Reserve Avengers Intro  Year  Years since joining  \\\n",
       "0                 NaN                      Sep-63  1963                   52   \n",
       "1                 NaN                      Sep-63  1963                   52   \n",
       "\n",
       "  Honorary  ... Return1 Death2 Return2 Death3 Return3 Death4 Return4 Death5  \\\n",
       "0     Full  ...      NO    NaN     NaN    NaN     NaN    NaN     NaN    NaN   \n",
       "1     Full  ...     YES    NaN     NaN    NaN     NaN    NaN     NaN    NaN   \n",
       "\n",
       "  Return5                                              Notes  \n",
       "0     NaN  Merged with Ultron in Rage of Ultron Vol. 1. A...  \n",
       "1     NaN  Dies in Secret Invasion V1:I8. Actually was se...  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use head:\n",
    "avengers.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only the last 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1673460403822,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "sjXKZA-V_GbH",
    "outputId": "5d5e3fef-fdd7-4182-b172-86d37932f750"
   },
   "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>URL</th>\n",
       "      <th>Name/Alias</th>\n",
       "      <th>Appearances</th>\n",
       "      <th>Current?</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Probationary Introl</th>\n",
       "      <th>Full/Reserve Avengers Intro</th>\n",
       "      <th>Year</th>\n",
       "      <th>Years since joining</th>\n",
       "      <th>Honorary</th>\n",
       "      <th>...</th>\n",
       "      <th>Return1</th>\n",
       "      <th>Death2</th>\n",
       "      <th>Return2</th>\n",
       "      <th>Death3</th>\n",
       "      <th>Return3</th>\n",
       "      <th>Death4</th>\n",
       "      <th>Return4</th>\n",
       "      <th>Death5</th>\n",
       "      <th>Return5</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>http://marvel.wikia.com/Victor_Alvarez_(Earth-...</td>\n",
       "      <td>Victor Alvarez</td>\n",
       "      <td>45</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14-Jan</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>http://marvel.wikia.com/Ava_Ayala_(Earth-616)#</td>\n",
       "      <td>Ava Ayala</td>\n",
       "      <td>49</td>\n",
       "      <td>YES</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14-Jan</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>http://marvel.wikia.com/Kaluu_(Earth-616)#</td>\n",
       "      <td>Kaluu</td>\n",
       "      <td>35</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15-Jan</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>Full</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   URL      Name/Alias  \\\n",
       "170  http://marvel.wikia.com/Victor_Alvarez_(Earth-...  Victor Alvarez   \n",
       "171     http://marvel.wikia.com/Ava_Ayala_(Earth-616)#       Ava Ayala   \n",
       "172         http://marvel.wikia.com/Kaluu_(Earth-616)#           Kaluu   \n",
       "\n",
       "     Appearances Current?  Gender Probationary Introl  \\\n",
       "170           45      YES    MALE                 NaN   \n",
       "171           49      YES  FEMALE                 NaN   \n",
       "172           35      YES    MALE                 NaN   \n",
       "\n",
       "    Full/Reserve Avengers Intro  Year  Years since joining Honorary  ...  \\\n",
       "170                      14-Jan  2014                    1     Full  ...   \n",
       "171                      14-Jan  2014                    1     Full  ...   \n",
       "172                      15-Jan  2015                    0     Full  ...   \n",
       "\n",
       "    Return1 Death2 Return2 Death3 Return3 Death4 Return4 Death5 Return5 Notes  \n",
       "170     NaN    NaN     NaN    NaN     NaN    NaN     NaN    NaN     NaN   NaN  \n",
       "171     NaN    NaN     NaN    NaN     NaN    NaN     NaN    NaN     NaN   NaN  \n",
       "172     NaN    NaN     NaN    NaN     NaN    NaN     NaN    NaN     NaN   NaN  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use tail:\n",
    "avengers.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are all the columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['URL', 'Name/Alias', 'Appearances', 'Current?', 'Gender',\n",
       "       'Probationary Introl', 'Full/Reserve Avengers Intro', 'Year',\n",
       "       'Years since joining', 'Honorary', 'Death1', 'Return1', 'Death2',\n",
       "       'Return2', 'Death3', 'Return3', 'Death4', 'Return4', 'Death5',\n",
       "       'Return5', 'Notes'],\n",
       "      dtype='str')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list the columns\n",
    "avengers.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New view of the table, with the URL column dropped:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 808
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1673460403822,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "RYsvE1Op_KAI",
    "outputId": "7eff4a38-3500-46a0-a268-82a7d74f3e71"
   },
   "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/Alias</th>\n",
       "      <th>Appearances</th>\n",
       "      <th>Current?</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Probationary Introl</th>\n",
       "      <th>Full/Reserve Avengers Intro</th>\n",
       "      <th>Year</th>\n",
       "      <th>Years since joining</th>\n",
       "      <th>Honorary</th>\n",
       "      <th>Death1</th>\n",
       "      <th>Return1</th>\n",
       "      <th>Death2</th>\n",
       "      <th>Return2</th>\n",
       "      <th>Death3</th>\n",
       "      <th>Return3</th>\n",
       "      <th>Death4</th>\n",
       "      <th>Return4</th>\n",
       "      <th>Death5</th>\n",
       "      <th>Return5</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Henry Jonathan \"Hank\" Pym</td>\n",
       "      <td>1269</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Merged with Ultron in Rage of Ultron Vol. 1. A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Janet van Dyne</td>\n",
       "      <td>1165</td>\n",
       "      <td>YES</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dies in Secret Invasion V1:I8. Actually was se...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anthony Edward \"Tony\" Stark</td>\n",
       "      <td>3068</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Death: \"Later while under the influence of Imm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Robert Bruce Banner</td>\n",
       "      <td>2089</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dies in Ghosts of the Future arc. However \"he ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thor Odinson</td>\n",
       "      <td>2402</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-63</td>\n",
       "      <td>1963</td>\n",
       "      <td>52</td>\n",
       "      <td>Full</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dies in Fear Itself brought back because that'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Eric Brooks</td>\n",
       "      <td>198</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13-Nov</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>Full</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Adam Brashear</td>\n",
       "      <td>29</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14-Jan</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Full</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Victor Alvarez</td>\n",
       "      <td>45</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14-Jan</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Full</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Ava Ayala</td>\n",
       "      <td>49</td>\n",
       "      <td>YES</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14-Jan</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Full</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Kaluu</td>\n",
       "      <td>35</td>\n",
       "      <td>YES</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15-Jan</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>Full</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name/Alias  Appearances Current?  Gender  \\\n",
       "0      Henry Jonathan \"Hank\" Pym         1269      YES    MALE   \n",
       "1                 Janet van Dyne         1165      YES  FEMALE   \n",
       "2    Anthony Edward \"Tony\" Stark         3068      YES    MALE   \n",
       "3            Robert Bruce Banner         2089      YES    MALE   \n",
       "4                   Thor Odinson         2402      YES    MALE   \n",
       "..                           ...          ...      ...     ...   \n",
       "168                  Eric Brooks          198      YES    MALE   \n",
       "169                Adam Brashear           29      YES    MALE   \n",
       "170               Victor Alvarez           45      YES    MALE   \n",
       "171                    Ava Ayala           49      YES  FEMALE   \n",
       "172                        Kaluu           35      YES    MALE   \n",
       "\n",
       "    Probationary Introl Full/Reserve Avengers Intro  Year  \\\n",
       "0                   NaN                      Sep-63  1963   \n",
       "1                   NaN                      Sep-63  1963   \n",
       "2                   NaN                      Sep-63  1963   \n",
       "3                   NaN                      Sep-63  1963   \n",
       "4                   NaN                      Sep-63  1963   \n",
       "..                  ...                         ...   ...   \n",
       "168                 NaN                      13-Nov  2013   \n",
       "169                 NaN                      14-Jan  2014   \n",
       "170                 NaN                      14-Jan  2014   \n",
       "171                 NaN                      14-Jan  2014   \n",
       "172                 NaN                      15-Jan  2015   \n",
       "\n",
       "     Years since joining Honorary Death1 Return1 Death2 Return2 Death3  \\\n",
       "0                     52     Full    YES      NO    NaN     NaN    NaN   \n",
       "1                     52     Full    YES     YES    NaN     NaN    NaN   \n",
       "2                     52     Full    YES     YES    NaN     NaN    NaN   \n",
       "3                     52     Full    YES     YES    NaN     NaN    NaN   \n",
       "4                     52     Full    YES     YES    YES      NO    NaN   \n",
       "..                   ...      ...    ...     ...    ...     ...    ...   \n",
       "168                    2     Full     NO     NaN    NaN     NaN    NaN   \n",
       "169                    1     Full     NO     NaN    NaN     NaN    NaN   \n",
       "170                    1     Full     NO     NaN    NaN     NaN    NaN   \n",
       "171                    1     Full     NO     NaN    NaN     NaN    NaN   \n",
       "172                    0     Full     NO     NaN    NaN     NaN    NaN   \n",
       "\n",
       "    Return3 Death4 Return4 Death5 Return5  \\\n",
       "0       NaN    NaN     NaN    NaN     NaN   \n",
       "1       NaN    NaN     NaN    NaN     NaN   \n",
       "2       NaN    NaN     NaN    NaN     NaN   \n",
       "3       NaN    NaN     NaN    NaN     NaN   \n",
       "4       NaN    NaN     NaN    NaN     NaN   \n",
       "..      ...    ...     ...    ...     ...   \n",
       "168     NaN    NaN     NaN    NaN     NaN   \n",
       "169     NaN    NaN     NaN    NaN     NaN   \n",
       "170     NaN    NaN     NaN    NaN     NaN   \n",
       "171     NaN    NaN     NaN    NaN     NaN   \n",
       "172     NaN    NaN     NaN    NaN     NaN   \n",
       "\n",
       "                                                 Notes  \n",
       "0    Merged with Ultron in Rage of Ultron Vol. 1. A...  \n",
       "1    Dies in Secret Invasion V1:I8. Actually was se...  \n",
       "2    Death: \"Later while under the influence of Imm...  \n",
       "3    Dies in Ghosts of the Future arc. However \"he ...  \n",
       "4    Dies in Fear Itself brought back because that'...  \n",
       "..                                                 ...  \n",
       "168                                                NaN  \n",
       "169                                                NaN  \n",
       "170                                                NaN  \n",
       "171                                                NaN  \n",
       "172                                                NaN  \n",
       "\n",
       "[173 rows x 20 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use drop:\n",
    "avengers.drop(columns=[\"URL\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the dimensions of the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1673460403822,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "C3Kbh_x0_qG0",
    "outputId": "d402fb4d-36eb-49d8-c664-29b0490ff8e6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(173, 21)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use shape\n",
    "avengers.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the Series representing one column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1673460403822,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "pDyXD9DW_uAz",
    "outputId": "f0349943-9716-4b7e-e726-a815e83ea02e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Henry Jonathan \"Hank\" Pym\n",
       "1                   Janet van Dyne\n",
       "2      Anthony Edward \"Tony\" Stark\n",
       "3              Robert Bruce Banner\n",
       "4                     Thor Odinson\n",
       "                  ...             \n",
       "168                    Eric Brooks\n",
       "169                  Adam Brashear\n",
       "170                 Victor Alvarez\n",
       "171                      Ava Ayala\n",
       "172                          Kaluu\n",
       "Name: Name/Alias, Length: 173, dtype: str"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract the Name/Alias columnn:\n",
    "avengers[\"Name/Alias\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract a view of the DataFrame containing only a subset of the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1673460403822,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "YS87EMGz_yCw"
   },
   "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/Alias</th>\n",
       "      <th>Appearances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Henry Jonathan \"Hank\" Pym</td>\n",
       "      <td>1269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Janet van Dyne</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anthony Edward \"Tony\" Stark</td>\n",
       "      <td>3068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Robert Bruce Banner</td>\n",
       "      <td>2089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thor Odinson</td>\n",
       "      <td>2402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Eric Brooks</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Adam Brashear</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Victor Alvarez</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Ava Ayala</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Kaluu</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name/Alias  Appearances\n",
       "0      Henry Jonathan \"Hank\" Pym         1269\n",
       "1                 Janet van Dyne         1165\n",
       "2    Anthony Edward \"Tony\" Stark         3068\n",
       "3            Robert Bruce Banner         2089\n",
       "4                   Thor Odinson         2402\n",
       "..                           ...          ...\n",
       "168                  Eric Brooks          198\n",
       "169                Adam Brashear           29\n",
       "170               Victor Alvarez           45\n",
       "171                    Ava Ayala           49\n",
       "172                        Kaluu           35\n",
       "\n",
       "[173 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a dataframe with only the Name/Alias and Appearances columns\n",
    "na = avengers[[\"Name/Alias\", \"Appearances\"]]\n",
    "na"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the size of the subset is one, it gives you a one-column table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1673460403822,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "bQqSg5qEAHq-",
    "outputId": "73321035-094f-4294-f373-5534b6819086"
   },
   "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/Alias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Henry Jonathan \"Hank\" Pym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Janet van Dyne</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anthony Edward \"Tony\" Stark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Robert Bruce Banner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thor Odinson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Eric Brooks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Adam Brashear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Victor Alvarez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Ava Ayala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Kaluu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name/Alias\n",
       "0      Henry Jonathan \"Hank\" Pym\n",
       "1                 Janet van Dyne\n",
       "2    Anthony Edward \"Tony\" Stark\n",
       "3            Robert Bruce Banner\n",
       "4                   Thor Odinson\n",
       "..                           ...\n",
       "168                  Eric Brooks\n",
       "169                Adam Brashear\n",
       "170               Victor Alvarez\n",
       "171                    Ava Ayala\n",
       "172                        Kaluu\n",
       "\n",
       "[173 rows x 1 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract a DataFrame with just Name/Alias:\n",
    "avengers[[\"Name/Alias\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that this is different from `avengers[\"Name/Alias\"]` above because this is a DataFrame, whereas the above gives you a Series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort the Avengers by number of appearances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1673460403823,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "pfSueLLXASFK"
   },
   "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/Alias</th>\n",
       "      <th>Appearances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Peter Benjamin Parker</td>\n",
       "      <td>4333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Steven Rogers</td>\n",
       "      <td>3458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>James \"Logan\" Howlett</td>\n",
       "      <td>3130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anthony Edward \"Tony\" Stark</td>\n",
       "      <td>3068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thor Odinson</td>\n",
       "      <td>2402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Dennis Sykes</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Gene Lorrene</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Doug Taggert</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Moira Brandon</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Fiona</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name/Alias  Appearances\n",
       "73         Peter Benjamin Parker         4333\n",
       "6                  Steven Rogers         3458\n",
       "92         James \"Logan\" Howlett         3130\n",
       "2    Anthony Edward \"Tony\" Stark         3068\n",
       "4                   Thor Odinson         2402\n",
       "..                           ...          ...\n",
       "117                 Dennis Sykes            6\n",
       "65                  Gene Lorrene            4\n",
       "68                  Doug Taggert            3\n",
       "39                 Moira Brandon            2\n",
       "125                        Fiona            2\n",
       "\n",
       "[173 rows x 2 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use sort_values:\n",
    "na = na.sort_values(\"Appearances\", ascending=False)\n",
    "na"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise: Show the 10th through 20th most-appearing avengers?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1673460403823,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "OUsVff9FAmIF",
    "outputId": "80a817f0-7642-49dc-ee6b-8db385c84694"
   },
   "outputs": [],
   "source": [
    "# get the 10th through 20th most-appearing avengers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note on `iloc` - it can also index across columns:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Appearances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>4333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>3130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Appearances\n",
       "73         4333\n",
       "6          3458\n",
       "92         3130\n",
       "2          3068"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the first four avengers, and the second through fourth columns:\n",
    "na.iloc[:4, 1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic plotting is built into pandas!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "executionInfo": {
     "elapsed": 705,
     "status": "ok",
     "timestamp": 1673460469665,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "EuGxNjUEBBUC",
    "outputId": "d0463d87-55b2-48dc-bb8b-60a0089449c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# line plot of appearances\n",
    "na.plot(y=\"Appearances\", use_index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a categorical column like Gender, we can count the frequency of each value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 545,
     "status": "ok",
     "timestamp": 1673376537840,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "ZkGtjYoOA0M3",
    "outputId": "cdcd0db7-13fa-45dd-fcbe-d0477b50a643"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        MALE\n",
       "1      FEMALE\n",
       "2        MALE\n",
       "3        MALE\n",
       "4        MALE\n",
       "        ...  \n",
       "168      MALE\n",
       "169      MALE\n",
       "170      MALE\n",
       "171    FEMALE\n",
       "172      MALE\n",
       "Name: Gender, Length: 173, dtype: str"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the Gender column:\n",
    "avengers[\"Gender\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "MALE      115\n",
       "FEMALE     58\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use value_counts to see frequency of each categorical label:\n",
    "avengers[\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['URL', 'Name/Alias', 'Appearances', 'Current?', 'Gender',\n",
       "       'Probationary Introl', 'Full/Reserve Avengers Intro', 'Year',\n",
       "       'Years since joining', 'Honorary', 'Death1', 'Return1', 'Death2',\n",
       "       'Return2', 'Death3', 'Return3', 'Death4', 'Return4', 'Death5',\n",
       "       'Return5', 'Notes'],\n",
       "      dtype='str')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avengers.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "executionInfo": {
     "elapsed": 380,
     "status": "ok",
     "timestamp": 1673460671582,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "bMI9B4yBBlKo",
    "outputId": "9823fba6-a847-4ef7-ae97-651b53635313"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Years since joining', ylabel='Appearances'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot Years since joining vs Appearances\n",
    "avengers.plot.scatter(x=\"Years since joining\", y=\"Appearances\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting fancier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group the avengers by gender, then average the Appearances column of each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 140,
     "status": "ok",
     "timestamp": 1673460767087,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "bt6F5HIvB-Jw",
    "outputId": "6135542b-cc4d-459a-a8d8-6d74d9517e4f"
   },
   "outputs": [],
   "source": [
    "avengers.groupby(\"Gender\")[\"Appearances\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the three most frequently-appearing female avengers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 358
    },
    "executionInfo": {
     "elapsed": 251,
     "status": "ok",
     "timestamp": 1673463596100,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "fXSyhxPbM44y",
    "outputId": "d65adf72-1054-493d-baf0-453d92af6465"
   },
   "outputs": [],
   "source": [
    "avengers[avengers[\"Gender\"] == \"FEMALE\"].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a table of only the avengers that appear more than 2000 times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 619
    },
    "executionInfo": {
     "elapsed": 490,
     "status": "ok",
     "timestamp": 1673463627717,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "sYbbFY61CpOp",
    "outputId": "47a8b648-caeb-4a95-8a10-834ae5f05f02"
   },
   "outputs": [],
   "source": [
    "avengers[avengers[\"Appearances\"] > 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1673461281683,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "YxcHA5HXDLU-",
    "outputId": "60aae015-5f1a-48e0-ab95-09167caa8ca5"
   },
   "outputs": [],
   "source": [
    "# column info\n",
    "avengers.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1673461060565,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "zdkzoCovDUMo",
    "outputId": "52584ecf-709a-4773-b5d3-4bb837a94faa"
   },
   "outputs": [],
   "source": [
    "avengers.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PB11b452u0J0"
   },
   "source": [
    "### pandas: a whirlwind tour\n",
    "\n",
    "Things to demo:\n",
    "\n",
    "- [ ] Show the first 5 rows, last 10 rows\n",
    "- [ ] Drop some columns\n",
    "- [ ] Get the dimensions of the table\n",
    "- [ ] Get a single column\n",
    "- [ ] Get multiple columns\n",
    "- [ ] Get a single column as a DataFrame\n",
    "- [ ] Sort the table\n",
    "- [ ] Get a custom slice of rows (not-quite-superstars)\n",
    "- [ ] Count number of rows with each value in a categorical column (e.g., gender)\n",
    "- [ ] Plot a column\n",
    "- [ ] Scatterplot 2 columns\n",
    "- [ ] Compute by groups\n",
    "- [ ] Get only rows that meet some condition\n",
    "- [ ] Show summary statistics of a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPbPAGEOongBtE9f7Ei5Guv",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}