notebooks/Lec15/Lecture 15 Recap and Intro to Pandas.ipynb

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    "# AMAT 502: Modern Computing for Mathematicians\n",
    "## Lecture 15 - Exponential Data, The Curse of Dimensionality and Introduction to Pandas\n",
    "### University at Albany SUNY"
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    "# Recap on Supervised Learning\n",
    "\n",
    "In **supervised learning** we are given **labeled data**.\n",
    "- When the labels are continuously varying and have *numerical interpretations*, like height or weight, we use **regression**\n",
    "- When the labels are discrete and have *categorical interpretations*, like Democrat or Republican, we use **classification**"
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    "## How Regression is Done in Python\n",
    "\n",
    "### Linear Regression\n",
    "The simplest form of regression is linear regression where we try and find a line that 'best fits' our dataset. \n",
    "\n",
    "This means that we want to compare a dependent variable $y$ from our dataset and an independent variable $x$ with a linear model:\n",
    "\n",
    "$$y = mx + b $$\n",
    "\n",
    "### Multi-Dimensional Linear Regression\n",
    "\n",
    "Alternatively, we can assume that the dependent variable $y$ depends on a vector $\\mathbf{x}=(x_1,x_2,\\ldots,x_n)$\n",
    "\n",
    "This implies a model like\n",
    "\n",
    "$$y = \\beta_0 + \\mathbf{\\beta}\\cdot\\mathbf{x} = \\beta_0 + \\beta_1x_1 + \\beta_2 x_2 + \\cdots \\beta_n x_n $$\n",
    "\n",
    "Geometrically, this is us finding the best fitting hyper-plane of dimension $n$\n",
    "\n",
    "### Polynomial Regression\n",
    "\n",
    "The way Python does polynomial regression is that it turns a one dimensional regression problem, i.e. where we have only one scalar independent variable $x$ and turns it into an $n$-dimensional regression problem, by applying the transformation\n",
    "\n",
    "$$\\Phi(x) = \\begin{bmatrix} x \\\\ x^2 \\\\ \\vdots \\\\ x^n\\end{bmatrix}$$\n",
    "\n",
    "And now multi-dimensional regression on $\\Phi(x)$ looks like\n",
    "\n",
    "$$y = \\beta_0 + \\mathbf{\\beta}\\cdot\\Phi(x) =  \\beta_0 + \\beta_1x + \\beta_2 x^2 + \\cdots \\beta_n x^n $$\n",
    "\n",
    "Notice that this turns a polynomial regression problem in one variable into a linear regression problem in $n$-variables."
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    "## Regression for the Fibonacci Numbers\n",
    "\n",
    "Recall that we used regression in order to predict the next number in an integer sequence.\n",
    "\n",
    "For **Fibonacci numbers**, i.e. those numbers that satisfy the recursion relation $$F_{n} = F_{n-1} + F_{n-2}$$\n",
    "where $F_0=1$ and $F_1=1$, we can ask whether a polynomial fits this sequence of numbers. \n",
    "\n",
    "We'll do this via the **Train-Model-Predict** paradigm."
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    "## Train-Model-Predict Paradigm for Fibonacci Numbers\n",
    "For our Fibonacci sequence case study we will be...\n",
    "- **Training** on the first 20 Fibonacci numbers\n",
    "- **Modelling** with a polynomial of $$f(x)=\\beta_0 + \\beta_1 x + \\cdots + \\beta_n x^{n}$$\n",
    "    - Using the sum of squared errors (or residuals) we'll choose different $n$ to get a better model\n",
    "    - Beware of overfitting and underfitting\n",
    "- **Predicting** the 21st Fibonacci numbers\n"
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   "source": [
    "def fib(n):\n",
    "    if n<=1:\n",
    "        return 1\n",
    "    else:\n",
    "        return fib(n-1)+fib(n-2)\n",
    "fib(5)\n",
    "\n",
    "def fibList(n):\n",
    "    return [fib(i) for i in range(n)]\n",
    "\n",
    "len(fibList(20))\n",
    "\n",
    "X= [[i] for i in range(20)]\n",
    "y=[[j] for j in fibList(20)]\n",
    "y # Shows that we like to think of data as a column vector"
   ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our sum of square errors is [0.06404079]\n",
      "Our prediction for the next Fibonacci number is [[17716.1218906]]\n",
      "Our actual next Fibonacci number is 17711\n",
      "Our error in predicting the next Fibonacci number is [[-5.1218906]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_reg = PolynomialFeatures(degree=12)\n",
    "X_poly = poly_reg.fit_transform(X)\n",
    "pol_reg = LinearRegression()\n",
    "pol_reg.fit(X_poly, y)\n",
    "\n",
    "# Visualizing the Polymonial Regression results\n",
    "def viz_polymonial():\n",
    "    plt.scatter(X, y, color='red')\n",
    "    plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color='blue')\n",
    "    plt.title('Fitting Fibonacci Number')\n",
    "    plt.xlabel('Fibonacci Index')\n",
    "    plt.ylabel('Fibonacci Number')\n",
    "    plt.show()\n",
    "    return\n",
    "viz_polymonial();\n",
    "y_pred=pol_reg.predict(poly_reg.fit_transform(X)) ## predicted values\n",
    "print(\"Our sum of square errors is\",sum((y_pred - y)**2)) ## sum of squared error\n",
    "y_pred_next = pol_reg.predict(poly_reg.fit_transform([[21]]))\n",
    "print(\"Our prediction for the next Fibonacci number is\",y_pred_next)\n",
    "print(\"Our actual next Fibonacci number is\",fib(21))\n",
    "print(\"Our error in predicting the next Fibonacci number is\",fib(21)-y_pred_next)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The Fibonacci Numbers Revisited\n",
    "\n",
    "This is an important case study because not every number sequence follows a polynomial growth.\n",
    "\n",
    "In fact it is well known that $$\\lim_{n \\to \\infty} \\frac{F_{n+1}}{F_n} = \\varphi = {\\frac {1+{\\sqrt {5}}}{2}}=1.6180339887\\dots$$ \n",
    "\n",
    "If we take the \"discrete derivative\" of $F_n$, written $dF_n$, then in the limit we have\n",
    "$$dF_n = \\frac{F_{n+1} - F_n}{(n+1)-n} \\approx \\varphi F_n - F_n \\qquad \\Rightarrow \\qquad dF_n = F_n(\\varphi -1)$$\n",
    "\n",
    "Anytime the rate of growth is proportional to the value of the function, you should expect **exponential** (not polynomial!) growth..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### A Closed Form Formula\n",
    "\n",
    "All of the above is what you might call a \"back of the envelope\" calculation. It's a heuristic and not very precise.\n",
    "\n",
    "Luckily there is a closed form-formula that proves that the Fibonacci sequence does not grow as a polynomial of fixed degree:\n",
    "\n",
    "$$F\\left(n\\right)={{\\varphi ^{n}-(1-\\varphi )^{n}} \\over {\\sqrt {5}}}={{\\varphi ^{n}-(-\\varphi )^{-n}} \\over {\\sqrt {5}}}.$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Recap on Classification\n",
    "\n",
    "In our last lecture we considered two forms of classification:\n",
    "- Linear Classifiers\n",
    "- K-Nearest Neighbors Classifiers\n",
    "\n",
    "## Linear Classifiers\n",
    "\n",
    "In a **linear classifier** the **model** is that a hyperplane separates data that has one of two classes: red and blue.\n",
    "\n",
    "When we **train,** we're trying to find a hyperplane that maximizes distance from the nearest red point and nearest blue point.\n",
    "\n",
    "Our **prediction** step then tests new samples by computing the dot product with the normal vector and comparing with the displacement $d$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Following code from https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "## Training Data\n",
    "X_train = np.array([[1.75,1.2], [2.5,0], [3,1], [4.5,2], [4.75,3], [5,2.5]])\n",
    "y_train = np.array([0, 0, 0, 1, 1, 1])\n",
    "## Setting Colors for the Training Data\n",
    "color_dict = {0: 'red',1:'blue'}\n",
    "train_Colors = [ color_dict[i] for i in y_train]\n",
    "## Plotting the Training Data\n",
    "plt.scatter(X_train[:, 0], X_train[:, 1], c=train_Colors)\n",
    "\n",
    "## Testing Data\n",
    "X_test = np.array([[3.5,1],[3.95,1.5],[4,2]])\n",
    "## Plotting Testing Data\n",
    "plt.scatter(X_test[:,0],X_test[:,1],c='gray');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf = SVC(kernel='linear')\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.53846154,  0.18461538,  0.53846154])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.decision_function(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Previous Plot infor\n",
    "plt.scatter(X_train[:, 0], X_train[:, 1], c=train_Colors)\n",
    "## Testing Data\n",
    "X_test = np.array([[3.5,1],[3.95,1.5],[4,2]])\n",
    "## Plotting Testing Data\n",
    "plt.scatter(X_test[:,0],X_test[:,1],c='gray')\n",
    "## Plotting the Decision Boundary for our Classifier\n",
    "# Following https://stackoverflow.com/questions/23794277/extract-decision-boundary-with-scikit-learn-linear-svm\n",
    "# https://scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane.html#example-svm-plot-separating-hyperplane-py\n",
    "\n",
    "\n",
    "## Little Bit of Code\n",
    "W=clf.coef_[0]\n",
    "\n",
    "I=clf.intercept_\n",
    "\n",
    "def decision_boundary(x):\n",
    "    a = -W[0]/W[1]\n",
    "    b = I[0]/W[1]\n",
    "    return a*x - b\n",
    "\n",
    "## Actually plotting\n",
    "x_dec_bdy = np.linspace(1,5,10)\n",
    "plt.plot(x_dec_bdy,decision_boundary(x_dec_bdy));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Drawbacks of Linear Classifiers\n",
    "\n",
    "- Sometimes data is not linearly separable.\n",
    "- For more than two classes of data you need multiple hyperplanes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Case Study in KNN\n",
    "\n",
    "In **K-nearest neighbors** our model is that our $k$ closest points are the best predictors of a test sample's class label.\n",
    "\n",
    "Let's reconsider the red and blue point model and run KNN on it to predict our class labels for the grey points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "classifier = KNeighborsClassifier(n_neighbors=3)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1]\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Drawbacks of KNN\n",
    "\n",
    "- If you let $K$ be too large, then then the prediction simply comes from which class dominates in the samples and forgets geometry.\n",
    "\n",
    "- For high-dimensional data, geometry is harder to use because points increasingly look like their the same distance away. This is known as the **curse of dimensionality**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The Curse of Dimensionality\n",
    "\n",
    "The $n$-ball of radius $r$ is defined as the set of points in $\\mathbb{R}^n$ such that $$B_n(r):=\\{\\mathbf{x}=(x_1,x_2,\\ldots,x_n) \\mid |\\mathbf{x}|^2_{\\ell_2} < r\\}$$\n",
    "\n",
    "The **unit $n$-ball** is the ball of radius 1.\n",
    "\n",
    "### In dimension... the volume is...\n",
    "For the unit ball we have the following pattern:\n",
    "- For $n=1$, the volume (length) is 2\n",
    "- For $n=2$, the volume (area) is $\\pi$, because $\\pi r^2$ is the formula for area.\n",
    "- For $n=3$, the volume is $\\frac{4 \\pi}{3}$ because $4\\pi r^3/3$ is the formula for the volume of a ball.\n",
    "\n",
    "### The General Formula\n",
    "In general, the volume of the $n$-ball of radius $r$ is\n",
    "$$V_{n}(r)={\\frac {\\pi ^{\\frac {n}{2}}}{\\Gamma \\left({\\frac {n}{2}}+1\\right)}}r^{n}$$\n",
    "Here $\\Gamma$ is the **gamma function,** which is the continuous extension of the factorial to the non-negative reals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def volume_of_hypersphere(rad,dim):\n",
    "    return (math.pi**(dim/2)/math.gamma(dim/2 + 1))*rad**dim\n",
    "\n",
    "volume_of_hypersphere(1,3)\n",
    "dims = np.arange(1,20)\n",
    "vols = np.array([ volume_of_hypersphere(1,i) for i in dims])\n",
    "vols2 = np.array([ volume_of_hypersphere(1.1,i) for i in dims])\n",
    "plt.plot(dims,vols);\n",
    "plt.plot(dims,vols2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Volume in the Shell\n",
    "\n",
    "The curse of dimensionality comes from the fact that the percentage of the volume that lies between .9 and 1 quickly approaches 100%\n",
    "\n",
    "In...\n",
    "- dimension 5, 40% of the volume is between distance .9 and 1 away from the center.\n",
    "- dimension 10, 65% of the volume is between distance .9 and 1\n",
    "- dimension 15, 79% of the volume is between .9 and 1 of the radius\n",
    "- dimension 20, 87.8% of the volume is there\n",
    "- dimension 25, 92.8% of the volume is there\n",
    "\n",
    "### The Upshot of the Curse of Dimensionality\n",
    "Since data looks more and more equidistant from one another, KNN is probably more inaccurate for data in dimensions 20 and higher!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9282102012308147"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "more_dims = np.arange(1,41)\n",
    "full_volume = np.array([ volume_of_hypersphere(1,i) for i in more_dims])\n",
    "inner_volume = np.array([ volume_of_hypersphere(.90,i) for i in more_dims])\n",
    "volume_in_shell = full_volume-inner_volume\n",
    "percent_volume = volume_in_shell/full_volume\n",
    "plt.plot(more_dims,percent_volume)\n",
    "percent_volume[24]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Introduction to Pandas\n",
    "\n",
    "* Python library built on NumPy \n",
    "\n",
    "* Pandas objects can be thought of as enhanced NumPy structured arrays in which the row and columns are identified with labels instead of just indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Series\n",
    "\n",
    "A Pandas **Series** object is a one-dimensional array of indexed data (with any countable, finite indexing set).\n",
    "\n",
    "This means that **Series** wraps both a sequence of values and a sequence of indices.\n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "red       0.00\n",
       "yellow    0.25\n",
       "green     0.50\n",
       "blue      0.75\n",
       "purple    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#Defining a Series object\n",
    "some_data = pd.Series([0.0,0.25,0.5,0.75,1.0], index = ['red','yellow','green','blue','purple'])\n",
    "some_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.   0.25 0.5  0.75 1.  ]\n",
      "Index(['red', 'yellow', 'green', 'blue', 'purple'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#retrieving Series values\n",
    "print(some_data.values)\n",
    "#retrieving Series index\n",
    "print(some_data.index)\n",
    "#retrieving Series element by index\n",
    "some_data['yellow']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Series as Dictionaries\n",
    "\n",
    "Series may seem like the same thing as a Python dictionary, however for certain operations (array - style operations in particular) **Pandas series objects are much more efficient.** \n",
    "\n",
    "Pandas *can* take in a dictionary and use its values and indices to create a series object "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#Dictionary\n",
    "population_dict = {'California':38332521,'Texas':26448193, 'New York': 19651127, 'Florida':19552860, 'Illinois': 12882135}\n",
    "\n",
    "#Series\n",
    "population = pd.Series(population_dict)\n",
    "\n",
    "#population_dict['California': 'New York']\n",
    "population['California':'New York']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### DataFrames\n",
    "\n",
    "Like the series object, a Pandas **DataFrame** object can be thought of as either a generalization of a NumPy array OR a Python dictionary. \n",
    "\n",
    "To better understand DataFrames, let's use a Series object to build up a dataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Texas         695662\n",
       "New York      141297\n",
       "Florida       170312\n",
       "Illinois      129995\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_dict = {'California':423967,'Texas':695662, 'New York': 141297, 'Florida':170312, 'Illinois': 129995}\n",
    "area = pd.Series(area_dict)\n",
    "area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>population</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>170312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "      <td>129995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area\n",
       "California    38332521  423967\n",
       "Texas         26448193  695662\n",
       "New York      19651127  141297\n",
       "Florida       19552860  170312\n",
       "Illinois      12882135  129995"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = pd.DataFrame({'population': population,'area': area})\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['California', 'Texas', 'New York', 'Florida', 'Illinois'], dtype='object')\n",
      "Index(['population', 'area'], dtype='object')\n",
      "[[38332521   423967]\n",
      " [26448193   695662]\n",
      " [19651127   141297]\n",
      " [19552860   170312]\n",
      " [12882135   129995]]\n"
     ]
    }
   ],
   "source": [
    "print(states.index)\n",
    "print(states.columns)\n",
    "print(states.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b   c\n",
      "1  4  7  10\n",
      "2  5  8  11\n",
      "3  6  9  12\n",
      "   a  b   c\n",
      "1  4  7  10\n",
      "2  5  8  11\n",
      "3  6  9  12\n"
     ]
    }
   ],
   "source": [
    "#Defined using dictionary\n",
    "df1 = pd.DataFrame({\"a\":[4,5,6], \"b\": [7,8,9], \"c\":[10,11,12]}, index = [1,2,3])\n",
    "print(df1)\n",
    "\n",
    "#Defined using lists\n",
    "df2 = pd.DataFrame([[4,7,10],[5,8,11],[6,9,12]],index = [1,2,3], columns = ['a','b','c'])\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Indexing\n",
    "\n",
    "Since we have two sets of labels, both for rows and columns, we need to be careful about how we parse through the DataFrame objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>170312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "      <td>129995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area\n",
       "California    38332521  423967\n",
       "Texas         26448193  695662\n",
       "New York      19651127  141297\n",
       "Florida       19552860  170312\n",
       "Illinois      12882135  129995"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Returning Columns\n",
    "\n",
    "In order to select a column in a DataFrame, we can access the columns by calling them by their columns name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population\n",
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127\n",
       "Florida       19552860\n",
       "Illinois      12882135"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states[['population']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
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       "</table>\n",
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      "text/plain": [
       "   a  b\n",
       "1  4  7\n",
       "2  5  8\n",
       "3  6  9"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting two columns\n",
    "df2[['a','b']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Returning Rows\n",
    "\n",
    "Pandas provide a unique method to retrieve rows from a Data frame. *DataFrame.loc[]* method is used to retrieve rows from Pandas DataFrame. Rows can also be selected by passing integer location to an *iloc[]* function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "            population\n",
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#parameters are using the implicit index\n",
    "states.iloc[:3,:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>170312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "      <td>129995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area\n",
       "California    38332521  423967\n",
       "Texas         26448193  695662\n",
       "New York      19651127  141297\n",
       "Florida       19552860  170312\n",
       "Illinois      12882135  129995"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#return rows across all columns, just like you would with a list\n",
    "states[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area\n",
       "California    38332521  423967\n",
       "Texas         26448193  695662\n",
       "New York      19651127  141297"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#parameters use explicit indexing\n",
    "states.loc[:'New York',:\"area\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "      <td>90.413926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>170312</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "      <td>129995</td>\n",
       "      <td>99.097158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area     density\n",
       "California    38332521  423967   90.413926\n",
       "Texas         26448193  695662   38.018740\n",
       "New York      19651127  141297  139.076746\n",
       "Florida       19552860  170312  114.806121\n",
       "Illinois      12882135  129995   99.097158"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#append a new column\n",
    "states['density'] = states['population']/states['area']\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38.01874042279153\n",
      "38.01874042279153\n"
     ]
    }
   ],
   "source": [
    "#return a specific cell\n",
    "print(states.iloc[1,2])\n",
    "\n",
    "print(states.loc['Texas','density'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Using Comparison Operations\n",
    "\n",
    "We can also return values that satisfy a given comparison operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>170312</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "      <td>129995</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area     density\n",
       "California    38332521  423967         NaN\n",
       "Texas         26448193  695662         NaN\n",
       "New York      19651127  141297  139.076746\n",
       "Florida       19552860  170312  114.806121\n",
       "Illinois      12882135  129995         NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Comparing all values\n",
    "states[states > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "      <td>90.413926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area    density\n",
       "California    38332521  423967  90.413926\n",
       "Texas         26448193  695662  38.018740"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Comparing specific values\n",
    "states[states.area > 180000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Statistics\n",
    "\n",
    "* **Mean:** The average values of a data set\n",
    "\n",
    "* **Variance:** A measure of how spread out the data is on average\n",
    "\n",
    "* **Standard Deviation:** The square root of the variance (so it is still a measure of deviation from the mean)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a     5.0\n",
      "b     8.0\n",
      "c    11.0\n",
      "dtype: float64\n",
      "a    1.0\n",
      "b    1.0\n",
      "c    1.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b   c\n",
       "1  4  7  10\n",
       "2  5  8  11\n",
       "3  6  9  12"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean\n",
    "print(df1.mean())\n",
    "\n",
    "#standard deviation\n",
    "print(df1.std())\n",
    "\n",
    "df1\n",
    "#states.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Statistics\n",
    "\n",
    "* **Covariance:** Measures the total variation of two random variables from their expected values. Using covariance, we can only gauge the direction of the relationship (whether the variables tend to move in tandem or show an inverse relationship, i.e., positive or negative covariance).\n",
    "\n",
    "* **Correlation Coefficient**, $r$: Correlation shows the linear relationship between any two array of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1485199924794.85\n",
      "0.6262485633149357\n",
      "              population          area       density\n",
      "population  9.293703e+13  1.485200e+12 -1.316323e+08\n",
      "area        1.485200e+12  6.051841e+10 -8.363160e+06\n",
      "density    -1.316323e+08 -8.363160e+06  1.402875e+03\n",
      "            population      area   density\n",
      "population    1.000000  0.626249 -0.364551\n",
      "area          0.626249  1.000000 -0.907647\n",
      "density      -0.364551 -0.907647  1.000000\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "      <td>90.413926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>170312</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
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       "      <td>99.097158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area     density\n",
       "California    38332521  423967   90.413926\n",
       "Texas         26448193  695662   38.018740\n",
       "New York      19651127  141297  139.076746\n",
       "Florida       19552860  170312  114.806121\n",
       "Illinois      12882135  129995   99.097158"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#covariance\n",
    "print(states['population'].cov(states['area']))\n",
    "\n",
    "#correlation coefficient\n",
    "print(states['population'].corr(states['area']))\n",
    "\n",
    "print(states.cov())\n",
    "print(states.corr())\n",
    "states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Basic Plotting\n",
    "\n",
    "The three easiest plots to use in the Pandas library are histograms, scatter plots, and line graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Following https://stackabuse.com/k-nearest-neighbors-algorithm-in-python-and-scikit-learn/\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
    "\n",
    "# Assign colum names to the dataset\n",
    "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']\n",
    "\n",
    "# Read dataset to pandas dataframe\n",
    "data = pd.read_csv(url, names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal-length</th>\n",
       "      <th>sepal-width</th>\n",
       "      <th>petal-length</th>\n",
       "      <th>petal-width</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal-length  sepal-width  petal-length  petal-width        Class\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Iris Histogram'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind = 'hist', title = 'Iris Histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind = 'hist', bins = 70, figsize = (12,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['sepal-width','sepal-length']].plot(kind = 'hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind = 'line',figsize = (12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.scatter(x = 'petal-width',y = 'petal-length');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-058fb2a34f72>:10: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  model.fit(x[:, np.newaxis],y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression(fit_intercept = True)\n",
    "\n",
    "x = data['petal-width']\n",
    "y = data['petal-length']\n",
    "\n",
    "model.fit(x[:, np.newaxis],y)\n",
    "#model.fit(x,y) <-- WRONG CODE\n",
    "\n",
    "xfit = np.linspace(0,3,100)\n",
    "yfit = model.predict(xfit[:,np.newaxis])\n",
    "\n",
    "plt.scatter(x,y);\n",
    "plt.plot(xfit,yfit);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model slope:  : 2.225885306553911\n",
      "Model intercept : 1.090572145877378\n",
      "Regression Line . : 2.225885306553911 * x +  1.090572145877378\n",
      "R^2 value:  0.9269012279220037\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-41-1c98bf1a7fee>:4: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  print('R^2 value: ', model.score(x[:, np.newaxis],y)) # explain a little\n"
     ]
    }
   ],
   "source": [
    "print('Model slope:  :', model.coef_[0])\n",
    "print('Model intercept :', model.intercept_)\n",
    "print('Regression Line . :',model.coef_[0],'* x + ',model.intercept_ )\n",
    "print('R^2 value: ', model.score(x[:, np.newaxis],y)) # explain a little"
   ]
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   "source": [
    "## Cheat Sheat References:\n",
    "\n",
    "* https://assets.datacamp.com/blog_assets/PandasPythonForDataScience.pdf\n",
    "\n",
    "* https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf"
   ]
  }
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