diff --git a/f23/Cole_Lecture_Notes/35_Plotting2/Lec35_Plotting2_Solution_Nelson.ipynb b/f23/Cole_Lecture_Notes/35_Plotting2/Lec35_Plotting2_Solution_Nelson.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import DataFrame, Series\n",
+ "\n",
+ "import sqlite3\n",
+ "import os\n",
+ "\n",
+ "import matplotlib\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "import requests\n",
+ "matplotlib.rcParams[\"font.size\"] = 12"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris\n",
+ "- This set of data is used in beginning Machine Learning Courses\n",
+ "- You can train a ML algorithm to use the values to predict the class of iris\n",
+ "- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Warmup 1: Requests and file writing\n",
+ "\n",
+ "# use requests to get this file \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
+ "response = requests.get(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\")\n",
+ "\n",
+ "# check that the request was successful\n",
+ "response.raise_for_status()\n",
+ "\n",
+ "# open a file called \"iris.csv\" for writing the data locally to avoid spamming their server\n",
+ "file_obj = open(\"iris.csv\", \"w\")\n",
+ "\n",
+ "# write the text of response to the file object\n",
+ "file_obj.write(response.text)\n",
+ "\n",
+ "# close the file object\n",
+ "file_obj.close()\n",
+ "\n",
+ "# Look at the file you downloaded. What's wrong with it?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "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>5.1</th>\n",
+ " <th>3.5</th>\n",
+ " <th>1.4</th>\n",
+ " <th>0.2</th>\n",
+ " <th>Iris-setosa</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</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>1</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>2</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>3</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",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5.4</td>\n",
+ " <td>3.9</td>\n",
+ " <td>1.7</td>\n",
+ " <td>0.4</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " 5.1 3.5 1.4 0.2 Iris-setosa\n",
+ "0 4.9 3.0 1.4 0.2 Iris-setosa\n",
+ "1 4.7 3.2 1.3 0.2 Iris-setosa\n",
+ "2 4.6 3.1 1.5 0.2 Iris-setosa\n",
+ "3 5.0 3.6 1.4 0.2 Iris-setosa\n",
+ "4 5.4 3.9 1.7 0.4 Iris-setosa"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Warmup 2: Making a DataFrame\n",
+ "\n",
+ "# read the \"iris.csv\" file into a Pandas dataframe\n",
+ "iris_df = pd.read_csv(\"iris.csv\")\n",
+ "\n",
+ "# display the head of the data frame\n",
+ "iris_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "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>sep-length</th>\n",
+ " <th>sep-width</th>\n",
+ " <th>pet-length</th>\n",
+ " <th>pet-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": [
+ " sep-length sep-width pet-length pet-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": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Warmup 3: Our CSV file has no header....let's add column names.\n",
+ "# Refer to the documentation: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html\n",
+ "\n",
+ "# Attribute Information:\n",
+ "# 1. sepal length in cm\n",
+ "# 2. sepal width in cm\n",
+ "# 3. petal length in cm\n",
+ "# 4. petal width in cm\n",
+ "# 5. class: Iris Setosa, Iris Versicolour, Iris Virginica\n",
+ "\n",
+ "# These should be our headers [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"]\n",
+ "\n",
+ "\n",
+ "iris_df = pd.read_csv(\"iris.csv\",\n",
+ " names=[\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"])\n",
+ "iris_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "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>type</th>\n",
+ " <th>name</th>\n",
+ " <th>tbl_name</th>\n",
+ " <th>rootpage</th>\n",
+ " <th>sql</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>table</td>\n",
+ " <td>iris</td>\n",
+ " <td>iris</td>\n",
+ " <td>2</td>\n",
+ " <td>CREATE TABLE \"iris\" (\\n\"sep-length\" REAL,\\n \"...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " type name tbl_name rootpage \\\n",
+ "0 table iris iris 2 \n",
+ "\n",
+ " sql \n",
+ "0 CREATE TABLE \"iris\" (\\n\"sep-length\" REAL,\\n \"... "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Warmup 4: Connect to our database version of this data!\n",
+ "iris_conn = sqlite3.connect(\"iris-flowers.db\")\n",
+ "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", iris_conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "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>sep-length</th>\n",
+ " <th>sep-width</th>\n",
+ " <th>pet-length</th>\n",
+ " <th>pet-width</th>\n",
+ " <th>class</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>5.8</td>\n",
+ " <td>4.0</td>\n",
+ " <td>1.2</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>5.7</td>\n",
+ " <td>3.8</td>\n",
+ " <td>1.7</td>\n",
+ " <td>0.3</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>5.7</td>\n",
+ " <td>4.4</td>\n",
+ " <td>1.5</td>\n",
+ " <td>0.4</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>5.5</td>\n",
+ " <td>3.5</td>\n",
+ " <td>1.3</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5.5</td>\n",
+ " <td>4.2</td>\n",
+ " <td>1.4</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>5.4</td>\n",
+ " <td>3.4</td>\n",
+ " <td>1.7</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>5.4</td>\n",
+ " <td>3.4</td>\n",
+ " <td>1.5</td>\n",
+ " <td>0.4</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>5.4</td>\n",
+ " <td>3.7</td>\n",
+ " <td>1.5</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>5.4</td>\n",
+ " <td>3.9</td>\n",
+ " <td>1.7</td>\n",
+ " <td>0.4</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>5.4</td>\n",
+ " <td>3.9</td>\n",
+ " <td>1.3</td>\n",
+ " <td>0.4</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sep-length sep-width pet-length pet-width class\n",
+ "0 5.8 4.0 1.2 0.2 Iris-setosa\n",
+ "1 5.7 3.8 1.7 0.3 Iris-setosa\n",
+ "2 5.7 4.4 1.5 0.4 Iris-setosa\n",
+ "3 5.5 3.5 1.3 0.2 Iris-setosa\n",
+ "4 5.5 4.2 1.4 0.2 Iris-setosa\n",
+ "5 5.4 3.4 1.7 0.2 Iris-setosa\n",
+ "6 5.4 3.4 1.5 0.4 Iris-setosa\n",
+ "7 5.4 3.7 1.5 0.2 Iris-setosa\n",
+ "8 5.4 3.9 1.7 0.4 Iris-setosa\n",
+ "9 5.4 3.9 1.3 0.4 Iris-setosa"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Warmup 5: Using SQL, get the 10 'Iris-setosa' flowers with the longest sepal length.\n",
+ "# Break any ties by ordering by the shortest sepal width.\n",
+ "\n",
+ "pd.read_sql(\"\"\"\n",
+ " SELECT *\n",
+ " FROM iris\n",
+ " WHERE class = 'Iris-setosa'\n",
+ " ORDER BY `sep-length` DESC, `sep-width` ASC\n",
+ " LIMIT 10\n",
+ "\"\"\", iris_conn)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Lecture 36: Scatter Plots\n",
+ "**Learning Objectives**\n",
+ "- Set the marker, color, and size of scatter plot data\n",
+ "- Calculate correlation between DataFrame columns\n",
+ "- Use subplots to group scatterplot data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Set the marker, color, and size of scatter plot data\n",
+ "\n",
+ "To start, let's look at some made-up data about Trees.\n",
+ "The city of Madison maintains a database of all the trees they care for."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "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>age</th>\n",
+ " <th>height</th>\n",
+ " <th>diameter</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>1.5</td>\n",
+ " <td>0.8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1</td>\n",
+ " <td>1.9</td>\n",
+ " <td>1.2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1</td>\n",
+ " <td>1.8</td>\n",
+ " <td>1.4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2</td>\n",
+ " <td>1.8</td>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2</td>\n",
+ " <td>2.5</td>\n",
+ " <td>1.5</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " age height diameter\n",
+ "0 1 1.5 0.8\n",
+ "1 1 1.9 1.2\n",
+ "2 1 1.8 1.4\n",
+ "3 2 1.8 0.9\n",
+ "4 2 2.5 1.5"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trees = [\n",
+ " {\"age\": 1, \"height\": 1.5, \"diameter\": 0.8},\n",
+ " {\"age\": 1, \"height\": 1.9, \"diameter\": 1.2},\n",
+ " {\"age\": 1, \"height\": 1.8, \"diameter\": 1.4},\n",
+ " {\"age\": 2, \"height\": 1.8, \"diameter\": 0.9},\n",
+ " {\"age\": 2, \"height\": 2.5, \"diameter\": 1.5},\n",
+ " {\"age\": 2, \"height\": 3, \"diameter\": 1.8},\n",
+ " {\"age\": 2, \"height\": 2.9, \"diameter\": 1.7},\n",
+ " {\"age\": 3, \"height\": 3.2, \"diameter\": 2.1},\n",
+ " {\"age\": 3, \"height\": 3, \"diameter\": 2},\n",
+ " {\"age\": 3, \"height\": 2.4, \"diameter\": 2.2},\n",
+ " {\"age\": 2, \"height\": 3.1, \"diameter\": 2.9},\n",
+ " {\"age\": 4, \"height\": 2.5, \"diameter\": 3.1},\n",
+ " {\"age\": 4, \"height\": 3.9, \"diameter\": 3.1},\n",
+ " {\"age\": 4, \"height\": 4.9, \"diameter\": 2.8},\n",
+ " {\"age\": 4, \"height\": 5.2, \"diameter\": 3.5},\n",
+ " {\"age\": 4, \"height\": 4.8, \"diameter\": 4},\n",
+ "]\n",
+ "trees_df = DataFrame(trees)\n",
+ "trees_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Scatter Plots\n",
+ "We can make a scatter plot of a DataFrame using the following function...\n",
+ "\n",
+ "`df_name.plot.scatter(x=\"x_col_name\", y=\"y_col_name\", color=\"peachpuff\")`\n",
+ "\n",
+ "Plot the trees data comparing a tree's age to its height...\n",
+ " - What is `df_name`?\n",
+ " - What is `x_col_name`?\n",
+ " - What is `y_col_name`?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 8,
+ "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": [
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", color = \"g\") # TODO: change y to diameter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now plot with a little more beautification...\n",
+ " - Use a new [color](https://matplotlib.org/3.5.0/_images/sphx_glr_named_colors_003.png)\n",
+ " - Use a type of [marker](https://matplotlib.org/stable/api/markers_api.html)\n",
+ " - Change the size (any int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 9,
+ "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": [
+ "# Plot with some more beautification options.\n",
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", color=\"r\", marker = \"D\", s=50) # D for diamond"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Tree Age vs Height')"
+ ]
+ },
+ "execution_count": 10,
+ "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": [
+ "# Add a title to your plot.\n",
+ "ax = trees_df.plot.scatter(x=\"age\", y=\"height\", color=\"r\", marker = \"D\", s=50) # D for diamond\n",
+ "ax.set_title(\"Tree Age vs Height\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Correlation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "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>age</th>\n",
+ " <th>height</th>\n",
+ " <th>diameter</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>age</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.797468</td>\n",
+ " <td>0.854578</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>height</th>\n",
+ " <td>0.797468</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.839345</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>diameter</th>\n",
+ " <td>0.854578</td>\n",
+ " <td>0.839345</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " age height diameter\n",
+ "age 1.000000 0.797468 0.854578\n",
+ "height 0.797468 1.000000 0.839345\n",
+ "diameter 0.854578 0.839345 1.000000"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# What is the correlation between our DataFrame columns?\n",
+ "corr_df = trees_df.corr()\n",
+ "corr_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.7974683544303798"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# What is the correlation between age and height (don't use .iloc)\n",
+ "corr_df['age']['height']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Variating Stylistic Parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 13,
+ "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": [
+ "# Option 1:\n",
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", marker=\"H\", s=\"diameter\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 14,
+ "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": [
+ "# Option 2:\n",
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", marker = \"H\", s=trees_df[\"diameter\"] * 50) # this way allows you to make it bigger"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use subplots to group scatterplot data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Re-visit the Iris Data\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sep-length</th>\n",
+ " <th>sep-width</th>\n",
+ " <th>pet-length</th>\n",
+ " <th>pet-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",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>145</th>\n",
+ " <td>6.7</td>\n",
+ " <td>3.0</td>\n",
+ " <td>5.2</td>\n",
+ " <td>2.3</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>146</th>\n",
+ " <td>6.3</td>\n",
+ " <td>2.5</td>\n",
+ " <td>5.0</td>\n",
+ " <td>1.9</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>147</th>\n",
+ " <td>6.5</td>\n",
+ " <td>3.0</td>\n",
+ " <td>5.2</td>\n",
+ " <td>2.0</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>148</th>\n",
+ " <td>6.2</td>\n",
+ " <td>3.4</td>\n",
+ " <td>5.4</td>\n",
+ " <td>2.3</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>149</th>\n",
+ " <td>5.9</td>\n",
+ " <td>3.0</td>\n",
+ " <td>5.1</td>\n",
+ " <td>1.8</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>150 rows × 5 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sep-length sep-width pet-length pet-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\n",
+ ".. ... ... ... ... ...\n",
+ "145 6.7 3.0 5.2 2.3 Iris-virginica\n",
+ "146 6.3 2.5 5.0 1.9 Iris-virginica\n",
+ "147 6.5 3.0 5.2 2.0 Iris-virginica\n",
+ "148 6.2 3.4 5.4 2.3 Iris-virginica\n",
+ "149 5.9 3.0 5.1 1.8 Iris-virginica\n",
+ "\n",
+ "[150 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "iris_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How do we create a *scatter plot* for various *class types*?\n",
+ "First, gather all the class types."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Iris-versicolor', 'Iris-setosa', 'Iris-virginica']"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# In Pandas\n",
+ "varietes = list(set(iris_df[\"class\"]))\n",
+ "varietes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# In SQL\n",
+ "varietes = list(pd.read_sql(\"\"\"\n",
+ " SELECT DISTINCT class\n",
+ " FROM iris\n",
+ "\"\"\", iris_conn)[\"class\"])\n",
+ "varietes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In reality, you can choose to write Pandas or SQL queries (or a mix of both!). For the rest of this lecture, we'll use Pandas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# If you want to continue using SQL instead, don't close the connection!\n",
+ "iris_conn.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='pet-width', ylabel='pet-length'>"
+ ]
+ },
+ "execution_count": 19,
+ "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": [
+ "# Change this scatter plot so that the data is only for class ='Iris-setosa'\n",
+ "iris_df[iris_df[\"class\"] == 'Iris-setosa'].plot.scatter(x = \"pet-width\", y = \"pet-length\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Write a for loop that iterates through each variety in classes\n",
+ "# and makes a plot for only that class\n",
+ "\n",
+ "for i in range(len(varietes)):\n",
+ " variety = varietes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " variety_df = iris_df[iris_df[\"class\"] == variety] \n",
+ " \n",
+ " #make a scatter plot for this variety\n",
+ " variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", label=variety)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# copy/paste the code above, but this time make each plot a different color\n",
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "for i in range(len(varietes)):\n",
+ " variety = varietes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " variety_df = iris_df[iris_df[\"class\"] == variety] \n",
+ " \n",
+ " #make a scatter plot for this variety\n",
+ " variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", label=variety, color=colors[i])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# copy/paste the code above, but this time make each plot a different color AND marker\n",
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n",
+ "for i in range(len(varietes)):\n",
+ " variety = varietes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " variety_df = iris_df[iris_df[\"class\"] == variety] \n",
+ " \n",
+ " #make a scatter plot for this variety\n",
+ " variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", label=variety, color=colors[i], marker=markers[i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Did you notice that it made 3 plots?!?! What's decieving about this?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n",
+ "min_x = iris_df[\"pet-width\"].min()\n",
+ "max_x = iris_df[\"pet-width\"].max()\n",
+ "min_y = iris_df[\"pet-length\"].min()\n",
+ "max_y = iris_df[\"pet-length\"].max()\n",
+ "for i in range(len(varietes)):\n",
+ " variety = varietes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " variety_df = iris_df[iris_df[\"class\"] == variety] \n",
+ " \n",
+ " #make a scatter plot for this variety\n",
+ " variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", label=variety, color=colors[i], marker=markers[i], xlim=(min_x, max_x), ylim=(min_y, max_y))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Have to be VERY careful to not crop out data.\n",
+ "# We'll talk about this next lecture."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Better yet, we could combine these."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### We can make Subplots in plots, called an AxesSubplot, keyword ax\n",
+ "1. if AxesSuplot ax passed, then plot in that subplot\n",
+ "2. if ax is None, create a new AxesSubplot\n",
+ "3. return AxesSubplot that was used"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# complete this code to make 3 plots in one\n",
+ "\n",
+ "plot_area = None # don't change this...look at this variable in line 12\n",
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n",
+ "for i in range(len(varietes)):\n",
+ " variety = varietes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " variety_df = iris_df[iris_df[\"class\"] == variety] \n",
+ " \n",
+ " #make a scatter plot for this variety\n",
+ " plot_area = variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", label=variety, color=colors[i], marker=markers[i], ax=plot_area)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Time-Permitting\n",
+ "Plot this data in an interesting/meaningful way & identify any correlations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>name</th>\n",
+ " <th>grade</th>\n",
+ " <th>gpa</th>\n",
+ " <th>attendance</th>\n",
+ " <th>height</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Cole</td>\n",
+ " <td>C</td>\n",
+ " <td>2.0</td>\n",
+ " <td>4</td>\n",
+ " <td>68</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Cynthia</td>\n",
+ " <td>AB</td>\n",
+ " <td>3.5</td>\n",
+ " <td>11</td>\n",
+ " <td>66</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Alice</td>\n",
+ " <td>B</td>\n",
+ " <td>3.0</td>\n",
+ " <td>10</td>\n",
+ " <td>60</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>Seth</td>\n",
+ " <td>BC</td>\n",
+ " <td>2.5</td>\n",
+ " <td>6</td>\n",
+ " <td>72</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " name grade gpa attendance height\n",
+ "0 Cole C 2.0 4 68\n",
+ "1 Cynthia AB 3.5 11 66\n",
+ "2 Alice B 3.0 10 60\n",
+ "3 Seth BC 2.5 6 72"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "students = pd.DataFrame({\n",
+ " \"name\": [\n",
+ " \"Cole\",\n",
+ " \"Cynthia\",\n",
+ " \"Alice\",\n",
+ " \"Seth\"\n",
+ " ],\n",
+ " \"grade\": [\n",
+ " \"C\",\n",
+ " \"AB\",\n",
+ " \"B\",\n",
+ " \"BC\"\n",
+ " ],\n",
+ " \"gpa\": [\n",
+ " 2.0,\n",
+ " 3.5,\n",
+ " 3.0,\n",
+ " 2.5\n",
+ " ],\n",
+ " \"attendance\": [\n",
+ " 4,\n",
+ " 11,\n",
+ " 10,\n",
+ " 6\n",
+ " ],\n",
+ " \"height\": [\n",
+ " 68,\n",
+ " 66,\n",
+ " 60,\n",
+ " 72\n",
+ " ]\n",
+ "})\n",
+ "students"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 0.3333333333333333\n",
+ "1 0.25\n",
+ "2 0.0\n",
+ "3 0.5\n",
+ "Name: height, dtype: string"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Min, Max, and Overall Difference in Student Height\n",
+ "min_height = students[\"height\"].min()\n",
+ "max_height = students[\"height\"].max()\n",
+ "diff_height = max_height - min_height\n",
+ "\n",
+ "# Normalize students heights on a scale of [0, 1] (black to white)\n",
+ "height_colors = (students[\"height\"] - min_height) / diff_height\n",
+ "\n",
+ "# Normalize students heights on a scale of [0, 0.5] (black to gray)\n",
+ "height_colors = height_colors / 2 \n",
+ "\n",
+ "# Color must be a string (e.g. c='0.34')\n",
+ "height_colors = height_colors.astype(\"string\")\n",
+ "\n",
+ "height_colors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='attendance', ylabel='gpa'>"
+ ]
+ },
+ "execution_count": 30,
+ "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": [
+ "students.plot.scatter(x=\"attendance\", y=\"gpa\", c=height_colors)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\ctnelson1997\\AppData\\Local\\Temp\\ipykernel_12312\\882796491.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+ " students.corr()\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>gpa</th>\n",
+ " <th>attendance</th>\n",
+ " <th>height</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>gpa</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.976831</td>\n",
+ " <td>-0.464758</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>attendance</th>\n",
+ " <td>0.976831</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>-0.635586</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>height</th>\n",
+ " <td>-0.464758</td>\n",
+ " <td>-0.635586</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " gpa attendance height\n",
+ "gpa 1.000000 0.976831 -0.464758\n",
+ "attendance 0.976831 1.000000 -0.635586\n",
+ "height -0.464758 -0.635586 1.000000"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "students.corr()"
+ ]
+ },
+ {
+ "attachments": {
+ "image.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "https://www.researchgate.net/publication/247907373_Stupid_Data_Miner_Tricks_Overfitting_the_SP_500"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import DataFrame, Series\n",
+ "\n",
+ "import sqlite3\n",
+ "import os\n",
+ "\n",
+ "import matplotlib\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "import requests\n",
+ "matplotlib.rcParams[\"font.size\"] = 12"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris\n",
+ "- This set of data is used in beginning Machine Learning Courses\n",
+ "- You can train a ML algorithm to use the values to predict the class of iris\n",
+ "- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Warmup 1: Requests and file writing\n",
+ "\n",
+ "# use requests to get this file \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
+ "response = requests.get(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\")\n",
+ "\n",
+ "# check that the request was successful\n",
+ "\n",
+ "\n",
+ "# open a file called \"iris.csv\" for writing the data locally to avoid spamming their server\n",
+ "\n",
+ "# write the text of response to the file object\n",
+ "\n",
+ "# close the file object\n",
+ "\n",
+ "# Look at the file you downloaded. What's wrong with it?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Warmup 2: Making a DataFrame\n",
+ "\n",
+ "# read the \"iris.csv\" file into a Pandas dataframe\n",
+ "\n",
+ "# display the head of the data frame\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Warmup 3: Our CSV file has no header....let's add column names.\n",
+ "# Refer to the documentation: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html\n",
+ "\n",
+ "# Attribute Information:\n",
+ "# 1. sepal length in cm\n",
+ "# 2. sepal width in cm\n",
+ "# 3. petal length in cm\n",
+ "# 4. petal width in cm\n",
+ "# 5. class: Iris Setosa, Iris Versicolour, Iris Virginica\n",
+ "\n",
+ "# These should be our headers [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Warmup 4: Connect to our database version of this data!\n",
+ "iris_conn = sqlite3.connect(\"iris-flowers.db\")\n",
+ "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", iris_conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Warmup 5: Using SQL, get the 10 'Iris-setosa' flowers with the longest sepal length.\n",
+ "# Break any ties by ordering by the shortest sepal width.\n",
+ "\n",
+ "pd.read_sql(\"\"\"\n",
+ "\n",
+ "\"\"\", iris_conn)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Lecture 36: Scatter Plots\n",
+ "**Learning Objectives**\n",
+ "- Set the marker, color, and size of scatter plot data\n",
+ "- Calculate correlation between DataFrame columns\n",
+ "- Use subplots to group scatterplot data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Set the marker, color, and size of scatter plot data\n",
+ "\n",
+ "To start, let's look at some made-up data about Trees.\n",
+ "The city of Madison maintains a database of all the trees they care for."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trees = [\n",
+ " {\"age\": 1, \"height\": 1.5, \"diameter\": 0.8},\n",
+ " {\"age\": 1, \"height\": 1.9, \"diameter\": 1.2},\n",
+ " {\"age\": 1, \"height\": 1.8, \"diameter\": 1.4},\n",
+ " {\"age\": 2, \"height\": 1.8, \"diameter\": 0.9},\n",
+ " {\"age\": 2, \"height\": 2.5, \"diameter\": 1.5},\n",
+ " {\"age\": 2, \"height\": 3, \"diameter\": 1.8},\n",
+ " {\"age\": 2, \"height\": 2.9, \"diameter\": 1.7},\n",
+ " {\"age\": 3, \"height\": 3.2, \"diameter\": 2.1},\n",
+ " {\"age\": 3, \"height\": 3, \"diameter\": 2},\n",
+ " {\"age\": 3, \"height\": 2.4, \"diameter\": 2.2},\n",
+ " {\"age\": 2, \"height\": 3.1, \"diameter\": 2.9},\n",
+ " {\"age\": 4, \"height\": 2.5, \"diameter\": 3.1},\n",
+ " {\"age\": 4, \"height\": 3.9, \"diameter\": 3.1},\n",
+ " {\"age\": 4, \"height\": 4.9, \"diameter\": 2.8},\n",
+ " {\"age\": 4, \"height\": 5.2, \"diameter\": 3.5},\n",
+ " {\"age\": 4, \"height\": 4.8, \"diameter\": 4},\n",
+ "]\n",
+ "trees_df = DataFrame(trees)\n",
+ "trees_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Scatter Plots\n",
+ "We can make a scatter plot of a DataFrame using the following function...\n",
+ "\n",
+ "`df_name.plot.scatter(x=\"x_col_name\", y=\"y_col_name\", color=\"peachpuff\")`\n",
+ "\n",
+ "Plot the trees data comparing a tree's age to its height...\n",
+ " - What is `df_name`?\n",
+ " - What is `x_col_name`?\n",
+ " - What is `y_col_name`?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now plot with a little more beautification...\n",
+ " - Use a new [color](https://matplotlib.org/3.5.0/_images/sphx_glr_named_colors_003.png)\n",
+ " - Use a type of [marker](https://matplotlib.org/stable/api/markers_api.html)\n",
+ " - Change the size (any int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Plot with some more beautification options.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add a title to your plot.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Correlation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# What is the correlation between our DataFrame columns?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# What is the correlation between age and height (don't use .iloc)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Variating Stylistic Parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Option 1:\n",
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", marker=\"H\", s=\"diameter\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Option 2:\n",
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", marker = \"H\", s=trees_df[\"diameter\"] * 50) # this way allows you to make it bigger"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use subplots to group scatterplot data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Re-visit the Iris Data\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "iris_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How do we create a *scatter plot* for various *class types*?\n",
+ "First, gather all the class types."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# In Pandas\n",
+ "varietes = ???\n",
+ "varietes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# In SQL\n",
+ "varietes = pd.read_sql(\"\"\"\n",
+ "\n",
+ "\"\"\", iris_conn)\n",
+ "varietes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In reality, you can choose to write Pandas or SQL queries (or a mix of both!). For the rest of this lecture, we'll use Pandas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# If you want to continue using SQL instead, don't close the connection!\n",
+ "iris_conn.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Change this scatter plot so that the data is only for class ='Iris-setosa'\n",
+ "iris_df.plot.scatter(x = \"pet-width\", y = \"pet-length\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Write a for loop that iterates through each variety in classes\n",
+ "# and makes a plot for only that class\n",
+ "\n",
+ "for i in range(len(varietes)):\n",
+ " variety = varietes[i]\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# copy/paste the code above, but this time make each plot a different color\n",
+ "colors = [\"blue\", \"green\", \"red\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# copy/paste the code above, but this time make each plot a different color AND marker\n",
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Did you notice that it made 3 plots?!?! What's decieving about this?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Have to be VERY careful to not crop out data.\n",
+ "# We'll talk about this next lecture."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Better yet, we could combine these."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### We can make Subplots in plots, called an AxesSubplot, keyword ax\n",
+ "1. if AxesSuplot ax passed, then plot in that subplot\n",
+ "2. if ax is None, create a new AxesSubplot\n",
+ "3. return AxesSubplot that was used"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# complete this code to make 3 plots in one\n",
+ "\n",
+ "plot_area = None # don't change this...look at this variable in line 12\n",
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Time-Permitting\n",
+ "Plot this data in an interesting/meaningful way & identify any correlations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "students = pd.DataFrame({\n",
+ " \"name\": [\n",
+ " \"Cole\",\n",
+ " \"Cynthia\",\n",
+ " \"Alice\",\n",
+ " \"Seth\"\n",
+ " ],\n",
+ " \"grade\": [\n",
+ " \"C\",\n",
+ " \"AB\",\n",
+ " \"B\",\n",
+ " \"BC\"\n",
+ " ],\n",
+ " \"gpa\": [\n",
+ " 2.0,\n",
+ " 3.5,\n",
+ " 3.0,\n",
+ " 2.5\n",
+ " ],\n",
+ " \"attendance\": [\n",
+ " 4,\n",
+ " 11,\n",
+ " 10,\n",
+ " 6\n",
+ " ],\n",
+ " \"height\": [\n",
+ " 68,\n",
+ " 66,\n",
+ " 60,\n",
+ " 72\n",
+ " ]\n",
+ "})\n",
+ "students"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Min, Max, and Overall Difference in Student Height\n",
+ "min_height = students[\"height\"].min()\n",
+ "max_height = students[\"height\"].max()\n",
+ "diff_height = max_height - min_height\n",
+ "\n",
+ "# Normalize students heights on a scale of [0, 1] (black to white)\n",
+ "height_colors = (students[\"height\"] - min_height) / diff_height\n",
+ "\n",
+ "# Normalize students heights on a scale of [0, 0.5] (black to gray)\n",
+ "height_colors = height_colors / 2 \n",
+ "\n",
+ "# Color must be a string (e.g. c='0.34')\n",
+ "height_colors = height_colors.astype(\"string\")\n",
+ "\n",
+ "height_colors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Plot!\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# What are the correlations?\n"
+ ]
+ },
+ {
+ "attachments": {
+ "image.png": {
+ "image/png": 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jlZLEhWswx1NnG+875SMN1LhMzbiEt24T7Q4FHcozSs3zF1L79hA1Xr+O4p4Garg6c+N12eLxprhotHOMjtl6p7yU6JkazW9BVqBg8yZNPylFqOexHBq8nuqlsKPY7rveb1emwiV+qrcp3Ti1f2cnHTmwg7q4huH57FXkd34luURvO6eWXluzlT2NxpPOBIZrZNmVk/NDVII4r6Gui4bSPiOSwhTMY/xm9j7BKVRp1Wv8sI5K0fP7lu5o3qWbqSfFopmVbG09dcY9FFy/ihZau1ZKCuUwJVMkpbfUf0XSdoN57yyqsX4zjvCzqCYN4hS3Fu7rqmm2eQNG81uQFSjYETD58lupU4VwMtm5YTiXh8cpEuqwb+NZRW03pfNgPJdqP+dwi75lJ3V299BOXtd4dRr1OHMuBdSkzuvpmjP30Lq5/ALfHinxpk7lESlTWLoEuW5XILikX20r+AzRYZtCjVPM2UJRAHRvVoUKX+e8hpSPqXCEdn6Wj/nxzXkYObGC3Z5q8W7DqiAWz81iBOXgL+dSg5pMEL+Huh5OV5oRi+YI9aYdNlRJ3ml5GLWNFdvSFR5itM/WQuAh/xxrDs4lf9DyLXF2Ax0/RtZOJHsz+Wh+C7IBBTsiPFTbss4+POCpRmrIMr7vyE/XUfM3rI+tj9q2t5H/HWrWgXd+rSPyyGZqXrszbZOyzpQa8lub4L4TTVX4T0X0sY3L00YfGQv4LKx5+OyQerH5A8M1dat1dW7buY3jQ0S99iEw6bx85cFk603hGpphjMRKey//e6/H1uc+cpwf050UdTqifzlCPffylgvsQ5iG5ZFmas4UvOKNCIW/raaZ5Qtqcr+eKX5a/nWnwoxT54qGDLFWRcl20SbrsBadPdReF6B1T5V28ZIobPcNLBzge2ItvF3SSAsdY4DnLrZGK4rYu62sowzSNJOP5rcgC5oFeHLKk1iXtnym3cOJ77ZNWm8s6bfl2OFD2u67ajV+JC3b+bSmHw3nzybVe4ok0+tTWvZ02GLO+r68QxtSp3JsYIfWNI+Xp/NOlOLJye6dJpVCeXI6rO24xbL+kiat93VeGmnS40+u+pF5orlu58yz4T05OeNqDrfeeU2F8+Skaf3rzXipkvgexHih8sxkve/DxYMdNl7sSzu0BosnJM91m7R+02PYS/3aJv06HF7CsmDPM4+28Os7tH0vWd6B2G6t7WpL3szr0PrVupx5vV/rsO7DTJ6FWlNPvzbEz4PJsYFebdMX/bZYxPbfBLVN+50vQS4M78lpdFj2P69J2zGgzvGlXse1e9LEVhaO2by2JZ5NhyeohWm/IaP57cQkF09OULCj5fA+rSvby+xI3roObXeOX91U14nDf/QOhSzu7lKSPch7iou0NMmmYNK5iXQk/WM+7HZJxXf4oYb053sJb2P5BmbfLosrPJXq/OmXm6mtZZhztijLTHnsuaVD67Aq+DwVrLOAlEzyER/uGmv5Goa5n5YCzjG+R+mPJcmjBUO5f0hzeY7M5LmcC3gjDdjNhZhwvu/aAP9mmTfteiNlKBwmSF/QTZ+G29dwDP8ck3I3mbl4kKagZ0m+L+/O4qpyNL+deEDBFpOX9mk77m/SGur8ms9aq/XM0vwLlmurNnRpu/fn+Xge5pqG5QFP61c2DYf3hrW2Zf6kv9aZPq32lntSFHspKFgpOe/radOWzzM/gl7Nt6xN2yG1NxvZtiuughUOP3aP1nD1LONjz/d44eowl/gdH+N8FSxj2y8rullXN2ibfibPTWEVrM7Qbu2eW2qTz6s8q5yn4b35fkaPaYdj/dqO0D1aE+/Pv8Bnb7Hh58+/bJW2aeehwnygM71rkl+X12oNd/G79qz1SIe0Hbcla2f2VEoKlnn9kNbfs8nIx8Szzkm9wzts15WJw8a7crn5HPG7Uteg3RPJ5WkczW8nFrko2Ar5wzdQZ8aMGXTw4EE1BwDIjxht/vh0utHsK7tlBx3+lsPlHQBgXHDixAlavXo1bdiwQS1JBUZOAOTDoBG794q0oezsVsRzL66EcgVgAgMFC8AISDsk5aleW6Sd+sutFscAgIkGFCwAI0GGpHx7Dx05acweH9xJzbc0JoZGeW5po+XphlMBACYMULAAjAhxJl9N755UobsJPHvaVQlXgZ6rO2inM9wYAGDCAQULQD5UBakr1ktdG1bR8gWWkGyeWeRftoo27TxE+x9aRXMzOBABAEwcYEUMAAAA5AmsiAEAAIAxAgoWAAAAcAEoWAAAAMAFoGDBGKFiX2aLfzkihompOQJyCSg+9qjQfXqetucV1UcPh6f/LkAhZ2SdLJj5Uqy8Gel55sro77NbzzQoV6Bgx5jkR8ORcnxBzY9CoNvc2nzJ8/vIpqWv3X5OZipI/FUfNQ10Ua2aGymp118iKI9PKXnHyR1l5KHg1l5qU3P54FszRF11aiYPPEvD1NuiZopAtvN0vkdmHicLAXkqZfP+5aUoh3mmne+T5T3K5zk2r7W0C3xAgIIdY3xrtMRHqi2qB18w5rfVU8NYK415TaRF1Se7pTd5bmtrSk+hJeCPnASx2BosWFBoUSRy7U2O+JtZqQpS2PzY1nXRkJyTmm/2FaDwA2w43yPzXsm9Gwq1UddAmIJVxrJMjOg+54oobF9z4j3S3yUq5fcIFAIo2BLEOzNZBk6WwI0Sq62Z7KF2qgz26Nv1BCv5ZX2aQktqiF9jpplqzFqstTalSs22/XSrknUOpXXfMkNJ9GzvoW9ZmiRD5nmapXJbad2qUJLNaIEtSWeDL2a7TrPmYd2nHIfn7df/g/RNdCnnYm9OTZw7J2etwJ7/9ibAULpzzEaVl6rVpIFlf5JU3tmO2Z167xLXs6Sd2odpFk7mY+o2iXVLNjvcPmZo6nTUym15FUtznjbSXWtu9yHzeeZD9nuXcp+n1ZP+ZHFBt9LMgzTvkUFy39Zn2kr8sbCxPwvyLunPQ8pzbOR4unsn51mzVl/NBbVkPiW3tT6L9jyHMh8DuCSVAOHqxgYueeshqczA2NnnzfBZRqBnM+RcMqycGdbMDJ2l5iV0mgojZ98PJz2MGc+3pAmvZoaeM0OdmQHH9VBsyRBqxvF5XrYzt9F/Yzm+zOnXYj93+7r012k/rrGtbJdy/Y7tMp+L49ydv7NgOy9zO3PemT9WMp1L4hh8DmravA7j2pPHNLa15oU5zfc3cS4qjxLXlAyb1tviyD/zPNV52649w/7NvLXlA/9e/mc+T/0nFjJd6zD3IeN5yko7tvOzwDVYex6Y2zjuXdr7bJ6HeZ4yr35nHsf4nXFOzmc6gXksPaWef8pzzGS6d87rTOanyn9HPpt512bZNxg9uYSrQw22hJASqZQ0a9a2Eb9KhWmqGoxRv/yf4000mfbHrCXZWupa5uP/HgquGb5ZNbrFKNnXLvZbtm2jxqUy56OmNb5Eab12pvg58pJX+s22hSkyGKWIXvquJm8VH3F+IHN/lYPEPtVxrbX8bGQ+F1krmOeeJ3VdtDzX+yO1IKlFcK2IQkOW5mvOLzXt8Rp12+Zd9tpf20rnPYlRbBv/q+NrqfJTYJi+U98a1TRq1p7XRvSaUHSX0c5R7eW92/Zj2b+xgHoOGLUys/VCf37mNdmez9TzdDLctaa/D5nPcxTkc+9MMr5HOT7TnF+sGBU9VD9N3vX0rQ4mme6dk9gB/QnXj6+zje8h//MsbdT75PX7VxWkppE852BUQMGWEFwiNfpn+C2qkQ9y2qa2POEPg7x+0m9aIX1AjPnBzAv5va78ebqll8Jj/LJKf5krfWVuYPbBakMU2F7J+Wg241maSNW9GZYh9aHPkWTTp9l1MAzm/qVQYDaT7o0ZTaT8ke4M1erNmLJPZ3N6dkZwraVEAd4j6SfW3++EIVQzdWRpts3t3sUptlf+i9KupHopHPEdjOnPFxdqxIZCvbtoIi4+ULClyDy/YQ2aocSaF1z61V9mi3GFxrXMvBnt7wtNX3ueH/hSwEP+xXI3eij8WJw/oA36B7FWarWmMdlwVDr7cbMwGKIG6dvTFXyOFsbm/hOFAk4ZDMacte1sjOhaR4i9hUZqeJSs3Y2U0b5H/LwmFJwqqGQl53vnIe8c+V9LXQPqvLT0Bl092yNGQQkUDSjYUqQvYpRYVTOd2RyqfzgGIxTWS6lOYhRqdRgpiRIadDQvWV90lzCbyYwSvtnkGCB/lY/8ejOZKmGbtQJFtutM7FN9JKK7+h0fzTTXz2Q+F1lbbOIU2W5csd7kaSEey7VeajZzSzOgup5cMJs4Fb4FxifbUEbW/Tia0cWwR7WkRFsDFJkfTihIo9k9f3K/1mznmYq5rU2R8Pl37E02d4+EWHc7hXgP6d+j7M+0lZ7g5kSB2WjWTX0O5BpTnmPHvUsSpXa+N8Z7YxTa5BkLLUkaRLVTU7LGbGneBkWCSzwJYORUfEyDhdSUNFRJGFhIqmvT2nRDErWNxXDDNJBI7lPtw2pgoQwg7Me1HsuCzTCDk81wwzRoSbeOsf0207XUJs7dMODIcp3yU8s+E8Ygtut/OPl7SQ6DHiPJvtKcu2U/id8xCaMVPf2txronOS/bpTsnwbo/R0p77pwX5vqFty5MTLMa05KGR5L4/BPH5PzTlzu34aTfD8sya16nPAPmfiTx/m3nnrx3zmfmYVvepDlP9TudDNd62d+Y23PKcB8ynqe+1o79fnFKPJeWZ0uS495lzXNzH9bnKLFfIdsznSTl3KzrrfmjPx9Z7p3lPAxDJ/u9dxo/GUkZTIGCkYuRU4X84Rugg2g6AJQTUluRfjcximviuhQAoFggmg4A4xBpqtWNpMxmdIvFLwCgdICCBaDsUMM8dCtfrr0W0GsVAKBwQMECUGYkhnvoCU3DAJQqULAAAACAC0DBAgAAAC4ABQsAAAC4ABQsKCFU9A9rBJd8yRjxpIzpS0YDGr/u7iyRX0Zz/1MowDMFwAiBggWFwarYbB80y4czT4Vn+mLNS6lIHNasrvgs56OnpMP1fI43onPLhjP/rPue10RDw7nWKwL28GnJVBiXlb7xHYAfTEigYEFhcAYYt0aMEV+q4sN1WN+tsq1W0GDpdsQxgzhON6IV6Va4rF1rxrh2oysFGXJj9XPLeVYtTvVLqBbuDGou5yhFmWZfjvFwxwS3nykAMgMFC4qHrakz5GgSdDQR8rbZg1A7PuqWGmD7LrXMSTo/zvOWU5c4Sx/2eJJUbTfPc7PtI0WZR2mz6dTdVgDJVKOLOPLNIP2xMzW72mvxxvln2jYbPlqu16x7KPz9b1ma5kOJyDmJ2q3l3tvDtCWPWw4B+AHICy4tJ4AvYjAqTH+qNj+tQq8RiJ1J+kc1fMmafmZ1/6mO35vbmr57zXlnYOmkL1b7Pp2+YPXzkOUq2XwHM87jCZmCXud8brZr4nXOoPamX9mUc7Vj7j9dvqU/tiVP1Dk4z1Wf53VtoV9m3NaJ7X4xtn1ZfCTrecbztvuqX6O6Byp/jP0Z2yeu0bbOPJZ5PY77YdnWmheJ83dsl/lcks+GmS+23wHgAAHXQclSG1rO9Z/8yBRY2uky0KeiqqTCtUJL/2wucU1HG/Rams4bpVlVn08Nam9Glsk1Mk26fEt/bBaHG0UzfmlqIO43M26bHVX75mMH5luuqqXRyDMVlH1CBOAHIA1QsKBMyBZYOg/4o88FS05DxDUineb1mZtETaOZkQe9FiUtfZXNRhB9R/Orx2tEd81NoaUjw7F/liVwulnQMANxd+7Jsm16mn3JPGmLpo8/WkzKKgA/mDBAwYLCY9beTAZ5boSxQ5PkHlg6M1FqTyg4rk2uH8ZqdbDwQa/ttSXGDK6fRqHpyn1YI6cMx74s98DpPY++RO+TiRy2NWnTjZyMVBKKra8cA/CD8Q4ULCgcVX4K6LXCZopYPnbRLfVEKYGl88EIQn1uhsDSieMqxZ41oDfX0jab52YGx04JRJ1P0Othzk2Mr1qJmhI1ZqM5NIkyFJLao1WZKuXetmD4hvT0AbcdzZ/6eRj7F8VtD8T9QZqTYdtCgAD8YMLCJdAEMHICo8duSCTJNIixBaxWy01DlrTBzE1DE06G0Ypp6GKkxH4F67aWgN4pxjqW7YxkGA3pZDueM+h1rufmOF7K+ZiknFeu+SZGP7kcO3mdCWMiPTmMhhzbWkke00i2axnuOm3XYN2/5XlJCVZuXVcaAfgBMEHAdQAAAMAFEHAdAAAAGCOgYAEAAAAXgIIFAAAAXAAKFgAAAHABKFgAAADABaBgAQAAABcoUwVrjX5hdz+XdG1nidghA+fVtvD2AgAAoBiUbw12Thq3buL9ZntAXz4U6qca3RsNK+OVYQqIG7mBLur3WRQvAAAA4BLjqolYImWQiq6hu0QT/67iYo2UKzTdpV56B/GiqO+66y4644wzkjVjR/rUpz6lti4mI4nTWY5YWyVGUAhSMT7N2KyjR+V7yef5CJ8PS6tOfkHdR3mf8sbt59+8nlFcy4jzEox3yljBhqlBPdRms6+E7ao2fd5KeDHxTSv+TRO+ZsUxek9aBSv7CQaD9Oijj9Jjjz1GH/nIR+j888/Xp820Zs0atbUbOJq9E4GlMwXedhnrR8OaXFM4HgpuzeZQ34r50S3B1ghbMG9rcgSILxgjfD6qghS2hO7LnXzuUyHIdn2Z3hl+QhIB2vN7RswuprwKasPlpfOZsChh8zyH77oq4WceZKR8FezeamqU5uECNvtWVVXRhz/8YV25XnDBBTRp0iR92kwzZ85UWxaeaKuEGrNERBkIUHjaGL5M8tEwP2wqyspQOqf0pYIKQxcuWCxP/rDLfRgmqkwKch7mx7al17iX+nwP1a8czy0QY4EoezOIgrw7yQhGvjUa9ba0Ua/WxHcyG7IPeeeG226ESEHV15x8Fjj1Uk0BW1pAKVOmClZeCvVC6M2+9ugt5YcZeNqCGajbQSRRMk+W1pOGXfaScLIUb25rbW5rp3az9J+jwjQDdSeDjttrEPpHw1JaD7S2O4zLHM19wzatWbZPbCPHNGOzSoxVLoRYj5n4cA1zbt0h+7moXxk4zjPn32WgyuuogWW6LnNZO4XS3NPEfeZ8zXoOjtYH28fcsq59l1qmyPQcWZc7a1CxDL9JkPZc7PkbSvNMW7cJbJHINyMl27HseS7PUaUeQJ6LQ8HKRL5lypdseWliBni34lvWpYcSlP3WqPde4uumvCOSMj3z+rT92hJ32ZHnae8LKA5cokpQntF0jEgYEkFEooTYomqYUTHkv75QtlXRQ4Zh0aJF2tSpU9Wc2ziieTijd1gie+iRUsxoILKdmpbrNqKdGJFGzIgpbVG1bzMPEvsy8iG5nax0YG6byD8zuolxDON4qfsxp43fmdemIqA492m9Fsf+9Xm1nf08ndsl15v3f9hzs11Dmut3nGfOvxNs15RmftjrUtdhOwe1TqbN/WXKU5lXx7LmAx/Ndi/Ma8j2HCXPyfyN/M92ng4ynYv5G076tTvyyLptIu/T7T9xTeY1JjHOlcl6LPv1mccyn6PM+ZIlL60k7pWkdOdorEs+R3w+6jrTPxtpjq+uzzxn2z75+Ml9g0KSSzSd8qzBSgnNrPHocSLbyD+Pa1jzA0QqPqRecpS+V6nhkhnnUrZ1xuMsBbhGbg3+vbZGL3naah5CXRctdwS3NmOfJvqeVWxN6Y/mFzp5rc4g6CoGpserh+U2Ym4OQ7y7wyhFt/jJx6Vno9Zt5Ke5n+ZdyfpNrW5wZgYET29clh0fNakm2nT7z0wO5xZaPqImwbx+p+5jha/ZCFC+xvzlcNfVRo3Opm4zHq0802aQ9kxI8746li1WrBlTtc5LEv7etyC5l0zPkZyrHq9WzfvWWJtS05ynk0znYpLmmU65fyqG66hJe6zsZMyXLHlpY14TscJT9FD9NKlVZuv6yfWZN2PZGscXjBi3vAeuIet3TN5pPn5JBMSfoJSnguWXtnNmh/HxmlZP1VGzuZiXLw5TJS+vDFZTr/5ii/KS/kzHtqWGfIjMPhr1QvYEN2d5EQ0MRcovoa9CNTeJEVecYnuN6fpp0rcr09kVnPlypkX6XfU85WNJf2xCUeRDeuOy7Fia8KQfq9xI9Lv18lfP+mEdwXU5gpFnJ9l0aDZ5Dkf650hfRJ6lndRVl4tySEf+51JKZMuXXJH+YP05SBhrNVOHs/CcIMdnwyxwybvJ3zX9LGXUhPyX7yAXiqSZW/aDJuKxo2yNnDxLw+rjpdlKaMnlFkVqUV6lWZqTlyr54fKtyd1K06gVcF1CakiJazRrjRajKS1pAJKO2plmOTgNyshJ30++Rj8JLLXpHIl3N+iFg9rQUNJwqCzxkV8vNBm2AiO6rpR+3MxEW43+OnkmhvTa5/Ckf470RQ7ys3cYybmMDKfi40Im1+2yPNU5kXu+ZKCvPdkSpRRfNnJ+NmSUhPzP4d3MrdUHuEH5WhGPOyylWrN0amn+yYSzGSnaahiK2Jvj7ApcRzUZpzaB5YqpNIyasbmftgXJ2q1RKzZr06pp3vwwqONHd+VWgzP3nwJ/wFJL6MOfW3ExmzxTCxkZr8uJmW/62G71fOSAWQPT0Q0C+b/j3guZniM59/YlEfJvNVtW8i8omdjOJSv2+5e99u4h/2JDadkUSd9mque3Z2SFQSFGodYQv0gZ8iVLXjqxtkSZeZD6vnE+Owz9sj/z/G3Qj292fyW7zeQcI/O5oqEUdNbCM3AXLvkkKE8jJ3corpGTkDQcMZJpEOFYbjFIkZQ0vkjOG5hGEEbiErhBwiClNmH4kWKYIVgMQ4xkGldYsR/DPHbCUISPYa5LHJ8x1+vLW5LXctnfJPfFJXPDQE3NW/dlv2Y+L5shiXmsNOeWZrvkfqwGKI48v2lFcppT5t8xjmNYU8o94GS9rmzXL/cokW+Jeyf3ZJjnw3kPcspTlV86zv0/bMvXdOdpI8O5/J36r6c0z7TtuInrVdumwXru9u2y50/WZ07lQfp8YbLkpRXr864n6zVYziX3+6PeRWu+W95Pe16ke29BIcjFyKlC/vCN0JkxYwYdPHhQzU1srr32Wurr66N4PFNfSZkiJV3ps5GmpRE392ZHhh9Ifxt/FAo4LhUkkX7NGmp28R4CUEh27SIaGiI691yiQEAtLHNOnDhBq1evpg0bNqglqaCJGICywNLM3xfR+zV1i2L5D0CJ09REJJ5mg0G1YIIABTuh4JqPaXHolkcmx2B9WDAWEnEyoKxLR2zNDUDxOXmS6A9/ILr6arVgggAFO6FQ7v/M5MYHWrksNBPG4BUK06WfSmgaBmXGM88QhcNqZoIABQsAAMBVLriA6CMfUTMTCChYAAAAriJGTidOEGkJk9qJARQsAAAA4AJQsACMOVEJSJxMGd3oAQDKiXGgYA1fp7q1ap89sHHCgtUaVgpWraAUqesiEp/FQrCBn1ljEgBQvpS9gk1EeFG0pfgMjVNoZZgC4pM3S3B22X7dunV07rnn0uTJk+nhhx+mF198UZ8206dkIBcAbjBH3Nlx0oOH90DBgnHF0yx3sAxOsAe7zBVslDYfCFCX/lHKgISVIiM0m+E/NHNUmVmzZtGSJUvon/7pn+jP/uzPdKUq02a6/PLL1ZYAZKGv3d7k60wp4499RLYhU21Eox7eFCdaYjnmkpBabsF5nmjdAQXiFIt4ad7IsoBlKcurLGeyTCi45pag3HwR97aIH1jD56zuxzPalvTfqYIW674+Lb4/xU9nwidsForvixiMK/i54wfRSObzN9CVXMbPZyq8HdXydmp2xPCTz+9E4jjmuViPaT2/qGX7HN4NADJxiuU3LDewvI9lJsu3WQ6wvMUynhi/AdeFwRBFZnamhmBb3MlfCY1659RTA4xFQClRFSTSo8Qw2+pTa4ytNUTRMG+n5kfKYIT3r6b1pmeF9ZiJKEa19uOtT1PTBSALGsthlp+x/CPLfJZfs3SwPMtyI8sMljNYJhplq2CjW+qpWQ8obAQUl4DIgdjyhHN534I26tkekYYyAEoHFV80he4Arxsymoal6XY0zbWDEkDNQpVFycbkjeCkhxA04XdGjx/MbHP8Fow71q8n+vznjfTlL/PTMMKPpCjWQywtLKJYF7PMZvlPlkdZrmaZ6JStgvWtMY2ZhvQ+WDFuShu5RQJVm5H++W9s78jjWQIwahIxUa01xyiR+G/mAqPeFyq+hs3l1j5SZxqJEtZj9GajHwZW45zGRiIJACPpzjv5do/gfr/Jch3LHJZ7WW5lMZXtx1gmYm01HWVvRWwlGSSap3c1U+1iP3nEsInMoMQRCm9Tgb8BKDaDISI98DrTwl+5xHPoM3tDk2k0Rk7WGqvgrNFaa6w6zhotGM/I4/XpTxM98IAR4SYXpLb6PIvUTj/BcjHLSZYHWX7HIrVXMWCqYAFJyt6KuN3SRBxZ0EixacZ415q9XdSp12g9FFzPKlaWT6un6miTfM4AKB5ra4waJz9/FBoyvnA5BVpIo3itKZMS5kKlMdyH2ZuhxrqgTU1gSNBE49prDeUqSraHb/9ZZ6kVGTjC8n9ZxBp4NUsNy3+zPMASYJlwlsF5UOYK1h4dpmmeZd4abaQqSOHENmoZAMWipZdooMuY1puB241p1+Anf6tyWiGGTaLLQ46+33lNyWU+PqfF6vzAuGf79mQZ7bXXiObYWjN4OcvrLP0sn2N5L8t/sTSyiNHS51mkr3UyC8jOuGoiBqBksVoQi2uUnGLxjqYP1lr7ZWVq9v3OtDQfLw0nt5mvllG1pekaTDRkrOp9LPUsC1mkyVeahX/CcgPLWSwgd6BgASgWyyy1xLUdhWuaNR1GWBVuq3Xe7GPlGut81a4j/cFW5xNmP22L3/gPJhSvsTSzVLKsYfkIiziK+FeWv2d5FwvIHyhYAIqFrRbLNcotw9Vic+yDNce07nLszxzTao6LtRlWMSnjYlkBL8ulbxiUI2845BWWnSy3sIg18B6Wf2P5DcvtLGeznAYVMSoqNOmYVMyYMYMOHjyo5iY21157LfX19VF8pIPEwMRFapSJoTYK6YcVwyapOYqxU4I2VpZNanqEmMeLWpSu1FqXVBqKVTCPb8V2nqxcBwrg5ALkzaJFRC+/bEz7/UStrcb0H1ikZmnlBZYXWayIUwdp2rXSyyJDaaycYLHyFos4iAiyLGERy+B3soDcOHHiBK1evZo2yHinDEDBZgAKFgBQDM6QIaOffoBO7X0/nfbHs+idez6qL5fa4zks1qEvp7M4+0FlO+e401ksYpxkRWqp57OYvM3iY7mQBeQPFOwogIIFALjNyyyePz9Bb7+Ta6qvXEDzPnAOfe1HhgW4KNKLWKzKU5ZNYbEqXRkmA4ve4pOLgkUDOwAAjAHShCv9nxcenEe/3zOVTvxuKj228526UZHI37LIcJj3WeQ9LGJwdJ5FoFxLFyhYAAAoMjKe9IssUoP930nPUOWZF9CZZ6rmYjBugIIFAIAiIla617P8gkW8IWEIzPhlHChYcZdYQe1quEG8O0DiKrGiol2G6RsMhiigL0tuBwAAxUYsgD/LIha9P2aRJl8wfil7BRvv7hC/OAasSBu2B2hI02go1E81urecOIVWhikwoJE20EX9PoviBQCAIvErlkUsMhRmO4v0n4LxTZkr2ChtPhDQw9UJ8cfCRBJBh6c98wNGmDqJoEMB8sv4PomsU9dPMTg3BwAUERm/KoHHxXnDVhbUXCcGZa1go60d5F2WdO0WO9BD1V7lCq7KS9XbYqxMY9Qzx6sc/3vIO6cnrYKV0Urbt2+nZcuW6WnPnj109OjRxLykb37zm2prAAAYHnGcH2URv74zWH7EMokFTAzKV8EOhigys5OCBfI8I/2zixcvpi1btuhp7ty5dN555yXmJX3hC19QWwMAwPCIcpXA5OL0QdwQIhD5xKJsFWx0Sz01BytZMSbjwX4FQaMBACXCARZpFv5rFmkWvoAFTCzKVsH61hjxXTVtSO+DbYtq1L24lvpjyvPSYIz667zkrfIafbH6wjjF9tbyMn0GAABcQSyE/47lH1g2s4iLQzDxKHMjJzti2ETbI7oyFYMnve9VDJsoTBHpdxWDp23VULAAANeQQOXLWKRp+F9YxLUhmJiUuYKVMbDJJuL2wSB1Lg5TZUUFVQarqVePHuKh4HpWsdMqqGJaPVVHmyQIGAAAFBRxnv89lv/D8kmWFhaEe5vYlPnd91GT3kxspKZ5rE6XhtW8RZFWBSls2QYAAArNQyw3sdzM8iUWq0N+MDFB8QoAAEbBKRaJv3ori3hpamV5BwsAULAAADBCpFm4m0Wi37SxNCf9ygEABQsAACMlxCKO+6Xv9dMscCIBrIwo4Pp3v/tdev3119VcYRGPSaUAAq4DADJxjGUTi9RYv8kiyhV9rhOLXAKuj0jBTp8+nWKxmJorLJbTGVOgYAEAmdjAIoZMEm5uMQtqrhOPXBQsmogBACBH/sjyHZY7WR5hWcIC5QoyAQULAAA58DqLWAjfxrKFZT4LANmAggUAgBz4CsvdLHtZPs4CJxJgOPCEAABKltdeI3rwQaL77yf6xS/UwiLzKouMce1hEaf901gAyIWyVbDx7oAeYk5Se59a2NeeWGZbPhiigHMZAKDkef55GVlAtHw50b//u1pYZL7K8jhLmEUc+AOQK2WqYKO0+UCj4RIx2kbN60MqWo4RVUdfzslwixin0MowBQZ42UAX9fva+dcAgHJABhU0NhL98pdEh//4Jv3OIodZjljkNRYxQrLKSZaRIjVX6W/9Ccs2lnksaBYG+TAOhumIw/8I+cX3MNdg26nJ7m9Yaq8riTq3BskjynZJA9H68LCB2jFMB4Cx55nfvEWXfPAkvf3G2XTe57fQ1G/+s1pD9B4W69hTCWZ+IYsVsfCdzGLd7iIWZzPvn7JY47UeZZEA6b9h+Q+WD7IAYGVijIOVZuFdftIkcg5PB9b3U8+2HqK6LhoSpWpdz0RbKyiyINXpvxz3jjvuoK9//ev6/Ntvv02nTp2iSZOSJvhXXXUVbd++Xc0BANxE3BD+rfa39OLJV+lJ+hmdc9pkotPf1tdJ7XSIxUqcJcZi5Vcsz7GYyD6fYHmexYpVAZt4WZ5iOZcFACfjXsGKsqyh3oTy1BVsbDmFl3r0dR0zhyjs3ZyTgnWCGiwAY8cgy2dYpCn4fpZLWdxCY5EhOKK0TaRp+WyWd7EAkI5x72jCt4ZfjQURqqhQ/arzmnTlqq9b0EY9Kvg6AKB8kFqmhHz7LYsYFrmpXAWpvZ7HIs3LpnhYoFzBaCn/Hvt5y6mrrpkimayDq7xUuzemFG2cYntryTtM/ysAYGx4keVylgEWUa4XswBQrpSngpWm4G6zbhqj2DZDaUZbAxQaNJZGdzVT7WI/ear8FOAXNSLLByMU3lYNBQtACfISywoWUbLSLPw+FgDKmfJUsPOaqPFApRrvWkP9oU7dKti3ppFi02RZBdXs7aJOvbnYQ8H1rGJl+bR6qo42keqxBQCUCNIsLE7z97P8jMXtZuFMbN1KdM45RGLbKOmOO9QKAEZA2TYR6/2vmpHMfldeSk1qmaYPy1FUBSmslg9n3AQAKC5i0LSIRYbGdLG8k2WsePNNoiuuILr+eqKLLyb62tfUCgBGQPn3wQIAyhapuUqzsBg0iSvCsaq5WvnKV4h+9SuiV19VCwAYIVCwAIAxwTRokhqsGDTNZBlrTuMvooxUfOIJohdeIJo8Wa0AYARAwQIAis7LLDexiJKV0G+lYtD04Q8bzcSdnUZ64AG1AoARAAULACgq0ix8LcuvWcSgqZTcEM7kSvTNNxN99rNG+sQn1AoARgAULACgaEhz8DUspWDQBIDbQMECAIqC6aFJ3B+WikETAG4CBQsAcB1xImH10FQKBk0AuA0ULADAVUrVoAkAt4GCBQC4hmnQZHpoQlxVMJGAggUAuEIpeWgCYCyAggUAFBzToKmUPDQBUGzKVsHGuwPK2X8FtVtC1SWXqxixwmCIAmm2BQAUHulr9bOIQdN3WWDQBCYqZapgo7T5QKPh1D/aRs3rQ0a8V1akDdsDNMTLh0L9VNMqKjZOoZVhCgzwtgNd1O+zKF4Lsq8f/OAH1NDQoKenn36ajh49mpiXdO+996qtAQDpEIMm8S38AosYNP0FCwATlQpWLJqaphkzZtBBccQ5DNOnT6dYLKbmCovldHIkSu0VEfJrTeTl2msDdRrRdaTWupKocz1Rg/zXo+uwsl3SQLQ+rIe3c3Lq1KnE8Wtra+mJJ56g559/Xp8XpAZ8mjgrBQCkIM3CH2ORITk/Z0GfKxjPnDhxglavXk0bNmxQS1Ipf23RF6HmFr8e4zV2oIeqvSpIXZWXqrfFKDYYo545XhW6zkPeOT28TJ9JQZTn6aefridRpoI5LwnKFYD0mAZNr7HAoAkAg7LWGNHWCqrY5SdtDUKoAzBWSM3VDDm3nQUGTQAYlLWC1YOuL4jYDZoAAEXDNGiSGiwMmgCwU/5tnvOWU1ddM0X6iLwza6k/pps7EQ3GqL/OS94qL9XujRlGUPw3treWl+kzAIBRYBo0mR6aYNAEgJ3yVLB97RToVoqUYhTbZihNz/wA0faIrkzjj4WNvtcqPwUoTBHpdx2MUHhbNRQsAKPE6qHpSRZ4aAIglfJUsPOaqPFApW6IVFFRQ/2hTsMquCpInYvDVMnLK4PV1Kv3zXoouJ5V7DTedlo9VUebdIMoAEB+fOUr/MHgL8bHbnqWJt/0H/Tsh8ScCQZNAGSibJuI9f5XzUj6sByFZ2lYLbcoUla8YbVt0zy1DACQF8ePyzA6osHAN+nU0++nC954DwyaAMhC+ffBAgBc5U2Wx1mkKVj4wy9m0akn0SQMwHBAwQIAMvIYy40s/8hSOfswnX46Ubz5Vn3d1Kn6PwBABqBgAQApvMpyA8vfspzN8izL1uV/TydOEJ08aaTdu9XGAIC0QMECAHQ0ludZvsEiilWU7A6W+1jewSLOzaQGayY4NgMgO3hFAAA6G1n+D8smln9hEQthmZ/EAgDIHyhYACYwf2CRftb3s9zJcgvLPpaFLOeyAABGDhQsABOQUyy9LNezNLDUsexiEQV7OgsAYPRAwQIwwTjJ8nmWj7C8zrKb5assM1gqWAAAhQEKFoACIUZB32NpZ7mHZSfLcywnWEoBMWDqYPGwyPR/KfkTFgBA4SlbBRvvDihXiRXU3qcW9rUnltmWS/B15zIACoQ4u7+dRYKNf5HlVywPs8gwlwUsPhZpev0hyyssxeYYy70sEvXmIZatLA+yyLkBANyjPBUsK8yGA42GS8SBLur3JcPVtUUNl4hJt4hxCq0MU2AgdVsARoIMZ5HA4lGWz7FczCK1VVGi/SwhFqkZSnxUmRYnDT9j+QLLLJY5LF9m+W8WUcYvsbzFki979iTTM88QnTqlVijEgKmPZTHLXSzLWeQ8RdHKsBsAgLtUsCLS1DTNmDGDDh48qOYyM336dIrFYmqusFhOJ0ei1F7RQd6BMAUHpXGuye5vWGqvK4k6twbJI8p2SQPRet7WEVFHjrtr1y763ve+p0//8Ic/pBdeeIE+9alPqS2ILrvsMrrhhhvUHJiIxFi6WX7KIs2/f8/ydywfZcnm9F6MiqT2+huWp1kkfqq4HpT5aSxeJVILns0yleW0Ycq/H/oQ8fPINegXie68k/h5JXqH0ptiwCTN1FJrFsVezyJ9rACAwnDixAlavXo1bdiwQS1JgyhYE1acaio7Xq9XtKArKX96tTZq479MtE2rras19lXXpQ2pZdSir9XpbSGNa7kpnDp1Srvzzju1008/PeWczLRixQq1NZhoPMdyNctpLOezbGE5znKKZaTIb0+w7GH5Z5b3sFSwyDFmsnyZ5WmWTMdYulTTLr9c0047TUqlmnb0qKYdZvknFtlPkOVNFgBA4Tl+/Lh22223qbn0lL+RU1+E+kPLk5FzFnfqNdDeOfXUkIgZOzzSP3v77bfTyZMn9d8vWrSIpk6dqk+b6b777lNbg4mAuAd8gEWaWD/OIjVUafKV2uenWc5isVrdvutdRCtWGOmcc4gef1ytyID89kyWS1i+xCK1Ywli/gjLNSxSQ5aA5hexSE1ZLH03s0jTdJzl/D95m2bO5Frsco0+0ThAG87q0D0wiWWwND9LP+s5LACAsaHMFWyU2nf5k+Hq5jUlpn0L2qhHBV8HIFekf1UcLUgwcVFq61hqWcRoaQvLJ1jEN286/uRPiObMIXrqKaJjx4zwbvkgCvd8FlHmclxR7mKV/D8s0tcrSLOvKPzLWX71b39Hf7rxa/Seje3U/40r6IEz79PdHEohQPpZMZ4VgLGljBWs9KdGyK8HVc9ClZdq98aUoo1TbG8teR39r2BiI/2jQyxiACSKVZSTGChtYPk5y3Us72OR2mY23vMeogceIHr7baKPfnT00WbkeNIX+1csV7HcwbKH5dcs/8YifbaigMUq+CYWWS6WweexAADGnrJVsPHuBoqtVEHV+9r14TfR1gCFBvXVFN3VTLWL/eSp8lOAwhSR5YMRCm+rhoIFOm+ziMK6leVqFvG/+2EWUVpi9fsPLNIMnCsSXeYXvzDSo48Svf/9akWBmcIitdz7WcRQSgyuGllQYwWgtChPBSvDdII91OwzxrZW+Jr1xb41jRSbZiyr2dtFnXpzsYeC61nFyvJp9VQdVUoZTFikGfgJFhku80GWn7BI02qERcazzmM5g6UckFqu1FileRkAUFqUp4KtClLYYnwkyRia46Mmc5k+LEdh2d42hAdMGMQ9oIxRFUUqjh8+yyL9mDKcRYbNyHS5KFUAQHkwDsbBusO1115LfX19FI/DTKqcEQcOMhZU+iz3s4ijB1Guf8NSyTLcWFMAConP56O3pZO+xJk2bRqFw2E1B9KRyzhYKNgMQMGWNzJU5XGWVSxvskhT8NdYJCwbmlPBWDFp0iR66638vXYVk40bN9LNN99cMt/iUiUXBYviOxh3yLCWZSwyhlQsgMVtYZhF+lyhXAEAxQIKFowbxD/wJ1lkqMq7WKRJWBw4/AULAAAUGyhYUPa8wCIOGOaziCekHpaNLOeyAADAWAEFC8qaHSxXsogThlaW/2SpeeVqWnzN6XTZZRKgQfrT1cYAlAAy/lo8c102ShG3mBKDWMcSqjPQ/QNqX3EzQnOWAFCwoOwQz0sytEacQ0hfqzhdGGBZxCJNw0ePEj38MNEf/2iEcItE1A8BKBHkGS6ETGYxMUN1hpdeQL87uJGafUnHO2BsgIIFZYUo0ttYxKVhFct2lq+zWD80Jj/+sVGDFSf8AJQK4nFLujR+MUoRBynWYA6m4x2puf75jBWscFPDcoLiAgULygZxYSixUr/Lso3lXhZxbZhtLKsEQBpEKR5MAMwaLJzplA5QsKCkEQ9MD7FIUPNmljaW37HIuNZMvPvdRKtWEX35y8nwcQBMKN71p9SPJuIxp2wVbLw7kOjUt3bmJ5e3U1QtE9/FgTTbgtLmf1kCLOJ5SQw6JNqNNA8P54B/yhSijg6j9momAMY185osNVcfNX3jqxTW0EQ81pSnghVn/wca9eYQbaCLS2pKmcry7QEa4uVDoX6qaZWlcQqtDFNgwLFtGsRrU39/P/3iF7+gI0eO6B5XZNpMbnmvAknEcOP3LP/KIvFY32CRWKxSe/1zFrg2BACUC+X5tRLn/WYc2CovVVM/xQZZQT4WJpIQdbzYMz9gxIGVEHVcC/JLSU5C19UZ2zoRZf3ggw/Shz70Ibr00kvp0UcfpVdffVWfNlNra6vaGriBKFcZv3oNy/dZxDG/+BGeywIAAOXGOKkOGDFeYwd6qNqrYuiI4t0WY2Uao545XhVZx0PeOT1pFaw0H99+++108uRJXdkuWrSIpk6datSSVbr//vvV1qDQ/JJFgpqL56WbWcTdYR3LJBYAAChHyl/B9kWoP7QcMV7LEBlwLyHkxCH/P7JIk7AEOr+RBQAAyp0yV7BRat/lp7AeWB2UE6Jc21nEWcRelm4WCSl3MUsFHPIDAMYBZaxg4xRaEiG/2RfLeGfWUn9MhZcbjFF/nZe8VV6jL1ZfGKfY3lq9ORkUH43lKIsYLfGd0cezijHTj1guZUHAcwDGmjg9ueNJNQ1GS9kq2Hh3A8VWNhlNw31cF+ozDJtoe0RXpmLwpPe9imEThSki/a5i8LTN6K8FxUP8pYqxkjQFL2VZySK+WCWM3BIWAIDBwADRZz6TTGvWqBW5oA9HzDRKIkrt+qgKUEzKU8HKcJxgT8I1WIWv2VheFaTOxWGq5GWVwWrq1Wu3HgquZxU7jbebVk/VUaWUgatIhJvNLBLg/AIWccgvhktiIRxlaWSpZAEAJHnlFaK//muif/93oksvJWprUytyILolRo2sQyPmWP/E+P8AhR6KUPPaGgp0P02h1pDRosfr27vjCd8BAZ4GhaU8FawM07FY90oyB1l7lobVMositWwPN2KFhXOVXmQRv6j3sXyBxcciDvh/wCKGS+IvWMLIif9UsRC+kAUAkMpp/EXuYwV5+ulEN91EdN55asWwRClCfvLN8xPtMmqqusLVv3thCl7jp7aWXgovvUhfZ8X8ZjbyHqBiC0sZ98GCsUKUqjjdf4BFjJQ+xiJDav4/lmksYqwk/az/wdLBIo75pRYL4yUAsnPxxURf+ALRk08S/fznRI8/rlYMR59RQ62oqOH/kQzNxA7EToULx6ElRktgTTBGcKVTWKBgQVbE+cNrLAdYpIm3iUUsfeexiPJ8N4v8/w1LL4v0s36IRbwuScBzKFUAcufss4nmziX64AeN9P73qxVZiVNoPVGvaqXTBrwU6Y6Tb5mXOswm4kEvefdKEzGRf6bRjVaxvp+q1R5IvHy3qElQMCr4hmhqmmbMmEEHDx5Uc5mZPn26a24DLaczplx77bXU19enu0+ciIhD/QjLYyzPs+xnkdrp5SxiAfzXLDNZJP5qLrTwy/v888a0NHvdcQfROclIWwBMCCZNmqS7YC1lNm7cSDfffHPJfItLlRMnTtDq1atpw4YNakkqqMGCBFIL/SrL2SzvYZFg5i+x3Moijvd/ytLKcgOLRLPJVbkKX/2qvLhGEkf8f/iDWgEAAOMU1GAzMJ5rsP/wD0QXVf2RHqWf0KEHP0zTlz5Bkzc30DtZprOIy0IJDydKVJRtIaioIHrkEaLf/pbo+98nevBBogth6wQmGF/60pf0QCKlzkUXXUQt0uwEMpJLDRYKNgPjXcGeef9n6H8W3k6Hn/5z+sjCo3TP9wZoKov0qQ4XDm4kmAr2yiuN+ZdegoIFAJQvaCIGNmRs6r+z7K/uooNXbaAZFdPp0r8+g66df77ep+phcUO5CldfbQyal7F9ks6A0yYAwDgHCnYC8EeW+1kWs9zL8vWWM6j3Z2/Rz35Gelq1Sm3oIg89ZBzLTBIUHQAAxjNQsOMcqbVewiIRaq5jkeg1n2CRITQAAADcAwp2HCKOIH7NcjvLh1nEs9JTLJ9hOZ0FAACA+5Sxgo1SuwyWtjqw7mvXPZKYSQIA6CR8clqWjVOkOVjCwInv38dZHmSRZuEPsHAOqK0AAAC4TZkqWCNUnTdUq+aTtEWVNxNOht9h3nZlmAID4uGki/p9maJNlDdvsjzKIrXVLSxrWcRJRA3LmSwAAACKS5kqWA8FtzaRX81lRULUUYD8EqJOQtfV9VNMQtc5EIX80EMP6R5MJD311FN09OjRxLyke+65R21dOkhz8B4W6V/9NEuQReKripOI09ADAAAAY8a4+wL3rzdCL1UsMUMyxYy4sPpaD3nn9KRVsPKba665hu677z49XXLJJXTeeecl5iXdeuutauvCIp6NJHoGn4Ke1q9XK4bhBMvnWS5jeZ1FmoTFF/AMFgAAAGPL+KviLO7Ua6O9c+qpoUziG775JlEwSFxLJnrPe4jWrVMrMvAKy7dZxIDpGZb/ZJHg5X/KAgAAoDQYXwp2XhOFlxp1Vd+CNurZXj7xDe+80xgfms2LmkS2EQf8C1n+haWZ5bssARY0BwMAQGkx/r/KVV6q3RtTijZOsb215JX+2BJCgiw//TRRfz/Ra68RTZ6sVijeZhFn+zLM5nqWahZpDpY4q+ezAAAAKD3GlYKNtkrcQzW9q5lqF/vJI4ZNFKaILBeDp23VJadgFy3iU+PzM6PN3H23WsEMsXyFRQKbyxCcbSz3sIhbQwAAACWMZmH69OlqKjter1c88ruScqW3xfK7ui5tyFiqtaUsYwa6tFq1vC2qlg3DokWLtKlTp6q5sSHM8m6W81meZAEAAFAaHD9+XLvtttvUXHrKtgbrW5Mc76ptDar6nI+aUpYxVUEKq+XG2NjSRZqDH2ERv8EylvWLLM+yiKUwAACA8gGWMSXEQZY6lk+xXMzyMEsTC/pZAQCg/BhRPFiJlfr73/9ezRWWJ598Uk2NDR/4ANHvfifjUZ+lo0ffpPb2S+hLX1IrC8xbLHGWp1m2sohCnc3yryyosQIAQOniWsD18cy0aYaSnTTpR/T9779Ia9d+ku64Q60sEGKsJN6Wvs/ycxZxwP+PLJeyzGN5BwsAAIDSBQHXR8Ds2UQ//KHEL13Ac0Hds1Ih0FjEX3Aby3ksi1heZOlmESX7JZa/Y4FyBQCA8QEUrIP/+i+it98muuaaxTR16p/S176mVoyAkywSNk6CnYuvYHFh+CSLKFnpb/0ey1+yAAAAGH9AwbqAeFz6bxZxBPH3LOJ1Sdwa/oQlxNLI8l4WhI8DAIDxCxRsgXiNRYyVvskyk+UWFulb/X8sUouV+fexoAkYAAAmBlCwo0BqqjGWNSyfYBEfwU+x3MGyi2U7i8RnhZ9gAACYeODLPwLEGcQTLNIELP2qUmutZfkNywMs0t8qTcBQrAAAMHEpYw0QpXaJ+9oaVfMG8W4VD7ainbdQDIYooC+roPY+tSxPjrLI0JqvsfwNizT5TmORMHESPm4FyzksAAAAgFCmCjZOoSUR8oZq1byCFWnD9gANaRoNhfqpRle+vO3KMAUGNNIGuqjfZ1G8FmQ48AMPPECXXnopVVdX0//8z//QK6++QtVXVVN1pFpv6pVm4N+xfIPlBywbWBawnMkCAAAAWClTBeuh4NYm8qs5k/hjYSKJoMPTnvkBI0ydRNChAPklgo5E1qnrp5iKuOPktNNOozPOOENPFdMr6O3Nb9PAjgF62/82nfu9c6nxvkZ9yM3lLBLcXIyYAAAAgHSMq07C2IEeqvYqF/9VXqreFmNlGqOeOV7l+N9D3jk9aRWsNB9ff/311NfXp7tr/Njff4ze9dt3sXIO67FXn7z2SWq6uUltDQAAAGQHVjiZ2E901j1n0UdZMLQGAABAvkDBAgAAAC4wrhSsd2Yt9cfixsxgjPrrvOSt8hp9sfrCOMX21vIyfQYAAABwjXGlYMWwibZHdGUqBk9636sYNlGYItLvKgZP26qhYAEAALhO2SrYaGsFVQZ7iNbWUMWSkFFDrQpS5+IwVVbIumrqXePjhR4KrmcVO62CKqbVU3W0iWQpAAAA4CZlq2B9azR97KqetgaVlTCr06VhtdyiSFnxhtW2TfPUMgAAAMBFYOQEAAAAuAAULAAAAOACULAAAACAC0DBAgAAAC4ABQsAAAC4ABQsAAAA4AJQsAAAAIALQMECAAAALgAFCwAAALgAFCwAAADgAuNMwUapvaJCD56up9ZoyvJAt4q2AwAAALjI+KvBtvQmfRTrzv4lMEAN/5FlvVQdbKCQRNYBAAAAXGQCNBFHKbK2jfy6k38f+Vt6KJZGwYpC3rhxI82bN09PP/nJT+jVV19NzEtatWqV2hoAAADIzvhTsHs7KKA3BweMmqoZeN1Yaw/KbkGaj1esWEF9fX16+uhHP0rnn39+Yl7SunXr1NYAAABAdsZhDTZAndI8HK2m+pUqTiwAAABQZMaZgvVRkxkbdp6f2raFKTKkrwAAAACKyvjvg630UvW2GMXUbOxAD1V7zfDsAAAAgDuMLwXb154chtMXoea6APmrxLCpmSJ9slAMnmrJW6VvAQAAALjG+FKw85qo8UClMQbW109d643mYt+aXv4jhk811B/qpCAULAAAAJcZd03EvjVqDKwWtihSHzWpsbHhpWgeBgAA4D7jvw8WAAAAGAOgYAEAAAAXgIIFAAAAXAAKFgAAAHABKFgAAADABaBgAQAAABeAggUAAABcAAq2hDh+/LieisXRo0fp1KlTaq44xONxevPNN9Wc+8jx/vjHP6q54vD73/9eTRWHF154QU0VB3lGJV+Lxeuvv17U4wlvvfUWvfHGG2rOfeSdKPZzKnkq34BiIcc7duyYmpsYQMGWEE8++aSeikUoFKIjR46oueKwePFi+u53v6vm3EeO99RTT6m54rB06VI1VRw+85nPqKni8Oijj+r5Wiw2bdpU1OMJzz33HO3YsUPNuc+uXbvo2WefVXPFoba2lr71rW+pOfeReyjXOZGYGAp2MKRixFZQu+6TuDR57bXX9FQshoaG9JJ6MZG4uoODaSLeu4Qcr5h5Kjz99NNqqjjs27dPTRWHl156Sc/XYvHb3/62qMcTpKYl11ksXnnllaK27AiSp7GYGQbFfeR4L774opqbGEwABRun0MowBQY00ga6qN/XTlG1BgAAAHCLCk0c9CpmzJhBBw8eVHPjBKm9riTq1OPEsrJd0kC03uqn2ECyQZpLNm7cqM9LE9Ef/vAHmj17tj5fDMz+kPPOO0//7zbSd3fBBRfQGWecoZa4j9TuPB4PXXTRRWqJu8jxvF4vvfOd71RL3OdXv/oV/dVf/ZWac5///d//pb/4i79Qc+5z+PBhGhgYoA984ANqibtIn/bLL79ctOMJ0s8sfbAXXnihWuIur776Kk2ePJnOOecctcR95N04//zzqaqqONFP5Hjf/va36cYbb1RLypsTJ07Q6tWracOGDWpJGkTBmkyfPl1NjSOibRq19KoZTettIa0tqmaysGjRIm3q1Klqrjg89NBDeioWa9eu1eLxuJorDvLItbW1qTn3keP96Ec/UnPFYcqUKWqqOLzvfe9TU8XhO9/5jp6vxeLzn/98UY8n/PKXv9TuvvtuNec+mzZt0p544gk1VxwqKiq0FStWqDn3kXvIClbNlT9cCNNuu+02NZceGDkBAAAALgAFCwAAALjA+FewVV6q3RsjYxRdnGJ7a8mbQ5fDWWedRWeffbaaA4VC+pgmTZqk5txHjnf66aerOVAIpM++mH2F8rwU83gTBcnTM888U825jxyvmPYeJYFqKtYZl32w2pDWVVerdQ3w5ECXVkttWrJHNjP79u3T+2GKycsvv6ynYhGLxbQTJ06oueLw+OOPa0NDQ2rOfaLRqPbaa6+pueLw05/+VE0Vh2L33b344ovak08+qebc59ChQ9rPf/5zNVcc3njjDW1wcFDNuY+8E0ePHlVzxUGe09/+9rdqzn16e3u1l156Sc2VP7n0wY5/K2JBLImn1VMPT7ZFNWqaZywGAAAARkIuVsQTow+2KkhhLkdIWQLKFQAAQDGAkRMAAADgAlCwAAAAgAtAwQIwGvraqWJJSFmpE0VbDZ/XerIsL1+i1G5eT2vU5te7osIlt6O2Y7h8LJO0xyxt3+Wg9IGCnTAkP5SBbv7s2z4oAQoVzf8+n4d8qGWqtfz9Qkd39VPXenHDKddTQZEFRl+/ntYTNahrHT3i5rP4H/14dwdfmHE9QzMjFFgZo0bz+ga81FHoQoQUWMS1aeIYXVTb0svTfoq4de3OY1qSf5d6X0CelMr3ZmyBgs0BdxSBpWZgfeASD2Jhjxlt7SCvBDzgj0YnbabAFusHpZFiKwtd20oqBHuqoea1Nfp0zVq1qStY81eSSy/1zAD59XHVcYpRFy23GtFVBamRIgW9j23RXiJf8rrcVrYxCiSuyTPfS9WLl5PPmDWub06Mtykc0V1EvbrfcAUfo3NmjPPQR03aEHl3Fb5VIOWYFnxrNGo8sLmg93A48L0ZP0DBZlQEyeSGIoi2RsifeOA6iVbysaS2IxbPUmpX2xWMhCJQH8qZfssHxUf+An8o+SgU3KpRbwtP6jUQ81p7qU3N6+sKhnEfDYUj09b8VXm8pfAvtZdT8QJ+CaJoknlZTGU7dvCzqSsED+d3oZ/TYoPvjTvfm9IEClZXBEPUVWeMkU1+kJOpsIogHYYy0hZE9BcssMWFR+9AmCJmqZVfqqal1vJ6lCIu1SalBpC4LkdTm29NU7I2VGjmeHXlZ2Nv4V9qz1IvRfRmUr6HC2K02ark+tqphvzuXWOKsi18zcdL4cQ1xR+LUf92S22Oaz8de9Pk8yjwzuynDutzwsdo2O5NthLs1ZcWFN8CopoMTd3x7gB1zLTU2kcNvjdufm9KDr6hCcanJ6cSRaL81HVp6XwaDYVqNX79cvI4lTOZjifL+TGwRhxyC+O63DqWeOzifcv+VaoNqat1+xp1D2H2Yxf+eMnrS1xXUejV2qzXY7vWAj+jisRz4jiGRMIq9j0sbl67yAT83rhNLp6coGB1iv8RKf4xcY3lf424h+5cI2M7zjg8njARrrGIIFxdjhTdUpIp9jFxjeV/jbiH7lxj0S2XS8FSejxeYwkCBcsU21JSKPYxcY3lf424h+5cY7Etl0vCUnocXmMpAgULAAApsCIvquVysY8nTIRrHFugYJliW0oKRbfOxDWW/TXiHrp0jUW2XB4LS+mJcI0liWYBRk6cYDxSQHCNhT0m7qE711h8y+WxsJSeCNdYTBAPFgAAAHABxIMFAAAAxggoWAAAAMAFoGABAAAAF4CCBQAAAFwAChYAAABwAShYAAAAwAWgYAEoEhL6zIz5mS58X4JBMwi2Sm743wUAuA4ULABFwrM0bIv12RNsoJAZM9NCdEs99ahpoja7T1cAQNkABQtAEfGt6WWVadJD9SsdtVMJ0m4JRt0WdTEoPQDAVaBgASgqPmqKJlUsbatP+N7luiu1+5rVNFPXlYgsAwAoP6BgASg285psTcXNvnbdoX20tYaS6rWWutanaRp29s9ySu3LZUXt2MZMqXE4Ldvqfb3J+Yx9xACAnICCBWAM8C3rYhVq0kw1rNCsTcO1oU4K6pFHkuhGUtOs/bMGPcFKqmhNxJzJSrMvm+IMU0OFVckDAEYDFCwAY4EEoA4lVayNui7qXOqou0q4r6CpWrl2O6CRpln6c9fWWGqnEtRa1ifTkOVYPUFLCDgr23qop6U38Zuw8xwAAHkBBQvAGOFZ2klddWomQfqm4fhj4WTNtS6g4mr6yG9tat6VuRbrmR+w1ZgjKU3FQhv1roFJFQCFAgoWgDHDQ8H11qbi9E3DQuyApWF4Wz1Vqn5Sa7My7Y0lLZKdfbVpmpZTqCt8MHMAJjJQsACMJVVeqlaTmYlTbK+azIW+9twUKgDAVaBgASh5POSdoyaFui4asvSvJpJySBHdZTVTaqNeWTdgrykDANwHChaAMsA706Iet8UopiZTcdR2W/yGo4rBGGq0ABQZKFgAygCnkVKNY1hOtNUc4+qo7ap+WXutFgBQDKBgASgHnMN61tYkDZg4WY2dfAvsnqLEIKqD2tBEDECRgYIFoEyQYAHp+1KNcbFNplvFeU22ca/U0kvhZbAPBqDYVGhiHaGYMWMGHTx4UM0BAAAAIB0nTpyg1atX04YNG9SSVFCDBQAAAFwAChYAAABwAShYAAAAwAWgYAEAAAAXgIIFAAAAXAAKFgAAAHABKFgAAADABaBgAQAAABeAggUAAABcAAoWAAAAcAEoWAAAAMAFbL6Ib775Ztq3b5+aAwAAAEAmrrvuOlqxYoWaS8WmYAEAAABQGNBEDAAAALgAFCwAAADgAlCwAAAAgAtAwQIAAAAFh+j/B1o5ePXWyBOIAAAAAElFTkSuQmCC"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "https://www.researchgate.net/publication/247907373_Stupid_Data_Miner_Tricks_Overfitting_the_SP_500"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/f23/Cole_Lecture_Notes/35_Plotting2/iris-flowers.db b/f23/Cole_Lecture_Notes/35_Plotting2/iris-flowers.db
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index 0000000000000000000000000000000000000000..4b3fca4ebaed7a7ba9f8a1dc97d521bf57636e24
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