615 lines
114 KiB
Plaintext
615 lines
114 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Histogram"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.style.use('default')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Iris Data\n",
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"> The data set is stored in ```sklearn``` as a dictionary. \n",
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"> "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn import datasets\n",
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"iris = datasets.load_iris()\n",
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"iris.keys()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The feature names are: \n",
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" ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
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"---------------------\n",
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"The head of feature data: \n",
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" [[5.1 3.5 1.4 0.2]\n",
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" [4.9 3. 1.4 0.2]\n",
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" [4.7 3.2 1.3 0.2]\n",
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" [4.6 3.1 1.5 0.2]\n",
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" [5. 3.6 1.4 0.2]]\n",
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"---------------------\n",
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"The head of target: \n",
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" [0 0 0 0 0]\n"
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]
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}
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],
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"source": [
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"# What are the items in iris?\n",
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"print('The feature names are: \\n', iris['feature_names'])\n",
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"print('---------------------')\n",
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"print('The head of feature data: \\n', iris['data'][:5,])\n",
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"print('---------------------')\n",
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"print('The head of target: \\n', iris['target'][:5])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Combine Feature and Target and Convert to DataFrame"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(150, 4)\n",
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"---------------------\n",
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"(150, 1)\n",
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"---------------------\n",
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"[[6.7 3. 5.2 2.3 2. ]\n",
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" [6.3 2.5 5. 1.9 2. ]\n",
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" [6.5 3. 5.2 2. 2. ]\n",
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" [6.2 3.4 5.4 2.3 2. ]\n",
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" [5.9 3. 5.1 1.8 2. ]]\n"
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]
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}
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],
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"source": [
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"# np.c_[iris['data'],iris['target']] combines feature and target more efficiently\n",
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"\n",
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"print( iris['data'].shape )\n",
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"print('---------------------')\n",
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"\n",
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"print( iris['target'].reshape((150,1)).shape )\n",
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"print('---------------------')\n",
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"\n",
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"x = np.hstack([iris['data'],iris['target'].reshape((150,1))])\n",
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"print(x[-5:])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>sepal length (cm)</th>\n",
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" <th>sepal width (cm)</th>\n",
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" <th>petal length (cm)</th>\n",
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" <th>petal width (cm)</th>\n",
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" <th>species</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>145</th>\n",
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" <td>6.7</td>\n",
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" <td>3.0</td>\n",
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" <td>5.2</td>\n",
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" <td>2.3</td>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>146</th>\n",
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" <td>6.3</td>\n",
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" <td>2.5</td>\n",
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" <td>5.0</td>\n",
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" <td>1.9</td>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>147</th>\n",
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" <td>6.5</td>\n",
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" <td>3.0</td>\n",
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" <td>5.2</td>\n",
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" <td>2.0</td>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>148</th>\n",
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" <td>6.2</td>\n",
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" <td>3.4</td>\n",
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" <td>5.4</td>\n",
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" <td>2.3</td>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>149</th>\n",
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" <td>5.9</td>\n",
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" <td>3.0</td>\n",
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" <td>5.1</td>\n",
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" <td>1.8</td>\n",
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" <td>2.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n",
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"145 6.7 3.0 5.2 2.3 \n",
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"146 6.3 2.5 5.0 1.9 \n",
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"147 6.5 3.0 5.2 2.0 \n",
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"148 6.2 3.4 5.4 2.3 \n",
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"149 5.9 3.0 5.1 1.8 \n",
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"\n",
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" species \n",
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"145 2.0 \n",
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"146 2.0 \n",
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"147 2.0 \n",
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"148 2.0 \n",
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"149 2.0 "
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"iris = pd.DataFrame(data=x, columns=iris['feature_names']+['species'])\n",
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"iris.tail()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Special functions in Numpy/ Pandas\n",
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"> ```np.where(condition,x,y)``` returns x when conditions True & y when conditions False. \n",
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"> 'a' can be replaced by 'b' using ```replace(a,b)```. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"iris.species = np.where(iris.species == 0.0, 'setosa', \n",
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" np.where(iris.species == 1.0,'versicolor','virginica'))\n",
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"\n",
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"iris.columns = iris.columns.str.replace(' ','') #Remove space bar"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000021030A85F60>,\n",
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" <matplotlib.axes._subplots.AxesSubplot object at 0x0000021030ACD0B8>],\n",
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" [<matplotlib.axes._subplots.AxesSubplot object at 0x00000210308E8668>,\n",
|
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" <matplotlib.axes._subplots.AxesSubplot object at 0x0000021030919C18>]],\n",
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" dtype=object)"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Pandas plot\n",
|
|
"iris.hist()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## GDP Data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"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>Country</th>\n",
|
|
" <th>Code</th>\n",
|
|
" <th>Population</th>\n",
|
|
" <th>GDP per Capita</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Afghanistan</td>\n",
|
|
" <td>AFG</td>\n",
|
|
" <td>32526562.0</td>\n",
|
|
" <td>594.323081</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Albania</td>\n",
|
|
" <td>ALB</td>\n",
|
|
" <td>2889167.0</td>\n",
|
|
" <td>3945.217582</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>Algeria</td>\n",
|
|
" <td>ALG</td>\n",
|
|
" <td>39666519.0</td>\n",
|
|
" <td>4206.031232</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>American Samoa*</td>\n",
|
|
" <td>ASA</td>\n",
|
|
" <td>55538.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Andorra</td>\n",
|
|
" <td>AND</td>\n",
|
|
" <td>70473.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Country Code Population GDP per Capita\n",
|
|
"0 Afghanistan AFG 32526562.0 594.323081\n",
|
|
"1 Albania ALB 2889167.0 3945.217582\n",
|
|
"2 Algeria ALG 39666519.0 4206.031232\n",
|
|
"3 American Samoa* ASA 55538.0 NaN\n",
|
|
"4 Andorra AND 70473.0 NaN"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df = pd.read_csv(\"countryGDP.csv\")\n",
|
|
"df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x21030b51be0>"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"df1 = df['GDP per Capita'].dropna() \n",
|
|
"df1.hist()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Set Bins\n",
|
|
"> One way is to create a NumPy array with chosen cutting points. \n",
|
|
"> Another way is to specify number of bins. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[ 277.06830917 10277.06830917 20277.06830917 30277.06830917\n",
|
|
" 40277.06830917 50277.06830917 60277.06830917 70277.06830917\n",
|
|
" 80277.06830917 90277.06830917 100277.06830917]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"b = np.arange(data1.min(), data1.max(), 10000) # Fixed bin size\n",
|
|
"print(b)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0, 0.5, 'Frequency')"
|
|
]
|
|
},
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Matplotlib plots\n",
|
|
"\n",
|
|
"plt.hist(df1, bins=b, alpha=0.5, color='blue')\n",
|
|
"plt.title(\"Countrys' GDP Level\")\n",
|
|
"plt.xlabel(\"GDP in Dollars\")\n",
|
|
"plt.ylabel(\"Frequency\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
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{
|
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"data": {
|
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"text/plain": [
|
|
"(array([89., 28., 14., 11., 5., 5., 1., 5., 7., 2., 2., 2., 1.,\n",
|
|
" 0., 2., 1., 0., 0., 0., 1.]),\n",
|
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" array([ 277.06830917, 5335.71330211, 10394.35829506, 15453.003288 ,\n",
|
|
" 20511.64828095, 25570.29327389, 30628.93826683, 35687.58325978,\n",
|
|
" 40746.22825272, 45804.87324566, 50863.51823861, 55922.16323155,\n",
|
|
" 60980.80822449, 66039.45321744, 71098.09821038, 76156.74320332,\n",
|
|
" 81215.38819627, 86274.03318921, 91332.67818215, 96391.3231751 ,\n",
|
|
" 101449.96816804]),\n",
|
|
" <a list of 1 Patch objects>)"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.hist(df1, bins=20, color='blue', histtype='step')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0.5, 1.0, \"Population Histogram ('000')\")"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"data1 = df['GDP per Capita'].dropna() \n",
|
|
"data2 = df['Population'].dropna()/1000 \n",
|
|
"\n",
|
|
"plt.subplot(1,2,1)\n",
|
|
"plt.hist(data1, color='red')\n",
|
|
"plt.xlabel('GDP')\n",
|
|
"plt.title('GDP Histogram')\n",
|
|
"\n",
|
|
"plt.subplot(1,2,2)\n",
|
|
"plt.hist(data2, color='blue')\n",
|
|
"plt.xlabel('Population')\n",
|
|
"plt.title('''Population Histogram ('000')''')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Activity 1\n",
|
|
"> The code starts with \n",
|
|
"```python\n",
|
|
"fig, axes = plt.subplots(1,2)\n",
|
|
"```\n",
|
|
"> Complete the plots where 1st box is GDP, 2nd box is Population. \n",
|
|
"> Bin size is fixed at 20. \n",
|
|
"> Set xlim for GDP to [0,75000], and for Population to [0,200000]. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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": "3.11.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|