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uysalserkan/Assignment_1.ipynb

Created June 3, 2021 10:40
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P.I. Works Assignment 1
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Assignment_1.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "outstanding-happening",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' Python Libraries\\n 1- pandas\\n 2- matplotlib\\n 3- seaborn\\n 4- numpy\\n 5- plotly\\n'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\" Python Libraries\n",
" 1- pandas\n",
" 2- matplotlib\n",
" 3- seaborn\n",
" 4- numpy\n",
" 5- plotly\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "coral-valley",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import plotly.graph_objects as go\n",
"import plotly.offline as pyo"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "tropical-worker",
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_excel('DailyActivities.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "psychological-spanish",
"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>Area of Interest</th>\n",
" <th>Charles</th>\n",
" <th>Henry</th>\n",
" <th>Susan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Work</td>\n",
" <td>8.5</td>\n",
" <td>9.5</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Family</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Homeworks</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Individual</td>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Socializing</td>\n",
" <td>1.5</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Spare Time</td>\n",
" <td>3.0</td>\n",
" <td>2.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Sleep</td>\n",
" <td>6.5</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Area of Interest Charles Henry Susan\n",
"0 Work 8.5 9.5 7.0\n",
"1 Family 0.5 1.0 1.5\n",
"2 Homeworks 3.0 2.0 1.0\n",
"3 Individual 1.0 1.5 2.5\n",
"4 Socializing 1.5 0.5 2.0\n",
"5 Spare Time 3.0 2.5 2.0\n",
"6 Sleep 6.5 7.0 8.0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[:]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "focused-portland",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 7 entries, 0 to 6\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Area of Interest 7 non-null object \n",
" 1 Charles 7 non-null float64\n",
" 2 Henry 7 non-null float64\n",
" 3 Susan 7 non-null float64\n",
"dtypes: float64(3), object(1)\n",
"memory usage: 352.0+ bytes\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "imperial-assurance",
"metadata": {},
"outputs": [],
"source": [
"area_of_interes = data.drop('Area of Interest', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "champion-qatar",
"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>Charles</th>\n",
" <th>Henry</th>\n",
" <th>Susan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8.5</td>\n",
" <td>9.5</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.5</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3.0</td>\n",
" <td>2.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6.5</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Charles Henry Susan\n",
"0 8.5 9.5 7.0\n",
"1 0.5 1.0 1.5\n",
"2 3.0 2.0 1.0\n",
"3 1.0 1.5 2.5\n",
"4 1.5 0.5 2.0\n",
"5 3.0 2.5 2.0\n",
"6 6.5 7.0 8.0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"area_of_interes"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "double-necklace",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Work\n",
"1 Family\n",
"2 Homeworks\n",
"3 Individual\n",
"4 Socializing\n",
"5 Spare Time\n",
"6 Sleep\n",
"Name: Area of Interest, dtype: object"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Area of Interest']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "apart-framework",
"metadata": {},
"outputs": [],
"source": [
"r = [0,1,2,3,4,5,6]\n",
"y= np.arange(0,10, 0.5)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "appointed-disaster",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1080x1080 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15, 15))\n",
"plt.bar(x=r, height=data['Charles'], color='r', edgecolor='white', alpha=0.25, label='Charles')\n",
"plt.bar(x=r, height=data['Henry'], color='g', edgecolor='white', alpha=0.25, label='Henry')\n",
"plt.bar(x=r, height=data['Susan'], color='b', edgecolor='white', alpha=0.25, label='Susan')\n",
"\n",
"\n",
"plt.xticks([val+0.25 for val in r], data['Area of Interest'], rotation=25)\n",
"plt.yticks(y)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "helpful-emission",
"metadata": {},
"source": [
"### Radar Chart"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "rational-discovery",
"metadata": {},
"outputs": [],
"source": [
"cats = data['Area of Interest']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "threatened-drill",
"metadata": {},
"outputs": [],
"source": [
"cats = [*cats, cats[0]]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "frozen-texture",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Work',\n",
" 'Family',\n",
" 'Homeworks',\n",
" 'Individual',\n",
" 'Socializing',\n",
" 'Spare Time',\n",
" 'Sleep',\n",
" 'Work']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cats"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "destroyed-county",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 7 entries, 0 to 6\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Area of Interest 7 non-null object \n",
" 1 Charles 7 non-null float64\n",
" 2 Henry 7 non-null float64\n",
" 3 Susan 7 non-null float64\n",
"dtypes: float64(3), object(1)\n",
"memory usage: 352.0+ bytes\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "warming-campaign",
"metadata": {
"scrolled": true
},
"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>Charles</th>\n",
" <th>Henry</th>\n",
" <th>Susan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8.5</td>\n",
" <td>9.5</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.5</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3.0</td>\n",
" <td>2.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6.5</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Charles Henry Susan\n",
"0 8.5 9.5 7.0\n",
"1 0.5 1.0 1.5\n",
"2 3.0 2.0 1.0\n",
"3 1.0 1.5 2.5\n",
"4 1.5 0.5 2.0\n",
"5 3.0 2.5 2.0\n",
"6 6.5 7.0 8.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"area_of_interes"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "lesser-receptor",
"metadata": {},
"outputs": [],
"source": [
"area_of_interes.loc[len(area_of_interes)] = area_of_interes.loc[0]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "extensive-cleaner",
"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>Charles</th>\n",
" <th>Henry</th>\n",
" <th>Susan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8.5</td>\n",
" <td>9.5</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>1.5</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.5</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3.0</td>\n",
" <td>2.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6.5</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8.5</td>\n",
" <td>9.5</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Charles Henry Susan\n",
"0 8.5 9.5 7.0\n",
"1 0.5 1.0 1.5\n",
"2 3.0 2.0 1.0\n",
"3 1.0 1.5 2.5\n",
"4 1.5 0.5 2.0\n",
"5 3.0 2.5 2.0\n",
"6 6.5 7.0 8.0\n",
"7 8.5 9.5 7.0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"area_of_interes"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "prostate-diameter",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'temp-plot.html'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fig = go.Figure(\n",
" data=[\n",
" go.Scatterpolar(r=area_of_interes['Charles'], theta=cats, fill='toself', name='Charles'),\n",
" go.Scatterpolar(r=area_of_interes['Henry'], theta=cats, fill='toself', name='Henry'),\n",
" go.Scatterpolar(r=area_of_interes['Susan'], theta=cats, fill='toself', name='Susan')\n",
" ],\n",
" layout=go.Layout(\n",
" title=go.layout.Title(text='Radar Map'),\n",
" polar={'radialaxis': {'visible': True}},\n",
" showlegend=True\n",
" )\n",
")\n",
"\n",
"pyo.plot(fig)"
]
},
{
"cell_type": "markdown",
"id": "powered-christmas",
"metadata": {},
"source": [
"<img src=\"https://i.ibb.co/BjDQTsT/Screenshot-from-2021-06-03-13-33-52.png\">"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "suffering-image",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.5 64-bit",
"language": "python",
"name": "python38564bit9b13fc8a8c5449f8ad604cc2782fb3a5"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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