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yashppawar/bubble.ipynb

Created December 21, 2021 10:44
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bubble.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/yashppawar/56eea73fb2da44dec897341e1460ad99/bubble.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "_eICGCcZCm4j"
},
"outputs": [],
"source": [
"# Import necessary Libraries\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import plotly.express as px"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "m9W6JlPKCm4Z"
},
"source": [
"# Bubble Chart\n",
"\n",
"exploring the uses of bubble chart/scatterplot\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tYxl9H0TCm4l"
},
"source": [
"Using randomly created data, and making one scatter plot"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 143
},
"cellView": "form",
"id": "cLN_M5TzCm4m",
"outputId": "8cb1b606-1cc7-45a6-ef71-ac7f532b4807"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
" <div id=\"df-e475bc48-1242-468d-b38d-31fc05696e2e\">\n",
" <div class=\"colab-df-container\">\n",
" <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",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e475bc48-1242-468d-b38d-31fc05696e2e')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-e475bc48-1242-468d-b38d-31fc05696e2e button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-e475bc48-1242-468d-b38d-31fc05696e2e');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
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" "
],
"text/plain": [
" x y\n",
"0 9 5\n",
"1 11 15\n",
"2 19 9"
]
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"# @markdown Enter the no of samples to be in the dataset (randomly generated)\n",
"size = 90 # @param { type: \"slider\", min: 10, max: 500, step: 10 }\n",
"x = np.random.randint(20, size=size)\n",
"y = np.random.randint(20, size=size)\n",
"\n",
"data = pd.DataFrame({\"x\": x, \"y\": y})\n",
"data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "AUaKRg4GCm4o",
"outputId": "83675547-9a8c-4bb2-e89a-dee5df476e8c"
},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"sns.scatterplot(x=data.x, y=data.y, alpha=0.7)\n",
"plt.title(\"Scatter Plot for Random vs Random\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3seQ9r-UCm4q"
},
"source": [
"### **Citation**\n",
"fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Date Retrieved] from [Heart Failure Prediction on kaggle](https://www.kaggle.com/fedesoriano/heart-failure-prediction).\n",
"# **Attribute Information**\n",
"\n",
"**Age**: age of the patient [years]\n",
"\n",
"**Sex**: sex of the patient [M: Male, F: Female]\n",
"\n",
"**ChestPainType**: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]\n",
"\n",
"**RestingBP**: resting blood pressure [mm Hg]\n",
"\n",
"**Cholesterol**: serum cholesterol [mm/dl]\n",
"\n",
"**FastingBS**: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]\n",
"\n",
"**RestingECG**: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]\n",
"\n",
"**MaxHR**: maximum heart rate achieved [Numeric value between 60 and 202]\n",
"\n",
"**ExerciseAngina**: exercise-induced angina [Y: Yes, N: No]\n",
"\n",
"**Oldpeak**: oldpeak = ST [Numeric value measured in depression]\n",
"\n",
"**ST_Slope**: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]\n",
"\n",
"**HeartDisease**: output class [1: heart disease, 0: Normal]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 224
},
"id": "dBR05kM1Cm4s",
"outputId": "2bbe6c38-6d7d-4e75-ae6f-74ed4f192311"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"shape (100, 12)\n"
]
},
{
"output_type": "execute_result",
"data": {
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"\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" <th>ChestPainType</th>\n",
" <th>RestingBP</th>\n",
" <th>Cholesterol</th>\n",
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" <td>289</td>\n",
" <td>0</td>\n",
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" <td>N</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>49</td>\n",
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" <td>160</td>\n",
" <td>180</td>\n",
" <td>0</td>\n",
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" <td>ASY</td>\n",
" <td>138</td>\n",
" <td>214</td>\n",
" <td>0</td>\n",
" <td>Normal</td>\n",
" <td>108</td>\n",
" <td>Y</td>\n",
" <td>1.5</td>\n",
" <td>Flat</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>54</td>\n",
" <td>M</td>\n",
" <td>NAP</td>\n",
" <td>150</td>\n",
" <td>195</td>\n",
" <td>0</td>\n",
" <td>Normal</td>\n",
" <td>122</td>\n",
" <td>N</td>\n",
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" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-6afca3d9-9717-4202-8993-d7797ba860a9 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-6afca3d9-9717-4202-8993-d7797ba860a9');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
],
"text/plain": [
" Age Sex ChestPainType ... Oldpeak ST_Slope HeartDisease\n",
"0 40 M ATA ... 0.0 Up 0\n",
"1 49 F NAP ... 1.0 Flat 1\n",
"2 37 M ATA ... 0.0 Up 0\n",
"3 48 F ASY ... 1.5 Flat 1\n",
"4 54 M NAP ... 0.0 Up 0\n",
"\n",
"[5 rows x 12 columns]"
]
},
"metadata": {},
"execution_count": 9
}
],
"source": [
"df = pd.read_csv(\"./heart.csv\")\n",
"\n",
"# preprocess\n",
"sample = df[df.Cholesterol != 0]\n",
"\n",
"# @markdown Enter the no. of samples \n",
"n_samples = 100# @param { type: \"integer\" }\n",
"sample = sample[:n_samples]\n",
"\n",
"print(\"shape\", sample.shape)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"source": [
"---\n",
"A Bubble Chart is a ***multi-variable*** graph that is a cross between a Scatterplot and a Proportional Area Chart.\n",
"---"
],
"metadata": {
"id": "WkU2vkneRJ84"
}
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 567
},
"cellView": "form",
"id": "_so9UUT0Cm4t",
"outputId": "b7ff5477-ea88-4254-f12b-e0379d9e079b"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x648 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"plt.figure(figsize=(15, 9))\n",
"\n",
"# @markdown Choose the Parameters for the Bubble Chart\n",
"\n",
"x = \"MaxHR\" # @param [\"MaxHR\", \"RestingBP\"]\n",
"y = \"Cholesterol\" # @param [\"Cholesterol\", \"RestingBP\"]\n",
"size = \"Age\" # @param [\"null\", \"Age\"]\n",
"if size == \"null\": size = None\n",
"\n",
"hue = \"HeartDisease\" # @param [\"null\", \"Sex\", \"HeartDisease\"]\n",
"if hue == \"null\": hue = None\n",
"\n",
"sns.scatterplot(x=x, y=y, size=size, hue=hue, alpha=0.3, sizes=(40, 800), data=sample)\n",
"\n",
"title = f\"plot of {x} vs {y}\"\n",
"if size: title += f\" vs {size}\"\n",
"if hue: title += f\" vs {hue}\"\n",
"\n",
"plt.title(title);"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"cellView": "form",
"id": "4zX-B6nbCm4v",
"outputId": "59c1de7a-a5c2-429d-8df0-4469902a98bb"
},
"outputs": [
{
"output_type": "display_data",
"data": {
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{\"text\": \"MaxHR\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Cholesterol\"}}},\n",
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],
"source": [
"# @markdown Choose the Parameters for the Bubble Chart\n",
"\n",
"x = \"MaxHR\" # @param [\"MaxHR\", \"RestingBP\"]\n",
"y = \"Cholesterol\" # @param [\"Cholesterol\", \"RestingBP\"]\n",
"size = \"Age\" # @param [\"null\", \"Age\"]\n",
"if size == \"null\": size = None\n",
"\n",
"hue = \"HeartDisease\" # @param [\"null\", \"Sex\", \"HeartDisease\", \"RestingBP\"]\n",
"if hue == \"null\": hue = None\n",
"\n",
"title = f\"Bubble Chart of {x} vs {y}\"\n",
"if size: title += f\" vs {size}\"\n",
"if hue: title += f\" vs {hue}\"\n",
"\n",
"px.scatter(sample, x, y, color=hue, size=size, color_continuous_scale=px.colors.sequential.Viridis, title=title)"
]
},
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{\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Summarizing Bubble Chart\"}},\n",
" {\"responsive\": true}\n",
" ).then(function(){\n",
" \n",
"var gd = document.getElementById('eebe7b9c-53d4-4753-af49-15cf17a54702');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" })\n",
" };\n",
" \n",
" </script>\n",
" </div>\n",
"</body>\n",
"</html>"
]
},
"metadata": {}
}
],
"source": [
"import plotly.graph_objects as go\n",
"\n",
"fig = go.Figure(data=[go.Scatter(\n",
" x = [1, 3.2, 5.4, 7.6, 9.8, 12.5],\n",
" y = [1, 3.2, 5.4, 7.6, 9.8, 12.5],\n",
" mode = 'markers',\n",
" marker = dict(\n",
" color = [120, 125, 130, 135, 140, 145],\n",
" size = [15, 30, 55, 70, 90, 110],\n",
" showscale = True\n",
" )\n",
")])\n",
"\n",
"fig.update_layout(title=\"Summarizing Bubble Chart\")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bBwdE6aOCm4x"
},
"outputs": [],
"source": [
""
]
}
],
"metadata": {
"interpreter": {
"hash": "1675d28b5f8ce0976c4040dac551d608e4b4b347dab8a96610fae2b5962ed58f"
},
"kernelspec": {
"display_name": "Python 3.9.1 64-bit ('base': conda)",
"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": "3.9.1"
},
"orig_nbformat": 4,
"colab": {
"name": "bubble.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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