{ "cells": [ { "cell_type": "markdown", "id": "0250c0bd", "metadata": {}, "source": [ " # Installing Necessary Libraries" ] }, { "cell_type": "code", "execution_count": 1, "id": "c3883ebb", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "id": "e09311aa", "metadata": {}, "outputs": [], "source": [ "from plotly.offline import iplot\n", "import plotly as py\n", "import plotly.tools as tls\n", "import chart_studio.plotly as cpy\n", "import chart_studio.tools as ctls" ] }, { "cell_type": "code", "execution_count": 3, "id": "9eb6849f", "metadata": {}, "outputs": [], "source": [ "import cufflinks as cf" ] }, { "cell_type": "markdown", "id": "f01ed6ac", "metadata": {}, "source": [ "## Using `cufflinks` and `iplot()` to create different plots" ] }, { "cell_type": "markdown", "id": "500308a7", "metadata": {}, "source": [ "- line\n", "- scatter\n", "- bar\n", "- box\n", "- spread\n", "- histogram\n", "- bubble\n", "- surface\n", "- heatmap " ] }, { "cell_type": "markdown", "id": "1e7c8062", "metadata": {}, "source": [ "### Knowing the version of the packages" ] }, { "cell_type": "code", "execution_count": 1, "id": "3ead8b1d", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'py' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'py' is not defined" ] } ], "source": [ "print(py.__version__)" ] }, { "cell_type": "code", "execution_count": 10, "id": "9a9c8481", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.17.3\n" ] } ], "source": [ "print(cf.__version__)" ] }, { "cell_type": "markdown", "id": "9ff2415b", "metadata": {}, "source": [ "### Embedding Online Plots to the Jupyter Notebook\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "795a2633", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ctls.embed('https://plotly.com/~cufflinks/8')" ] }, { "cell_type": "markdown", "id": "b5b98629", "metadata": {}, "source": [ "### Using Plotly-Cufflinks offline" ] }, { "cell_type": "code", "execution_count": 4, "id": "4f6e14c5", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "py.offline.init_notebook_mode(connected=True)" ] }, { "cell_type": "code", "execution_count": 5, "id": "37d66c47", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cf.go_offline()" ] }, { "cell_type": "markdown", "id": "7f2be9e2", "metadata": {}, "source": [ "### Connecting Jupyter Notebook with Plotly Account" ] }, { "cell_type": "code", "execution_count": 11, "id": "6d274676", "metadata": {}, "outputs": [], "source": [ "import chart_studio\n", "chart_studio.tools.set_credentials_file(username='PriyankaJha', api_key='EgKBYXoVIi8Aw32QlKrf')" ] }, { "cell_type": "markdown", "id": "9e6d201d", "metadata": {}, "source": [ "## `lineplot`" ] }, { "cell_type": "code", "execution_count": 6, "id": "a41ac988", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ABC
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" ], "text/plain": [ " A B C\n", "0 -0.405230 0.070950 -2.137773\n", "1 0.111522 1.920144 -2.159755\n", "2 -0.626906 3.780232 -2.015929\n", "3 0.716704 3.244089 -1.326465\n", "4 0.211570 2.747247 -1.485680" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#generating some pseudo data for analysis\n", "df = pd.DataFrame(np.random.randn(100, 3), columns=['A', 'B', 'C'])\n", "df['A'] = df['A'].cumsum()\n", "df['B'] = df['B'].cumsum()\n", "df['C'] = df['C'].cumsum()\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 7, "id": "45c57b86", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['ggplot', 'pearl', 'solar', 'space', 'white', 'polar', 'henanigans']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#different themes for plots in plotly\n", "cf.getThemes()" ] }, { "cell_type": "code", "execution_count": 12, "id": "e14e647e", "metadata": {}, "outputs": [ { "data": { 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311female35.01053.1000SFirstwomanFalseCSouthamptonyesFalse
403male35.0008.0500SThirdmanTrueNaNSouthamptonnoTrue
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" ], "text/plain": [ " survived pclass sex age sibsp parch fare embarked class \\\n", "0 0 3 male 22.0 1 0 7.2500 S Third \n", "1 1 1 female 38.0 1 0 71.2833 C First \n", "2 1 3 female 26.0 0 0 7.9250 S Third \n", "3 1 1 female 35.0 1 0 53.1000 S First \n", "4 0 3 male 35.0 0 0 8.0500 S Third \n", "\n", " who adult_male deck embark_town alive alone \n", "0 man True NaN Southampton no False \n", "1 woman False C Cherbourg yes False \n", "2 woman False NaN Southampton yes True \n", "3 woman False C Southampton yes False \n", "4 man True NaN Southampton no True " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic = sns.load_dataset('titanic')\n", "titanic.head()" ] }, { "cell_type": "code", "execution_count": 12, "id": "8b6bbde2", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { 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ABC
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" ], "text/plain": [ " A B C\n", "0 15.161858 15.444999 15.744817\n", "1 16.349772 15.269878 16.264184\n", "2 15.119788 13.444587 15.544242\n", "3 15.642109 11.379189 14.308738\n", "4 16.190989 12.725358 13.952934" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#generating another set of dataset with only positive values\n", "df2 = pd.DataFrame(np.random.randn(150, 3), columns=['A', 'B', 'C'])\n", "df2['A'] = df2['A'].cumsum() + 15\n", "df2['B'] = df2['B'].cumsum() + 15\n", "df2['C'] = df2['C'].cumsum() + 15\n", "df2.head()" ] }, { "cell_type": "code", "execution_count": 18, "id": "836ada80", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plotly.com", "plotlyServerURL": "https://plotly.com", "showLink": true }, "data": [ { "fill": "tonexty", "fillcolor": "rgba(255, 153, 51, 0.3)", "line": { "color": "rgba(255, 153, 51, 1.0)", "dash": "solid", "shape": "linear", "width": 1.3 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