Module_2_Data_Visualization_Seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 2 Data Visualization with Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "mtcars = pd.read_csv('data/mtcars.csv',usecols=['car_names', 'mpg','hp','cyl','am'],index_col='car_names')\n",
    "#mtcars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "#sns.set_style(\"whitegrid\")\n",
    "#sns.set_style(\"ticks\", {\"xtick.major.size\": 8, \"ytick.major.size\": 8})\n",
    "\n",
    "plt.scatter(mtcars.mpg, mtcars.hp)\n",
    "plt.xlabel('mpg')\n",
    "plt.ylabel('hp')\n",
    "plt.plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Statistical Relationships Between Quantiative Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.scatterplot(x=\"mpg\", y=\"hp\", data=mtcars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"mpg\", y=\"hp\", data=mtcars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.scatterplot(x=\"mpg\", y=\"hp\", size=\"cyl\",data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"mpg\", y=\"hp\", size=\"cyl\",sizes=(20, 200), data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.scatterplot(x=\"mpg\", y=\"hp\", hue=\"cyl\", size=\"cyl\",sizes=(20, 200),data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = sns.cubehelix_palette(dark=.5, light=0.9, as_cmap=True)\n",
    "sns.relplot(x=\"mpg\", y=\"hp\", hue=\"cyl\", size=\"cyl\",sizes=(20, 200),palette=cmap,data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"mpg\", y=\"hp\", hue=\"cyl\", size=\"cyl\",sizes=(20, 200),style='am',\n",
    "                palette='Set2',data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = sns.cubehelix_palette(dark=.5, light=0.9, as_cmap=True)\n",
    "sns.scatterplot(x=\"mpg\", y=\"hp\", hue=\"cyl\", size=\"cyl\",sizes=(20, 200),style='am',data=mtcars);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ex Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "college = pd.read_csv('data/college.csv')\n",
    "college"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x='tuition', y='sat_avg', data=college)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x='tuition', y='sat_avg', hue='control',alpha=0.5, data=college)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x='tuition', y='sat_avg', hue='control',size='undergrads',sizes=(20,200), alpha=0.5, data=college)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ex Scatter Plot (Optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "ax = sns.scatterplot(x=iris.sepal_length, y=iris.sepal_width,hue=iris.species, style=iris.species)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", jitter=False,data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", hue='am',jitter=False,data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", jitter=False,data=mtcars.query('am==1'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", order=[8,6,4],jitter=False,data=mtcars.query('am==1'));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Categorical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", kind='bar',data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", orient='h',kind='bar',data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", kind='bar',hue='am',data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(y=\"cyl\", x=\"mpg\", orient='h',kind='bar',hue='am',data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"region\",kind='count',data=college);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset(\"titanic\")\n",
    "sns.catplot(x=\"sex\", y=\"survived\", hue=\"class\", kind=\"bar\", data=titanic);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", kind='count',hue='am',data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(y=\"cyl\", kind='count',hue='am',data=mtcars);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y='mpg', kind='bar',col='am',data=mtcars);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ex: Bar Plot withh Hue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"region\",kind='count',data=college);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 431x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"region\",kind='count', hue='control',data=college);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Statistical Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.distplot(mtcars.mpg);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.distplot(mtcars.mpg,kde=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ex: Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(college.undergrads,kde=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", kind='box',data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", kind='box',hue='am',data=mtcars);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ex: Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=college, x=\"control\", y=\"tuition\", kind=\"box\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "sns.catplot(data=iris, x=\"species\", y=\"sepal_length\", kind=\"box\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Violin Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", kind='violin',data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x=\"cyl\", y=\"mpg\", kind='violin',hue='am',data=mtcars);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pair Plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(x=\"mpg\", y=\"hp\", data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(mtcars);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ex Pair Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "sns.pairplot(iris);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = mtcars.corr()\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = iris.corr()\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Statistical Relationship with Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.regplot(x=\"mpg\", y=\"hp\", data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lmplot(x=\"mpg\", y=\"hp\", data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lmplot(x=\"mpg\", y=\"hp\", ci=None, data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lmplot(x=\"mpg\", y=\"hp\", hue=\"am\", data=mtcars);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lmplot(x=\"mpg\", y=\"hp\", col=\"am\", data=mtcars);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fmri = sns.load_dataset(\"fmri\")\n",
    "sns.relplot(x=\"timepoint\", y=\"signal\", kind=\"line\", data=fmri);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"timepoint\", y=\"signal\", ci=None, kind=\"line\", data=fmri);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"timepoint\", y=\"signal\", hue=\"event\", style=\"event\",kind=\"line\", data=fmri);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.relplot(x=\"timepoint\", y=\"signal\",col=\"region\", row=\"event\", kind=\"line\", data=fmri);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}