{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Title :\n", "\n", "## Description :\n", "The aim of this exercise is to **plot** TV Ads vs Sales based on the Advertisement dataset which should look similar to the graph given below.\n", "\n", "\n", "\n", "## Data Description:\n", "\n", "## Instructions:\n", "- Read the Advertisement data and view the top rows of the dataframe to get an understanding of the data and the columns.\n", "- Select the first 7 observations and the columns `TV` and `Sales` to make a new data frame.\n", "- Create a scatter plot of the new data frame `TV` budget vs `Sales`.\n", "\n", "## Hints: \n", "\n", "pd.read_csv(filename)\n", "Returns a pandas dataframe containing the data and labels from the file data\n", "\n", "df.iloc[]\n", "Returns a subset of the dataframe that is contained in the row range passed as the argument\n", "\n", "np.linspace()\n", "Returns evenly spaced numbers over a specified interval\n", "\n", "df.head()\n", "Returns the first 5 rows of the dataframe with the column names\n", "\n", "plt.scatter()\n", "A scatter plot of y vs. x with varying marker size and/or color\n", "\n", "plt.xlabel()\n", "This is used to specify the text to be displayed as the label for the x-axis\n", "\n", "plt.ylabel()\n", "This is used to specify the text to be displayed as the label for the y-axis\n", "\n", "\n", "**Note:** This exercise is auto-graded and you can try multiple attempts." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import necessary libraries\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading the Advertisement dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# \"Advertising.csv\" containts the data set used in this exercise\n", "data_filename = 'Advertising.csv'\n", "\n", "# Read the file \"Advertising.csv\" file using the pandas library\n", "df = pd.read_csv(\"Advertising.csv\")\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TVRadioNewspaperSales
count200.000000200.000000200.000000200.000000
mean147.04250023.26400030.55400014.022500
std85.85423614.84680921.7786215.217457
min0.7000000.0000000.3000001.600000
25%74.3750009.97500012.75000010.375000
50%149.75000022.90000025.75000012.900000
75%218.82500036.52500045.10000017.400000
max296.40000049.600000114.00000027.000000
\n", "
" ], "text/plain": [ " TV Radio Newspaper Sales\n", "count 200.000000 200.000000 200.000000 200.000000\n", "mean 147.042500 23.264000 30.554000 14.022500\n", "std 85.854236 14.846809 21.778621 5.217457\n", "min 0.700000 0.000000 0.300000 1.600000\n", "25% 74.375000 9.975000 12.750000 10.375000\n", "50% 149.750000 22.900000 25.750000 12.900000\n", "75% 218.825000 36.525000 45.100000 17.400000\n", "max 296.400000 49.600000 114.000000 27.000000" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get a quick look of the data\n", "df.describe()\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "### edTest(test_pandas) ###\n", "# Create a new dataframe by selecting the first 7 rows of\n", "# the current dataframe\n", "df_new = df.head(7)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TV Radio Newspaper Sales\n", "0 230.1 37.8 69.2 22.1\n", "1 44.5 39.3 45.1 10.4\n", "2 17.2 45.9 69.3 9.3\n", "3 151.5 41.3 58.5 18.5\n", "4 180.8 10.8 58.4 12.9\n", "5 8.7 48.9 75.0 7.2\n", "6 57.5 32.8 23.5 11.8\n" ] } ], "source": [ "# Print your new dataframe to see if you have selected 7 rows correctly\n", "print(df_new)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting the graph" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Sales')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Aa4axb0lqnb/claTGGPyS1Jhl+7D13XsPsn3PAQ4dnWDNqhG2blzvw9QliWUa/Lv3HmTbrn1MPHIMgINHJ9i2ax+A4S+pectyqGf7ngOPhf6UiUeOsX3PgSFVJEmnj2UZ/IeOTiyqXZJasiyDf82qkUW1S1JLlmXwb924npGVK45rG1m5gq0b1w+pIkk6fSzLi7tTF3C9q0eSTrQsgx8mw9+gl6QTLcuhHknS3Ax+SWqMwS9JjTH4JakxBr8kNSZVNewa5pXkCPCt7uNq4LtDLOd0ZJ+cyD45nv1xohb65LlVdcJDy5dE8E+XZLyqxoZdx+nEPjmRfXI8++NELfeJQz2S1BiDX5IasxSD3wezn8g+OZF9cjz740TN9smSG+OXJD0xS/GMX5L0BBj8ktSYJRX8SS5JciDJN5K8Y9j1DEOS+5LsS3J3kvGu7ZlJbkvy9e71rGHX2ackNyQ5nOSeaW1z9kGSbd0xcyDJxuFU3a85+uTfJznYHSt3J/mlad8t6z5Jcm6SzybZn+TeJG/u2ps+TqYsmeBPsgL4L8AvAucDVyQ5f7hVDc3PVdVLp92D/A7gM1X1AuAz3efl7Ebgkhlts/ZBd4xcDry4W+eD3bG03NzIiX0C8P7uWHlpVX0KmumTR4G3VtWLgIuAq7p/d+vHCbCEgh94OfCNqvo/VfUj4KPApUOu6XRxKXBT9/4mYNPwSulfVd0OPDSjea4+uBT4aFX9bVV9E/gGk8fSsjJHn8xl2fdJVT1YVXd1778P7AfW0vhxMmUpBf9a4NvTPj/QtbWmgFuT3JlkS9d2dlU9CJMHPPDsoVU3PHP1QevHzW8n+Uo3FDQ1rNFUnyRZB2wAvojHCbC0gj+ztLV4L+orq+qnmBzyuirJxcMu6DTX8nHz+8DzgZcCDwK/27U30ydJngbcDFxdVQ+fbNFZ2pZln8DSCv4HgHOnfX4OcGhItQxNVR3qXg8Dn2Dyv6PfSXIOQPd6eHgVDs1cfdDscVNV36mqY1X1d8B/5fGhiyb6JMlKJkP/I1W1q2v2OGFpBf+XgBckeV6Sn2DyQswnh1zTQCV5apKnT70HfgG4h8l+2Nwtthm4ZTgVDtVcffBJ4PIkT07yPOAFwF8Mob6Bmwq4zj9m8liBBvokSYDrgf1V9b5pX3mcsIQetl5Vjyb5bWAPsAK4oaruHXJZg3Y28InJY5ozgD+sqj9J8iXgY0muBO4HXj/EGnuXZCfwamB1kgeAa4DrmKUPqureJB8D/pLJOz2uqqpjQym8R3P0yauTvJTJIYv7gH8BzfTJK4E3AvuS3N21vZPGj5MpTtkgSY1ZSkM9kqRTwOCXpMYY/JLUGINfkhpj8EtSYwx+LVtJnjVtZsq/mjFT5cYZy16d5IMz2tZNn+3yx6zhviSrf8x1NzU8EaF6ZPBr2aqqv56amRL4EN1MlUxOZXD5jMUvB3YOtsJ5bWJyJlrplDL41aKPA69L8mR4bBKvNcAdsyx7RpKbuonOPp7kKd06j53JJxlL8qfd+2cluTXJ3iQfZtocMEn+bZKvdvPA70zytq79+Un+pJt473NJ/kGSVwC/Cmzv/ofy/N56Q80x+NWcqvprJn+OPzV//eXAH9Xsv2ZcD+yoqpcADwP/ap7NXwPcUVUbmJwG4DyY/OMA/BqTs0ReBoxNW2cH8KaqehnwNuCDVfW/uvW3dv9r+d+L/5dKszP41aqdPD7cc7Jhnm9X1Z937/8b8LPzbPfibjmq6n8Cf9O1/yxwS1VNdPPD/3d4bPbIVwB/3E0t8GHgnJkblU6lJTNXj3SK7Qbel+SngJGph3bMYub/AqY+P8rjJ05nzrMOzD7tL902jnbXHqSB8IxfTaqqHwB/CtzAyS/qnpfkZ7r3V/D4dYD7gJd1739t2vK3A28ASPKLwNTDT+4AfiXJmd1Z/i93dTwMfDPJ67t1kuQnu3W+Dzz9x/n3SSdj8KtlO4GfZPIxnnPZD2xO8hXgmUzeEQTwbuD3knwOmD6L47uBi5PcxeS02fcDVNWXmByz/zKwCxgHvtet8wbgyiRfBu7l8UeKfhTY2l0o9uKuThln55QGJMnTquoH3Z1BtwNbTjLEJPXGMX5pcHZ0P8g6E7jJ0NeweMYvSY1xjF+SGmPwS1JjDH5JaozBL0mNMfglqTH/H+lxP5IWTjMjAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Use a scatter plot for plotting a graph of TV vs Sales\n", "plt.scatter(df_new.TV, df_new.Sales)\n", "\n", "# Add axis labels for clarity (x : TV budget, y : Sales)\n", "plt.xlabel(\"TV budget\")\n", "plt.ylabel(\"Sales\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Post-Exercise Question\n", "\n", "Instead of just plotting seven points, experiment to plot all points." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Sales')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Your code here\n", "plt.scatter(df.TV, df.Sales)\n", "\n", "# Add axis labels for clarity (x : TV budget, y : Sales)\n", "plt.xlabel(\"TV budget\")\n", "plt.ylabel(\"Sales\")" ] }, { "cell_type": "code", "execution_count": 0, "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }