{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Untitled95.ipynb", "provenance": [], "authorship_tag": "ABX9TyOgjaPjvKm8ePQ8wOb6gf79", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "source": [ "# Visualize Amounts" ], "metadata": { "id": "yKsH5pmBH20B" } }, { "cell_type": "markdown", "source": [ "To get started in visualizing, we'll look at one of the simplest ideas, single quantaties. Let's grab some data too!" ], "metadata": { "id": "azZmT8IBICFv" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HmMw58R1H2PL" }, "outputs": [], "source": [ "import pandas as pa\n", "\n", "df = pa.read_csv('https://raw.githubusercontent.com/nurfnick/Data_Viz/main/Data_Sets/iris.csv')" ] }, { "cell_type": "markdown", "source": [ "## Bar Charts" ], "metadata": { "id": "Gk9oTHOdN3kV" } }, { "cell_type": "markdown", "source": [ "I'll give a small bar chart of the means of the different Classes of flowers." ], "metadata": { "id": "lxL7R7mFK307" } }, { "cell_type": "code", "source": [ "df.groupby('Class').SepalLength.agg('mean')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jzv_ivWeKqc8", "outputId": "8af6be56-9777-4db6-bf96-9be35f54fa37" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Class\n", "Iris-setosa 5.006\n", "Iris-versicolor 5.936\n", "Iris-virginica 6.588\n", "Name: SepalLength, dtype: float64" ] }, "metadata": {}, "execution_count": 2 } ] }, { "cell_type": "code", "source": [ "df.groupby('Class').SepalLength.agg('mean').plot(kind = 'bar')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 355 }, "id": "EAVCcrEGKwI4", "outputId": "6aefd85e-b616-4733-86b9-668e4f4a0ae4" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 3 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "df.groupby('Class').SepalLength.agg('mean').plot.bar()" ], "metadata": { "id": "JCEADFn5tKR_", "outputId": "44399778-3b3a-4dae-e5e2-297298002bd5", "colab": { "base_uri": "https://localhost:8080/", "height": 355 } }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 4 }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "There are lots of options some of which we should be using reguarly. A title is always nice" ], "metadata": { "id": "A82fc8bOL4kq" } }, { "cell_type": "code", "source": [ "df.groupby('Class').SepalLength.agg('mean').plot(kind = 'bar', title = 'Mean by Class')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 371 }, "id": "K7avpMHTLDqH", "outputId": "d5d328c0-85bf-4311-e687-90c7a337a0d7" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 5 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "A vertical description on what the $y$ axis represents should not be forgotten!" ], "metadata": { "id": "S2BtQLuHMG7a" } }, { "cell_type": "code", "source": [ "df.groupby('Class').SepalLength.agg('mean').plot(kind = 'bar',\n", " title = 'Mean by Class', \n", " ylabel= 'Mean of Sepal Length')\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 371 }, "id": "8hsX-J5eMFTL", "outputId": "e374fbf2-25ca-4767-9b51-a9c1fe71dc11" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 6 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "One of the complaints about a graphic like this is the length of the class titles. It takes up a lot of vertical space. With a `barh` you can change the orientation of the bars." ], "metadata": { "id": "R9RkewJAMtIQ" } }, { "cell_type": "code", "source": [ "df.groupby('Class').SepalLength.agg('mean').plot(kind = 'barh',\n", " title = 'Mean by Class', \n", " ylabel= 'Mean of Sepal Length')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 299 }, "id": "kbnuOOK-MTaU", "outputId": "d59c9651-5d7e-44d2-96a8-2bc3360b8295" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 7 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "I couldn't get the label of the values to appear, maybe you can?" ], "metadata": { "id": "77IsyWAtNp-2" } }, { "cell_type": "markdown", "source": [ "If there are lots of values, don't use bars! Let's see this with a different dataset." ], "metadata": { "id": "ekjKu6D9N2fO" } }, { "cell_type": "code", "source": [ "df2 = pa.read_csv('https://raw.githubusercontent.com/nurfnick/Data_Viz/main/Activity_Dataset_V1.csv')" ], "metadata": { "id": "IbfS3itbM_o5" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "In the following graph it is very difficult to follow the data points across. " ], "metadata": { "id": "JyGxnf7oO6U9" } }, { "cell_type": "code", "source": [ "df2.groupby('workout_type').calories.agg('mean').sort_values(ascending = True).plot(kind = 'barh')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 283 }, "id": "fKNtbftvON12", "outputId": "4a2a22fb-ea1c-4861-e6fe-9971614b9d2c" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 9 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "To clear this up you could use a point instead of a bar!" ], "metadata": { "id": "_lsbEtB9PCYe" } }, { "cell_type": "markdown", "source": [ "## Dot Plots Work Well Too" ], "metadata": { "id": "UJgaIQ55N6el" } }, { "cell_type": "code", "source": [ "df2.groupby('workout_type').calories.agg(['mean']).sort_values(by = 'mean',ascending = True).reset_index().plot.scatter(x = 'mean', y = 'workout_type')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 297 }, "id": "yE_6xh8hOXrf", "outputId": "d4f52f92-0ef3-43e0-e004-8f20cbe18776" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 10 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "This creates other issues in that the origin of the figure is not zero. To fix that, we simply require that the x limits go from 0 to 310." ], "metadata": { "id": "-71TD_x_Q6dH" } }, { "cell_type": "code", "source": [ "df2.groupby('workout_type').calories.agg(['mean']).sort_values(by = 'mean',ascending = True).reset_index().plot.scatter(x = 'mean', y = 'workout_type', xlim = [0,310])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 297 }, "id": "R_bcLyMRPXu3", "outputId": "1657701f-5730-4dea-ab26-18f4b1732739" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 11 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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YEcOSHiabhqqYgH+PiHOK+rEAeHKU+iMxLCWbCJqOjg5PzGlmO4zW5kaG81vPJT+cz9Pa3FiR9mfCZdVrgOdIej9Aum94BnBBRDyV6rxR0u6SGoG3k10SvQY4WtLz0n67S3pRmce+Efg4cFNavwk4geySbAC7An9KifH1wEj7m4DZBe38jOweaVOKZd5IXGZm9mwtTfV0dbbRUJdjdv0sGupydHW2VWTUCDNg5BgRIekdwNmSPkuW8K8CPlVQ7VZgOdAKfC8iugEkfQa4Oj0FOgz8E1DOfb2VZMlwJDneno5xblpfBvxI0hqgG7g7xdwnaaWktcD/pPuOLwduSpd4+4FjyEatZmY2ikXt81i41xzWbRygtbmxYokRQM9cPZyZJB0HdETER2sdSzV1dHREd3d3rcMwM5tWJK2KiI7i8plwWdXMzKyipv1l1fFExAXABTUOw8zMphGPHM3MzIqUlRwlvU7S8Wl5rqQXVycsMzOz2plwckyvXVsCjHwvsA74XjWCMjMzq6VyRo7vABaRvpQeEb9j6+/qmZmZzQjlJMe/pC+2B0B6bZqZmdmMU05y/G9J5wC7SfoQ8L/At6sTlpmZWe1M+KscEfFVSW8EniCbM/FzEfHzqkVmZmZWI+V+z3EN2ewVkZbNzMxmnHKeVv17sneUvpNsFoybJX2gWoGZmZnVSjn3HP8FOCAijouIY8nmQlyyvQFI2iKpR9JaSZdKes42tHGcpDNHKX++pB9LWi3pLklXldHmjeXGMcF2r5K0WzXaNjObrvr6h1j9yGMVm6x4e5VzWbWPbKqlEZtS2fYaiIh2AEnLgH8A/qMC7QL8K9lEx99I7bdNdMeIOLhCMRS3e2Q12jUzm65W9KxnyfJe6nI5hvN5ujrbWNQ+r6YxlTNyvB+4RdKp6YUANwP3SvqEpE9UKJ4bgL3S3Io/lNQr6eaRpFaqfAx7AOtGViKiN7VzlqRFafkKSeel5Q9I+lJa7k8/D5P0S0krJD0o6TRJ75V0q6Q1kl6S6l0g6b9SXA+m/c6T9GtJF4zEIOlhSXMkLUjbvi3pTklXp/kmkfTq1MceSV9JU1uZmc04ff1DLFney+Bwnk1DmxkcznPy8t6ajyDLSY4PAD8kfc8RWAE8RPYigO1+GYCkWcARZA/6fAG4IyLayOZlvChVK1VeylnAdyT9QtKnJf2fVH4DcEhange8Ii0fAlw/Sjv7k41oXw68D3hpRBxENm/jxwrqNQOvBU4ErgS+BuwLvFJS+yjt7g2cFRH7Ao8Bnan8fODDaURdck5HSYsldUvq3rBhQ6lqZmZT1rqNA9Tltk5Fdbkc6zYO1CiiTDmXVS+PiGo8odooqSct3wB8B7iFlCgi4lpJLZJ2AV5XonxUEfEzSXsCbyZLvHdI2i8d5+OSXgHcBTRL2oMssf3zKE3dFhG/B5D0AHB1Kl8DvL6g3o/S5MtrgD+O/L4k3QksAHrY2kMRMVK2CliQ7kfOjoiRCZS/D7y1RP+WAkshm8+x1O/BzGyqam1uZDif36psOJ+ntbmxRhFlyhk5np0uJX5E0q4VjGEgItrT52MR8ZcKtk1E/Dkivh8R7wNuAw6NiPXAbmRJ83qyZPl3QH9EbBqlmcLxfb5gPc/W/8EYGqXOaPVGa3dLiTpmZjNWS1M9XZ1tNNTlmF0/i4a6HF2dbbQ01dc0rnJeAnCIpJcCxwOrJN0KXBARV4+z67a4AXgv8EVJhwGPRsQTkkqVj9qIpMOBmyPiKUmzgZcAv02bbwY+DhwOtACXpU9NRcRjkjZJek1E3AK8u9YxmZlV06L2eSzcaw7rNg7Q2txY88QIZY5UIuJeSZ8BuoFvAgcoy0yfiojLKxjXqcB5knqBp4Bjxykv5UDgTEmbyUbJ50bEbWnbDcCbIuJ+Sb8Bdk9lU8EHgW9LygO/BB6vcTxmZlXV0lQ/JZLiCGXvEp9AxezJ0OOBtwA/B74TEbenh1xuiogXVS/MHYukpogYeVr2FGCPiDhhrH06Ojqiu7t7UuIzM5spJK2KiI7i8nJGjv9J9nTmpyLi6ceIIuJ3aTRplfMWSZ8kOz+/AY6rbThmZjuWcpLjFRHx3cICSSdExDeKy237RMQlwCW1jsPMbEdVztOq7x+l7LgKxWFmZjZljDtylPR/gfcAL5Z0ZcGm2cCfqxWYmZlZrUzksuqNwO+BOcAZBeWbgN5qBGVmZlZL4ybHiPgN2UMhrx2rnqSbImLMOmZmZtNBOfccx9NQwbbMzMxqppLJ0e/2NDOzGaGSydHMzGxGqGRyHP0Fp2ZmZtPMhJOjpNPHKXtfRSIyMzOrsXJGjm8cpeyIkYWImLaz1ad5IXvS5w+S1hes7zzOvhdIOnqU8oclzRml/MZKxm5mNhP09Q+x+pHH6OsfGr/yJJjISwD+EfgIsGeaDWPEbGBltQKbTBHRB7QDSDqVbF7Hr45slzQrIjZX6FgHV6IdM7OZYkXPepYs76Uul2M4n6ers41F7fNqGtNERo7fB94GXJl+jnwOjIhjqhhbTaUR4bck3QJ0SXqJpJ9KWiXpBkn7jLLPF9N+OxWUNUr6H0kfSusjs20cJuk6SZdJulvSsjT9F5KOTGWrJH1T0o8nqdtmZpOqr3+IJct7GRzOs2loM4PDeU5e3lvzEeREXgLwOPC4pCVFm5rS1Eq/HW2/GaIVODgitki6BviHiLhP0muAs8kmSgZA0lfIRtPHR0SkPNcE/AC4KCIuGqX9A4B9gd+RjcIXSuoGzgEOjYiHJF1cKjhJi4HFAPPnz9/+3pqZTbJ1Gweoy+UYJP90WV0ux7qNAzWd37GcWTl+QvZdRpF94f/FwD1kf9xnqktTYmwCDgYuTUkPoPCsfRa4JSIWF+2/AuiKiGUl2r81ItYBSOoBFgD9wIMR8VCqczEpARaLiKXAUsjmcyyjX2ZmU0JrcyPD+fxWZcP5PK3NjTWKKDPhB3Ii4pUR0ZZ+7g0cBNxUvdCmhCfTzxzwWES0F3xeXlDvNuBASbsX7b8SeLMKMmqRwusGWyjvPytmZtNeS1M9XZ1tNNTlmF0/i4a6HF2dbTUdNcJ2/DGOiNvT5cUZLyKekPSQpL+NiEtTsmuLiNWpyk+BnwE/kfSmiNiUyj+XPmeRPdQ0EfeQPfy0ICIeBt5VuZ6YmU09i9rnsXCvOazbOEBrc2PNEyOUkRwlfaJgNQe8iuxe2Y7ivcB/SfoMUEd2L3EkOZKS5mzgSklHFux3AnCepK6IOHm8g0TEgKSPAD+V9CTZqNTMbEZraaqfEklxhCImdqtK0ucLVjcDDwPLI2KwCnHt0NKDTv1phHoWcF9EfG2sfTo6OqK7u3tyAjQzmyEkrYqIjuLyCY8cI+ILqaGmtN5fufCsyIckHQvsDNxB9vSqmZlNknIuq+4HfBfYPa0/Chw7nd+MM1WlUeKYI0UzM6uecl4ftxT4RES8KCJeBPy/VGZmZjajlJMcnxsRvxhZiYjrgOdWPCIzM7MaK+erHA9K+izZpVWAY4AHKx+SmZlZbZUzcvwAMBe4HFielo+vRlBmZma1VM7I8Z0R8c+FBZJOA06pbEhmZma1VU5y7JQ0OPKeUElnArV9+Z2ZmVkVlJUcyd7+kgfeTPau0Q9WJywzM7Pamchkx4Uv0/574IdkL9T+gqTdI+LPVYrNzMysJiYyclxFNlXVCAFvSZ8A9qxCXGZmZjUz7tOqEfFiYC/gfRGxZ0S8uOBT8cQoqazX0kk6TNKPKx1Havulkq6SdJ+k2yX9t6Tnb0M7D0uak5ZvrHykZmbTT1//EKsfeYy+/qHxK0+yCd1zjIh8egDngCrHUzOSZkXE5oL1BrIJnj8RET9KZYeRfYXlj9t6nIg4ePsiNTOb/lb0rGfJ8l7qcjmG83m6OttY1D6v1mE9rZzvOV4jqXOMiXsrKo0Ir5N0maS7JS0bObakN6ey24F3Fuyzu6QfSuqVdLOktnHKT5X0XUkreeblBiPeA9w0khgheytQRKyVdL2k9oLj/krS/pKaJJ0vaU06Vuco/eqfQP+OTGWrJH2zWiNjM7Na6OsfYsnyXgaH82wa2szgcJ6Tl/dOqRFkOcnxw8ClwF8kPSFpk6QnqhTXiAOAjwOvILu3uTCN6L4NvA04EHhBQf0vAHdERBvwKeCiccpJbb8hIv5v0bH3I7vfOprvAMdBdukVaEgTH38WeDwiXpmOde029u8c4IiIOJBspDoqSYsldUvq3rBhwziHMjObGtZtHKAut3X6qcvlWLdxoEYRPduEk2NEzI6IXETURcQuaX2XagYH3BoR6yIiD/QAC4B9gIci4r7IJqP8XkH915FGgBFxLdAiaZcxygGujIhyz8ilwFsl1ZG9OeiCVP4GsvkXScfauI39ezAiHkp1Li61c0QsjYiOiOiYO7dkDjUzm1JamxsZzue3KhvO52ltnjpfnS9n5IikRZK+mj5vrVZQBQrH2Fso73uZE/VkifI7yUamzxIRTwE/B44C/g5Yto3Hnoz+mZlNKS1N9XR1ttFQl2N2/Swa6nJ0dbbR0lRf69CeVs58jqcBr+aZRHCCpIUR8cmqRFba3cACSS+JiAeAwsuhNwDvBb6YHp55NCKekFSqfKzjfB/4pKS3RMRPACQdCvw5zWF5LvAj4IaCEeLPgX8iu1SKpOYJjB6L3QPsKWlBRDwMvKvM/c3MprxF7fNYuNcc1m0coLW5cUolRihvpHIk0J4uASLpQrJZ6ic1OUbEoKTFwE8kPUWWEGenzacC50nqBZ4Cjh2nfKzjDKTR8dclfR0YBnqBE9L2Veme6/kFu/0bcJaktWQjwS+Qvai9nP4NSPoI8FNJTwK3lbO/mdl00dJUP+WS4ghlt+0mUDFLLIeNvBEnvTnnuvTgyQ5H0v8BrgP2GfkPQwXbboqI/vT06lnAfRHxtbH26ejoiO7u7kqGYWY240laFREdxeXljBy/DNwu6Tqyt+Qcyg46I4ek9wNfIvsOZEUTY/IhSccCO5ONzs+pwjHMzKyEckaO3wPuBTYCDwO3RcQfqhealcMjRzOz8lVi5Pgd4BBgEfAS4A5J10fENyoUo5mZ2ZQw4eQYEb+QdD3ZE6uvB/4B2BdwcjQzsxmlnK9yXAM8F7iJ7AnRV0fEn6oVmJmZWa2U8xKAXuAvZK9VawP2kzR1XmdgZmZWIeVcVj0RQNJssveKnk/2XtOp+SUVMzOzbVTOZdWPkj2QcyDZ06rnkV1eNTMzm1HKeVq1AfgPYFXhvIdmZmYzTTmXVb9azUDMzMymirJm5ZhpJG2R1FPwWVCl4xyXXjc3Xr2HJc2pRgxmZjZxO/oUSQMR0T7ahvReU1Xo9XDHAWuB31WgLTOzaa2vf2jKzsYxYoceORaTtEDSPZIuIktmL5T0L5Juk9Qr6QsFdY+RdGsacZ4jaaf0uUDSWklrJJ0o6WigA1iW6r5F0g8L2nmjpCtGieVZ7U/Cr8DMrKpW9Kxn4enXcsy5t7Dw9Gu5smd9rUMa1Y6eHBsLLqmOJKi9gbMjYl/gZWn9IKAdOFDSoZJeTjbP4sI08txCNl9kOzAvIvaLiFcC50fEZUA38N5U9ypgH0lz0/GOJ3vy92ljtG9mNm319Q+xZHkvg8N5Ng1tZnA4z8nLe+nrHxp/50nmy6oFl1XTPcffRMTNqehN6XNHWm8iS5ZtZF9puS1NmNwI/Ils8uM9Jf0n8BPg6uIDRkRI+i5wjKTzgdcC7y+q9tcl2t9KmtdyMcD8+fPL67mZ2SRbt3GAulyOQZ65W1WXy7Fu48CUu7y6oyfH0TxZsCzg3yNiqymjJH0MuDAinjXRs6T9gb8he/fs3wEfGOUY55Ml0kHg0lG+GqNS7ReKiKXAUshm5RirrplZrbU2NzKc3/oxjuF8ntbmqfeytR39sup4fgZ8QFITgKR5kp4HXAMcnZaRtLukF6UnTXMRsRz4DPCq1M4mYPZIoxHxO7KHcz5DliiLjdp+VXpoZjZJWprq6epso6Eux+z6WTTU5ejqbJtyo0bwyHFMEXF1uv93U7q82Q8cExF3SfoMcLWkHDAM/BMwAJyfygBGRn4XAN+SNAC8NiIGgGXA3Ij49SjHLdX+b6rVVzOzybCofR4L95oz5Z9WnfBkx1ZZks4E7oiI71SiPU92bGZWvkpMdmwVImkV2b3N/1frWMzM7NmcHGsgIg6sdQxmZlaaH8gxMzMr4uRoZmZWxMnRzMysiJOjmZlZESdHMzOzIk6OZmZmRZwczczMijg5mpmZFXFyNDMzK7LDJkdJLQUTHf9B0vqC9Z0rdIyH00wdE62/SNIpaflUSSel5QskHV2JmMzMaqmvf4jVjzw2JSc4LrTDvj4uIvqAdsgSEdAfEV8d2S5p1ijzLFY7piuBKyfzmGZmk2VFz3qWLO+lLpdjOJ+nq7ONRe3zah3WqHbYkeNo0gjtW5JuAbokvUTSTyWtknSDpH1SvbdJukXSHZL+V9LzU3mLpKsl3SnpXLJJi5G0QNLdqf17JS2T9AZJKyXdJ+mgVO+4NFuHmdmM0tc/xJLlvQwO59k0tJnB4TwnL++dsiNIJ8dnawUOjohPAEuBj6UXhZ8EnJ3q/Ar4q4g4APgBcHIq/zzwq4jYF7gCmF/Q7l7AGcA+6fMe4HWp3U9tS6CSFkvqltS9YcOGbWnCzGxSrNs4QF1u65RTl8uxbuNAjSIa2w57WXUMl0bEFklNwMHApWmiY4CRWTlbgUsk7QHsDDyUyg8F3gkQET+RtLGg3YciYg2ApDuBayIiJK0BFmxLoBGxlCyB09HR4Yk5zWzKam1uZDif36psOJ+ntbmxRhGNzSPHZ3sy/cwBj0VEe8Hn5WnbfwJnRsQrgQ8DDRNot/DaQb5gPY//k2JmM1xLUz1dnW001OWYXT+LhrocXZ1ttDTVj79zDfiPcgkR8YSkhyT9bURcqmz42BYRq4FdgfWp6rEFu11Pdrn03yQdATRPbtRmZlPXovZ5LNxrDus2DtDa3DhlEyN45Die9wIflLQauBM4KpWfSna5dRXwaEH9LwCHpsum7wR+O4mxmplNeS1N9ez/wt2mdGIEUIRvVc0EHR0d0d3dXeswzMymFUmrIqKjuNwjRzMzsyJOjmZmZkWcHM3MzIo4OZqZmRVxcjQzMyvi5GhmZlbEydHMzKyIk6OZmVkRJ0czM7MiTo5mZmZFnBzNzMyKODkWkfQ1SR8vWP+ZpHML1s+Q9IkS+14g6ei0/LCkOaPUubEKYZuZTWl9/UOsfuQx+vqHxq88BXjKqmdbCfwd8HVJOWAOsEvB9oOBE7e18Yg4ePvCMzObXlb0rGfJ8l7qcjmG83m6OttY1D6v1mGNySPHZ7sReG1a3hdYC2yS1CypHng58CZJt0laK2lpmutxVJIaJf2PpA+l9f708zBJ10m6TNLdkpaNtCPpyFS2StI3Jf24mh02M6uWvv4hlizvZXA4z6ahzQwO5zl5ee+UH0E6ORaJiN8BmyXNJxsl3gTcQpYwO4A1wJkR8eqI2A9oBN5aorkm4EfAxRHx7VG2HwB8HHgFsCewUFIDcA5wREQcCMwtFaukxZK6JXVv2LCh/M6amVXZuo0D1OW2TjV1uRzrNg7UKKKJcXIc3Y1kiXEkOd5UsL4SeL2kWyStAQ4nG2GOZgVwfkRcVGL7rRGxLiLyQA+wANgHeDAiHkp1Li4VZEQsjYiOiOiYO7dkDjUzq5nW5kaG8/mtyobzeVqbG2sU0cQ4OY5uJVkifCXZZdWbyUaOB5MlzrOBoyPilcC3gYYx2nnzGJddC68rbMH3gM1shmlpqqers42Guhyz62fRUJejq7ONlqb6Woc2Jv8xHt2NwElkI7gtwJ8l7UY2QvxQqvOopCbgaOCyEu18Ln3OAj4ywWPfA+wpaUFEPAy8a5t6YGY2RSxqn8fCveawbuMArc2NUz4xgkeOpawhe0r15qKyxyPiUbLR4lrgZ8Bt47R1AtAoqWsiB46IAbJE+lNJq4BNwOPlhW9mNrW0NNWz/wt3mxaJEUARUesYrIikpojoT5djzwLui4ivjbVPR0dHdHd3T06AZmYzhKRVEdFRXO6R49T0IUk9wJ3ArmRPr5qZ2STxPccpKI0SxxwpmplZ9XjkaGZmVsTJ0czMrIiTo5mZWREnRzMzsyJOjmZmZkWcHM3MzIo4OZqZmRVxcjQzMyvi5GhmZlZkh0+Oklok9aTPHyStL1jfeZx9OyR9My0fJ+nMUeocJ2lDau9uSSdWqy9mZrXQ1z/E6kceo69/aPzK08QO//q4iOgD2gEknQr0R8RXR7ZLmhURm0vs2w1M5G3fl0TERyW1APdIuiwiHtnu4M3MamxFz3qWLO+lLpdjOJ+nq7ONRe3zah3WdtvhR46jkXSBpG9JugXoknSQpJsk3SHpRkkvS/UOk/TjibabEvH9wB6SFkhaW3DMk1JyRtJ1kk6XdKukeyUdUtkempltv77+IZYs72VwOM+moc0MDuc5eXnvjBhB7vAjxzG0AgdHxBZJuwCHRMRmSW8Avgx0ltugpPlAA9ALvGCc6rMi4iBJRwKfB94wSnuLgcUA8+fPLzccM7Ptsm7jAHW5HIPkny6ry+VYt3Fg2szbWIqTY2mXRsSWtLwrcKGkvYEA6sps612SDgX2AT4aEYPZVI1jujz9XAUsGK1CRCwFlkI2n2OZMZmZbZfW5kaG8/mtyobzeVqbG2sUUeX4smppTxYsfxH4RUTsB7yNbPRXjksiog04GDhN0guAzWz9+y9uc+S6xBb8nxgzm4Jamurp6myjoS7H7PpZNNTl6Opsm/ajRvAf3YnaFViflo/b1kYiolvSd4ETgM8Bz0sP6fQDbwV+up1xmplNqkXt81i41xzWbRygtblxRiRG8MhxorqAf5d0B9v/H4rTgePJRor/CtwK/By4ezvbNTOriZamevZ/4W4zJjECKMK3qmaCjo6O6O6eyLdKzMxshKRVEdFRXO6Ro5mZWREnRzMzsyJOjmZmZkWcHM3MzIr4gZwZQtIm4J5ax1EFc4BHax1EFbhf04v7Nb2U068XRcTc4kJ/z3HmuGe0J66mO0nd7tf04X5NL+5Xab6samZmVsTJ0czMrIiT48yxtNYBVIn7Nb24X9OL+1WCH8gxMzMr4pGjmZlZESdHMzOzIk6O05ykN0u6R9L9kk6pdTzbQ9LDktZI6pHUncp2l/RzSfeln821jnMiJJ0n6U+S1haUjdoXZb6ZzmGvpFfVLvLSSvTpVEnr0znrkXRkwbZPpj7dI+lvahP1+CS9UNIvJN0l6U5JJ6Ty6X6+SvVrWp8zSQ2SbpW0OvXrC6n8xZJuSfFfImnnVF6f1u9P2xdM6EAR4c80/QA7AQ8AewI7A6uBV9Q6ru3oz8PAnKKyLuCUtHwKcHqt45xgXw4FXgWsHa8vwJHA/wAC/gq4pdbxl9GnU4GTRqn7ivTvsR54cfp3ulOt+1CiX3sAr0rLs4F7U/zT/XyV6te0Pmfp996UluuAW9J5+G/g3an8W8A/puWPAN9Ky+8mm3x+3ON45Di9HQTcHxEPRsRfgB8AR9U4pko7CrgwLV8IvL12oUxcRFwP/LmouFRfjgIuiszNwG6S9piUQMtQok+lHAX8ICKGIuIh4H6yf69TTkT8PiJuT8ubgF8D85j+56tUv0qZFucs/d7702pd+gRwOHBZKi8+XyPn8TLgryVpvOM4OU5v84BHCtbXMfY//qkugKslrZK0OJU9PyJ+n5b/ADy/NqFVRKm+TPfz+NF0efG8gsve07JP6ZLbAWSjkRlzvor6BdP8nEnaSVIP8CeyyeIfAB6LiM2pSmHsT/crbX8caBnvGE6ONpW8LiJeBRwB/JOkQws3RnZdZEZ892gG9eW/gJcA7cDvgTNqGs12kNQELAc+HhFPFG6bzudrlH5N+3MWEVsioh1oJRvd7lPpYzg5Tm/rgRcWrLemsmkpItann38CriD7R//HkUtW6eefahfhdivVl2l7HiPij+kPVR74Ns9chptWfZJUR5ZAlkXE5al42p+v0fo1U84ZQEQ8BvwCeC3Z5e2R94UXxv50v9L2XYG+8dp2cpzebgP2Tk9p7Ux2s/nKGse0TSQ9V9LskWXgTcBasv4cm6odC6yoTYQVUaovVwLvT09B/hXweMHlvCmt6F7bO8jOGWR9end6UvDFwN7ArZMd30Sk+0/fAX4dEf9RsGlan69S/Zru50zSXEm7peVG4I1k91N/ARydqhWfr5HzeDRwbboSMLZaP3nkz3Y/uXUk2VNoDwCfrnU829GPPcmelFsN3DnSF7J7A9cA9wH/C+xe61gn2J+LyS5ZDZPd//hgqb6QPX13VjqHa4COWsdfRp++m2LuTX+E9iio/+nUp3uAI2od/xj9eh3ZJdNeoCd9jpwB56tUv6b1OQPagDtS/GuBz6XyPcmS+f3ApUB9Km9I6/en7XtO5Dh+fZyZmVkRX1Y1MzMr4uRoZmZWxMnRzMysiJOjmZlZESdHMzOzIk6OZmZmRZwczczMijg5mtmESVog6W5JF0i6V9IySW+QtDLNe3hQetvReWnOvTskHVWw7w2Sbk+fg1P5YZKuk3RZanvZRGZNMKsmvwTAzCYsze5wP9kMD3eSvcJwNdnbchYBxwN3AXdFxPfSa75uTfUDyEfEoKS9gYsjokPSYWSv+toX+B2wEviXiPjV5PXMbGuzxq9iZraVhyJiDYCkO4FrIiIkrQEWkL30eZGkk1L9BmA+WeI7U1I7sAV4aUGbt0bEutRmT2rHydFqxsnRzMo1VLCcL1jPk/1N2QJ0RsQ9hTtJOhX4I7A/2S2dwRJtbsF/m6zGfM/RzCrtZ8DHRu4bSjogle8K/D6yqZLeB+xUo/jMxuXkaGaV9kWgDuhNl12/mMrPBo6VtJpsctonaxSf2bj8QI6ZmVkRjxzNzMyKODmamZkVcXI0MzMr4uRoZmZWxMnRzMysiJOjmZlZESdHMzOzIv8fAd6P9+ccUO4AAAAASUVORK5CYII=\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "All the workout types are giving us about the same bang for our buck, at least in mean calories." ], "metadata": { "id": "0rCTdx6ORQHq" } }, { "cell_type": "markdown", "source": [ "## Adding Labels" ], "metadata": { "id": "6of27SIgNzWU" } }, { "cell_type": "markdown", "source": [ "It might also be nice to see the numbers presented with the data. This is esspecially nice for a small number of quantities." ], "metadata": { "id": "LDq6XzVN3bzH" } }, { "cell_type": "code", "source": [ "ax = df2.groupby('workout_type').calories.agg(['mean']).sort_values(by = 'mean',ascending = True).reset_index().plot.scatter(x = 'mean', y = 'workout_type')#this made the same graph as above.\n", "\n", "for i,k in enumerate(df2.groupby('workout_type').calories.agg(['mean']).sort_values(by = 'mean',ascending = True).reset_index()['mean']): #here I loop through the values, k, and indicies ,i.\n", " ax.annotate(str(int(k)),[k+.2,i+.2])\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 281 }, "id": "oGFyC6IaRMuU", "outputId": "e10740fd-744c-493d-d8e4-0b3af71c8b72" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "## Bar Charts with Multiple Data" ], "metadata": { "id": "xIOR_FBaNx4O" } }, { "cell_type": "code", "source": [ "df.groupby('Class').agg('mean').plot(kind = 'bar')" ], "metadata": { "id": "iQ-VnTXUIxnB", "colab": { "base_uri": "https://localhost:8080/", "height": 355 }, "outputId": "75d2e533-b507-43fa-ec1e-9649238b5158" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 13 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "df.groupby('Class').agg('mean').plot(kind = 'bar', stacked = True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 355 }, "id": "Ft2Gbo67atNj", "outputId": "16e15a1c-a822-41f6-9b6f-4fc6574f23e0" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 14 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "If you want to add labels, it should be simple but the version on colab is out of date... I update here." ], "metadata": { "id": "9c-E1IRhbz9h" } }, { "cell_type": "code", "source": [ "!pip install --upgrade matplotlib" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DHIXL0Afb7X6", "outputId": "0d9a1e44-1653-45e1-a7ef-106670ba731b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.5.1)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.0)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n", "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n", "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.7)\n", "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (7.1.2)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.21.5)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (21.3)\n", "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (4.31.2)\n", "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (3.10.0.2)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7->matplotlib) (1.15.0)\n" ] } ] }, { "cell_type": "code", "source": [ "import matplotlib\n", "matplotlib.__version__" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "_5isVITWc4Ia", "outputId": "717a0947-63fd-4bc0-b71a-736a07cc2cb3" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'3.5.1'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 16 } ] }, { "cell_type": "markdown", "source": [ "Now with the correct version it is acually really easy." ], "metadata": { "id": "6Z4oY6y6fL32" } }, { "cell_type": "code", "source": [ "ax = df.groupby('Class').agg('mean').plot(kind = 'bar', ylim =[0,8])\n", "\n", "for container in ax.containers:\n", " ax.bar_label(container)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 341 }, "id": "JAUjvHh-dvTG", "outputId": "fba3542c-91b5-437f-be59-dbfd0145a1d3" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "ax = df.groupby('Class').agg('mean').plot(kind = 'bar', stacked = True)\n", "\n", "for container in ax.containers:\n", " ax.bar_label(container)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 337 }, "id": "JpvMYyWdbSHz", "outputId": "5251a3ea-10f9-4e05-c16d-5cc88461447f" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "Be careful with the stacked as it is giving a cummulative total. This doesn't really make any sense here..." ], "metadata": { "id": "Tzoq8uJMd3xZ" } }, { "cell_type": "markdown", "source": [ "## Your Turn\n", "\n", "Using the [Air B&B NYC data](https://raw.githubusercontent.com/nurfnick/Data_Viz/main/Data_Sets/AB_NYC_2019.csv) complete the following tasks.\n", "\n", "1. Create a bar graph of the maximum 'price' by 'neighbourhood_group'. Include the 'price' in your graph\n", "2. Create a multiple bar graph with 'neighbourhood_group' and 'room_type' by looking at the average 'price'." ], "metadata": { "id": "nUMIrKVof3x7" } } ] }