diff --git a/your-code/main.ipynb b/your-code/main.ipynb index a0a5b66..5c8f171 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,11 +11,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# Libraries" + "# Libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import random\n", + "import matplotlib.pyplot as plt" ] }, { @@ -29,11 +33,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "\n", + "def roll_dice(x):\n", + " roll = np.random.randint(1,7, size = x)\n", + " df = pd.DataFrame(roll, columns=['roll'])\n", + " return df\n", + "\n", + "df = roll_dice(10)" ] }, { @@ -45,11 +56,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "df = df.sort_values(by=['roll']).reset_index(drop=True)\n", + "\n", + "plt.scatter(df.index, df['roll'])\n", + "plt.show()" ] }, { @@ -61,11 +89,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "count = df['roll'].value_counts()\n", + "plt.hist(df['roll'])\n", + "plt.show()" ] }, { @@ -76,6 +120,7 @@ "source": [ "\"\"\"\n", "your comments here\n", + "The scatter plot show every occurence of value\n", "\"\"\"" ] }, @@ -91,11 +136,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2.9" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "def mean_rolls(x):\n", + " sum_rolls = x.sum()\n", + " times_rolls = len(x)\n", + " mean_roll = sum_rolls / times_rolls\n", + " return mean_roll\n", + "\n", + "mean_rolls(df['roll'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.9" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['roll'].mean()" ] }, { @@ -107,11 +190,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 1, 2: 4, 3: 1, 4: 3, 5: 1}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "def frequency(x):\n", + "\n", + " freq = {}\n", + " for item in x:\n", + " if (item in freq):\n", + " freq[item] += 1\n", + " else:\n", + " freq[item] = 1\n", + " return freq\n", + "\n", + "frequency(df['roll'])\n" ] }, { @@ -124,11 +230,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2.5" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "def median(list):\n", + " mylist = [x for x in list]\n", + " middle = int(len(mylist)/2)\n", + " \n", + " if len(mylist) % 2 == 0:\n", + " median = (mylist[int(middle)] + mylist[int(middle -1)]) / 2\n", + " \n", + " else:\n", + " median = mylist[middle]\n", + " \n", + " return median\n", + "\n", + "median(df['roll'])" ] }, { @@ -140,11 +271,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 2\n", + "2 2\n", + "3 2\n", + "4 2\n", + "5 3\n", + "6 4\n", + "7 4\n", + "8 4\n", + "9 5\n", + "Name: roll, dtype: int32" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "df['roll']" ] }, { @@ -158,11 +310,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (1076281805.py, line 2)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"C:\\Users\\Deco\\AppData\\Local\\Temp\\ipykernel_16272\\1076281805.py\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m data2 = pd.read_csv(C:\\Users\\Deco\\Desktop\\IRONHACK\\SQL\\lab-understanding-descriptive-stats\\data\\\\roll_the_dice_hundred.csv')\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "data2 = pd.read_csv(C:\\Users\\Deco\\Desktop\\IRONHACK\\SQL\\lab-understanding-descriptive-stats\\data\\\\roll_the_dice_hundred.csv')\n", + "\n", + "df2 = pd.DataFrame(data2)\n", + "df2 = df2.sort_values(by=['value'])\n", + "\n", + "plt.scatter(df2.index, df2['value'])\n", + "plt.show()" ] }, { @@ -500,9 +668,9 @@ ], "metadata": { "kernelspec": { - "display_name": "ironhack-3.7", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "ironhack-3.7" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -514,7 +682,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.4" } }, "nbformat": 4,