From c9208714ffcb0e8a1153fbca5b22ee49bdd406af Mon Sep 17 00:00:00 2001 From: cfmago Date: Thu, 30 Jul 2020 12:38:27 +0100 Subject: [PATCH 1/4] partial solution challenge 1 --- your-code/main.ipynb | 573 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 563 insertions(+), 10 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index a0a5b66..630560f 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,11 +11,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ - "# Libraries" + "# Libraries\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import scipy.stats as stats\n", + "import random\n", + "import pandas as pd" ] }, { @@ -29,11 +36,118 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "\n", + "def dice_roll():\n", + " # define list of possible outcomes\n", + " dice = [1, 2, 3, 4, 5, 6]\n", + " # randomly choose between possible outcomes, do that 10 time (k argument)\n", + " # random.choices returns a list\n", + " r = random.choices(dice, k=10)\n", + " # save it in a dataframe\n", + " r = pd.DataFrame(r)\n", + " return r\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " result\n", + "0 1\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 3\n", + "5 3\n", + "6 4\n", + "7 5\n", + "8 5\n", + "9 6" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# I want the old index to be dropped\n", + "r.drop('index', axis=1, inplace=True)\n", + "r" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the results\n", + "\n", + "%matplotlib inline\n", + "\n", + "x = r['result']\n", + "\n", + "sns.distplot(x, kde = False)\n", + "plt.title(\"Histogram of simulated dice rolls\")\n", + "plt.xlim([0,7])\n", + "plt.show()" ] }, { @@ -65,7 +617,8 @@ "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "\n" ] }, { @@ -500,9 +1053,9 @@ ], "metadata": { "kernelspec": { - "display_name": "ironhack-3.7", + "display_name": "Python 3", "language": "python", - "name": "ironhack-3.7" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -514,7 +1067,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.6" } }, "nbformat": 4, From 7e7cf150dee85623d7be50b1ba851fed60a862d9 Mon Sep 17 00:00:00 2001 From: cfmago Date: Thu, 30 Jul 2020 15:47:01 +0100 Subject: [PATCH 2/4] I started challenge 2 --- your-code/main.ipynb | 122 +++++++++++++++++++++++++++++++++++++++---- 1 file changed, 112 insertions(+), 10 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 630560f..15b6782 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -575,12 +575,23 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 119, "metadata": {}, "outputs": [ + { + "ename": "NameError", + "evalue": "name 'bins' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Histogram of simulated dice rolls\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'bins' is not defined" + ] + }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -594,11 +605,40 @@ "source": [ "# plot the results\n", "\n", - "%matplotlib inline\n", + "# xticks are not aligned with bars\n", "\n", "x = r['result']\n", "\n", - "sns.distplot(x, kde = False)\n", + "sns.distplot(x, kde=False, hist_kws={\"rwidth\":0.75}, bins=6)\n", + "plt.title(\"Histogram of simulated dice rolls\")\n", + "plt.xlim([0,7])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# xticks are not aligned with bars\n", + "\n", + "x = r['result']\n", + "\n", + "plt.hist(x, bins=6, width=0.7)\n", "plt.title(\"Histogram of simulated dice rolls\")\n", "plt.xlim([0,7])\n", "plt.show()" @@ -613,12 +653,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# your code here\n", - "\n" + "\n", + "freq = r['result'].value_counts()\n", + "\n", + "plt.bar(freq.index, freq.values)\n", + "plt.show()" ] }, { @@ -628,7 +685,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "The two plots show the same information: number of accurrences of each side of the dice. \n", "\"\"\"" ] }, @@ -644,11 +701,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "\n", + "def mean(x):\n", + " x = sum(x)/len(x)\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.3" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean(r['result'])" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.3" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the mean is correct\n", + "np.mean(r['result'])" ] }, { From f0ad14cf0527247d2098c7ca84f390dd815a53a2 Mon Sep 17 00:00:00 2001 From: cfmago Date: Mon, 17 Aug 2020 01:39:53 +0100 Subject: [PATCH 3/4] solution --- your-code/main.ipynb | 1553 ++++++++++++++++++++++++++++++++++-------- 1 file changed, 1279 insertions(+), 274 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 15b6782..d8b4abf 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -55,99 +55,11 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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3\n", - "5 3\n", - "6 2\n", - "7 3\n", - "8 6\n", - "9 1" + "0 3\n", + "1 6\n", + "2 1\n", + "3 5\n", + "4 6\n", + "5 5\n", + "6 4\n", + "7 1\n", + "8 3\n", + "9 2" ] }, - "execution_count": 54, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -259,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -269,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -298,43 +210,43 @@ " \n", " \n", " \n", - " 1\n", + " 2\n", " 1\n", " \n", " \n", - " 9\n", + " 7\n", " 1\n", " \n", " \n", - " 6\n", + " 9\n", " 2\n", " \n", " \n", - " 4\n", + " 0\n", " 3\n", " \n", " \n", - " 5\n", + " 8\n", " 3\n", " \n", " \n", - " 7\n", - " 3\n", + " 6\n", + " 4\n", " \n", " \n", " 3\n", - " 4\n", + " 5\n", " \n", " \n", - " 0\n", + " 5\n", " 5\n", " \n", " \n", - " 2\n", - " 5\n", + " 1\n", + " 6\n", " \n", " \n", - " 8\n", + " 4\n", 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" 1\n", + " 6\n", " \n", " \n", " 9\n", - " 8\n", + " 4\n", " 6\n", " \n", " \n", @@ -451,19 +363,19 @@ ], "text/plain": [ " index result\n", - "0 1 1\n", - "1 9 1\n", - "2 6 2\n", - "3 4 3\n", - "4 5 3\n", - "5 7 3\n", - "6 3 4\n", - "7 0 5\n", - "8 2 5\n", - "9 8 6" + "0 2 1\n", + "1 7 1\n", + "2 9 2\n", + "3 0 3\n", + "4 8 3\n", + "5 6 4\n", + "6 3 5\n", + "7 5 5\n", + "8 1 6\n", + "9 4 6" ] }, - "execution_count": 67, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -476,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -526,11 +438,11 @@ " \n", " \n", " 5\n", - " 3\n", + " 4\n", " \n", " \n", " 6\n", - " 4\n", + " 5\n", " \n", " \n", " 7\n", @@ -538,7 +450,7 @@ " \n", " \n", " 8\n", - " 5\n", + " 6\n", " \n", " \n", " 9\n", @@ -555,14 +467,14 @@ "2 2\n", "3 3\n", "4 3\n", - "5 3\n", - "6 4\n", + "5 4\n", + "6 5\n", "7 5\n", - "8 5\n", + "8 6\n", "9 6" ] }, - "execution_count": 68, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -575,23 +487,12 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 14, "metadata": {}, "outputs": [ - { - "ename": "NameError", - "evalue": "name 'bins' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Histogram of simulated dice rolls\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m 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\n", 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\n", 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" ] @@ -617,12 +518,12 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -653,12 +554,12 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -680,12 +581,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nThe two plots show the same information: number of occurrences of each side of the dice. \\n'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", - "The two plots show the same information: number of accurrences of each side of the dice. \n", + "The two plots show the same information: number of occurrences of each side of the dice. \n", "\"\"\"" ] }, @@ -701,49 +613,49 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "# your code here\n", "\n", - "def mean(x):\n", + "def mean_func(x):\n", " x = sum(x)/len(x)\n", " return x" ] }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3.3" + "3.6" ] }, - "execution_count": 129, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "mean(r['result'])" + "mean_func(r['result'])" ] }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3.3" + "3.6" ] }, - "execution_count": 130, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -762,96 +674,407 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 2, 2: 1, 3: 2, 4: 1, 5: 2, 6: 2}" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "# value_counts()\n", + "\n", + "def freq_func(x):\n", + " result_dict = {}\n", + " for result in x:\n", + " if result not in result_dict.keys():\n", + " # add result of dice roll as key and assign value 1\n", + " result_dict[result] = 1\n", + " else:\n", + " # if a result of dice roll occurs once more, increment value in dict by 1\n", + " result_dict[result] += 1 \n", + " return result_dict\n", + "\n", + "freq_func(r['result'])" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 36, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.6666666666666667" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n", - "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median." + "\n", + "mean_func(result_dict.values())\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1.6666666666666667" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "mean(result_dict.values())\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. " + "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n", + "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Challenge 3\n", - "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n", - "#### 1.- Sort the values and plot them. What do you see?" + "# your code here\n", + "\n", + "def median_func(lst):\n", + " sortedLst = sorted(list(lst))\n", + " lstLen = len(lst)\n", + "\n", + " if (lstLen % 2) == 0:\n", + " index1 = int(lstLen / 2)\n", + " index2 = int((lstLen / 2) - 1)\n", + " return (sortedLst[index1] + sortedLst[index2]) / 2, [index1, index2]\n", + " else:\n", + " index1 = int(lstLen / 2)\n", + " return sortedLst[index1], [index1]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(3.5, [5, 4])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "median_func(r['result'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "outputs": [ + { + "data": { + "text/plain": [ + "(3.5, [5, 4])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "median(r['result'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls." + "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Q1, median, Q3): [13.0, 14, 21.5]\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "num_list = [28, 12, 8, 27, 16, 31, 14, 13, 19, 1, 1, 22, 13]\n", + "\n", + "med, median_indices = median(num_list)\n", + "\n", + "Q2, Q1_indices = median(num_list[:median_indices[0]])\n", + "Q1, Q2_indices = median(num_list[median_indices[-1] + 1:])\n", + "\n", + "quartiles = [Q1, med, Q2]\n", + "\n", + "print(\"(Q1, median, Q3): {}\".format(quartiles))" + ] + }, + { + "cell_type": "code", + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "num_list = [28, 12, 8, 27, 16, 31, 14, 13, 19, 1, 1, 22, 13]\n", + "\n", + "def quantiles_func(x):\n", + " med, median_indices = median(x)\n", + " Q2, Q1_indices = median(x[:median_indices[0]])\n", + " Q1, Q2_indices = median(x[median_indices[-1] + 1:])\n", + " quartiles = [Q1, med, Q2]\n", + " return \"(Q1, median, Q3): {}\".format(quartiles)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'(Q1, median, Q3): [13.0, 14, 21.5]'" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quartiles = quantiles_func(num_list)\n", + "quartiles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 3.- Now, calculate the frequency distribution.\n" + "## Challenge 3\n", + "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n", + "#### 1.- Sort the values and plot them. What do you see?" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "\n", + "roll_hundred = pd.read_csv('/Users/celinaagostinho/Desktop/lab-understanding-descriptive-stats/data/roll_the_dice_hundred.csv')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Unnamed: 0 roll value\n", + "0 0 0 1\n", + "1 1 1 2\n", + "2 2 2 6\n", + "3 3 3 1\n", + "4 4 4 6\n", + ".. ... ... ...\n", + "95 95 95 4\n", + "96 96 96 6\n", + "97 97 97 1\n", + "98 98 98 3\n", + "99 99 99 6\n", + "\n", + "[100 rows x 3 columns]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roll_hundred" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "roll_hundred.drop('Unnamed: 0', axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "roll_hundred = roll_hundred.sort_values(by='value')\n", + "roll_hundred_hist = roll_hundred.groupby('value')['value'].count()\n", + "\n", + "x = roll_hundred_hist.index\n", + "y = roll_hundred_hist.values\n", + "\n", + "plt.bar(x,y)\n", + "\n", + "plt.show()\n" ] }, { @@ -860,7 +1083,66 @@ "metadata": {}, "outputs": [], "source": [ - "# your code here" + "\"\"\"\n", + "the most frequent values are 4 and 6\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.74" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "mean_func(roll_hundred['value'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 12, 2: 17, 6: 23, 5: 12, 4: 22, 3: 14}" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequencies = freq_func(roll_hundred['value'])\n", + "frequencies" ] }, { @@ -872,11 +1154,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "# histogram of what?\n", + "\n", + "x = list(frequencies.keys())\n", + "y = list(frequencies.values())\n", + "\n", + "sns.barplot(x, y)\n", + "plt.title(\"Bar plot of dice rolls\")\n", + "#plt.xlim([0,7])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# xticks are not aligned with bars\n", + "\n", + "x = roll_hundred['value']\n", + "\n", + "sns.distplot(x, kde=False, hist_kws={\"rwidth\":0.75}, bins=6)\n", + "plt.title(\"Histogram of dice rolls\")\n", + "plt.xlim([0,7])\n", + "plt.show()" ] }, { @@ -886,7 +1219,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "Mean is close to 4, most frequent values are 4 and 6\n", "\"\"\"" ] }, @@ -899,11 +1232,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 98, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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rollvalue
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\n", + "
" + ], + "text/plain": [ + " roll value\n", + "0 0 5\n", + "1 1 6\n", + "2 2 1\n", + "3 3 6\n", + "4 4 5" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "roll_thousand = pd.read_csv('/Users/celinaagostinho/Desktop/lab-understanding-descriptive-stats/data/roll_the_dice_thousand.csv')\n", + "\n", + "roll_thousand.drop('Unnamed: 0', axis=1, inplace=True)\n", + "\n", + "roll_thousand.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "roll_thousand = roll_thousand.sort_values(by='value')\n", + "roll_thousand_hist = roll_thousand.groupby('value')['value'].count()\n", + "\n", + "x = roll_thousand_hist.index\n", + "y = roll_thousand_hist.values\n", + "\n", + "plt.bar(x,y)\n", + "\n", + "plt.show()" ] }, { @@ -913,7 +1351,11 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "\n", + "frequencies are balanced between the different possible values\n", + "\n", + "Roughly same frequency for each value with increase in dice rolls (larger sample of dice rolls)\n", + "\n", "\"\"\"" ] }, @@ -929,11 +1371,232 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "0 68.0\n", + "1 12.0\n", + "2 45.0\n", + "3 38.0\n", + "4 49.0\n", + ".. ...\n", + "995 27.0\n", + "996 47.0\n", + "997 53.0\n", + "998 33.0\n", + "999 31.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "ages = pd.read_csv('/Users/celinaagostinho/Desktop/lab-understanding-descriptive-stats/data/ages_population.csv')\n", + "ages" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{68.0: 3,\n", + " 12.0: 11,\n", + " 45.0: 29,\n", + " 38.0: 30,\n", + " 49.0: 19,\n", + " 27.0: 25,\n", + " 39.0: 45,\n", + " 42.0: 32,\n", + " 33.0: 22,\n", + " 30.0: 34,\n", + " 25.0: 19,\n", + " 44.0: 23,\n", + " 53.0: 12,\n", + " 46.0: 23,\n", + " 50.0: 16,\n", + " 22.0: 16,\n", + " 6.0: 2,\n", + " 29.0: 26,\n", + " 35.0: 33,\n", + " 28.0: 20,\n", + " 26.0: 23,\n", + " 60.0: 4,\n", + " 41.0: 36,\n", + " 52.0: 14,\n", + " 32.0: 30,\n", + " 23.0: 17,\n", + " 15.0: 8,\n", + " 40.0: 27,\n", + " 63.0: 7,\n", + " 31.0: 24,\n", + " 34.0: 29,\n", + " 61.0: 2,\n", + " 64.0: 2,\n", + " 37.0: 30,\n", + " 56.0: 15,\n", + " 14.0: 10,\n", + " 13.0: 6,\n", + " 51.0: 9,\n", + " 36.0: 31,\n", + " 18.0: 7,\n", + " 48.0: 19,\n", + " 58.0: 7,\n", + " 20.0: 13,\n", + " 54.0: 13,\n", + " 19.0: 11,\n", + " 62.0: 4,\n", + " 55.0: 13,\n", + " 21.0: 14,\n", + " 43.0: 32,\n", + " 17.0: 10,\n", + " 7.0: 1,\n", + " 47.0: 17,\n", + " 1.0: 2,\n", + " 16.0: 8,\n", + " 24.0: 18,\n", + " 59.0: 8,\n", + " 57.0: 7,\n", + " 8.0: 5,\n", + " 67.0: 4,\n", + " 2.0: 2,\n", + " 66.0: 3,\n", + " 4.0: 1,\n", + " 73.0: 1,\n", + " 82.0: 1,\n", + " 70.0: 1,\n", + " 5.0: 2,\n", + " 71.0: 1,\n", + " 9.0: 2,\n", + " 69.0: 1,\n", + " 11.0: 3,\n", + " 10.0: 3,\n", + " 65.0: 2}" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequencies = freq_func(ages['observation'])\n", + "frequencies" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "ages_plot = ages.sort_values(by='observation')\n", + "ages_plot = ages_plot.groupby('observation')['observation'].count()\n", + "\n", + "x = ages_plot.index\n", + "y = ages_plot.values\n", + "\n", + "plt.bar(x,y)\n", + "\n", + "plt.show()" ] }, { @@ -945,11 +1608,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 105, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The mean is 36.56\n", + "The standard deviation is 12.81008977329979\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "print('The mean is', np.mean(ages['observation']))\n", + "print('The standard deviation is', np.std(ages['observation']))" ] }, { @@ -959,7 +1634,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "They are consistent with the ranges shown in the plot\n", "\"\"\"" ] }, @@ -972,11 +1647,142 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 106, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "0 25.0\n", + "1 31.0\n", + "2 29.0\n", + "3 31.0\n", + "4 29.0\n", + ".. ...\n", + "995 26.0\n", + "996 22.0\n", + "997 21.0\n", + "998 19.0\n", + "999 28.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "ages2 = pd.read_csv('/Users/celinaagostinho/Desktop/lab-understanding-descriptive-stats/data/ages_population2.csv')\n", + "ages2" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "frequencies = freq_func(ages2['observation'])\n", + "\n", + "ages2_plot = ages2.sort_values(by='observation')\n", + "ages2_plot = ages2_plot.groupby('observation')['observation'].count()\n", + "\n", + "x = ages2_plot.index\n", + "y = ages2_plot.values\n", + "\n", + "plt.bar(x,y)\n", + "\n", + "plt.show()" ] }, { @@ -993,7 +1799,10 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "The ages are in intervals\n", + "The mean is around 28, rather than 37.\n", + "The standard deviation is not wide.\n", + "\n", "\"\"\"" ] }, @@ -1006,11 +1815,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The mean is 27.155\n", + "The standard deviation is 2.9683286543103704\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "print('The mean is', np.mean(ages2['observation']))\n", + "print('The standard deviation is', np.std(ages2['observation']))" ] }, { @@ -1020,7 +1841,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "The mean and standard deviation are consistent with what was observed before.\n", "\"\"\"" ] }, @@ -1036,11 +1857,142 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "0 21.0\n", + "1 21.0\n", + "2 24.0\n", + "3 31.0\n", + "4 54.0\n", + ".. ...\n", + "995 16.0\n", + "996 55.0\n", + "997 30.0\n", + "998 35.0\n", + "999 43.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "ages3 = pd.read_csv('/Users/celinaagostinho/Desktop/lab-understanding-descriptive-stats/data/ages_population3.csv')\n", + "ages3" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "frequencies = freq_func(ages3['observation'])\n", + "\n", + "ages3_plot = ages3.sort_values(by='observation')\n", + "ages3_plot = ages3_plot.groupby('observation')['observation'].count()\n", + "\n", + "x = ages3_plot.index\n", + "y = ages3_plot.values\n", + "\n", + "plt.bar(x,y)\n", + "\n", + "plt.show()" ] }, { @@ -1052,11 +2004,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The mean is 41.989\n", + "The standard deviation is 16.136631587788084\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "print('The mean is', np.mean(ages3['observation']))\n", + "print('The standard deviation is', np.std(ages3['observation']))" ] }, { @@ -1066,7 +2030,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "There's a wider range of values, and a bimodal distribution, which inflates the mean.\n", "\"\"\"" ] }, @@ -1079,11 +2043,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 120, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The lower quartile boundary is 30.0\n", + "The median is 40.0\n", + "The upper quartile boundary is 53.0\n", + "The mean is 41.989\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "#ages3.quantile()\n", + "\n", + "q1 = ages3['observation'].quantile(q=0.25)\n", + "median = ages3['observation'].quantile(q=0.5)\n", + "q3 = ages3['observation'].quantile(q=0.75)\n", + "mean = ages3['observation'].mean()\n", + "\n", + "print('The lower quartile boundary is', q1)\n", + "print('The median is', median)\n", + "print('The upper quartile boundary is', q3)\n", + "print('The mean is', mean)" ] }, { @@ -1093,7 +2080,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "There's a difference of less than one years between the mean and the median.\n", "\"\"\"" ] }, @@ -1106,11 +2093,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 123, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentile 70 is 50.0\n", + "Percentile 80 is 57.0\n", + "Percentile 90 is 67.0\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "q7 = ages3['observation'].quantile(q=0.7)\n", + "q8 = ages3['observation'].quantile(q=0.8)\n", + "q9 = ages3['observation'].quantile(q=0.9)\n", + "\n", + "print('Percentile 70 is', q7)\n", + "print('Percentile 80 is', q8)\n", + "print('Percentile 90 is', q9)" ] }, { @@ -1120,7 +2125,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "10% of the sample is between 57 and 67 years old.\n", "\"\"\"" ] }, From a4e1e8ae38a7d21f68e83c77c74fe8f306bca0aa Mon Sep 17 00:00:00 2001 From: cfmago Date: Mon, 17 Aug 2020 09:19:22 +0100 Subject: [PATCH 4/4] corrected a few things --- your-code/main.ipynb | 253 +++++++++++++++++++++---------------------- 1 file changed, 121 insertions(+), 132 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index d8b4abf..1fea71f 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -101,11 +101,11 @@ " \n", " \n", " 0\n", - " 3\n", + " 5\n", " \n", " \n", " 1\n", - " 6\n", + " 4\n", " \n", " \n", " 2\n", @@ -113,31 +113,31 @@ " \n", " \n", " 3\n", - " 5\n", + " 2\n", " \n", " \n", " 4\n", - " 6\n", + " 4\n", " \n", " \n", " 5\n", - " 5\n", + " 3\n", " \n", " \n", " 6\n", - " 4\n", + " 3\n", " \n", " \n", " 7\n", - " 1\n", + " 2\n", " \n", " \n", " 8\n", - " 3\n", + " 5\n", " \n", " \n", " 9\n", - " 2\n", + " 6\n", " \n", " \n", "\n", @@ -145,19 +145,19 @@ ], "text/plain": [ " result\n", - "0 3\n", - "1 6\n", + "0 5\n", + "1 4\n", "2 1\n", - "3 5\n", - "4 6\n", - "5 5\n", - "6 4\n", - "7 1\n", - "8 3\n", - "9 2" + "3 2\n", + "4 4\n", + "5 3\n", + "6 3\n", + "7 2\n", + "8 5\n", + "9 6" ] }, - "execution_count": 9, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -214,39 +214,39 @@ " 1\n", " \n", " \n", - " 7\n", - " 1\n", + " 3\n", + " 2\n", " \n", " \n", - " 9\n", + " 7\n", " 2\n", " \n", " \n", - " 0\n", + " 5\n", " 3\n", " \n", " \n", - " 8\n", + " 6\n", " 3\n", " \n", " \n", - " 6\n", + " 1\n", " 4\n", " \n", " \n", - " 3\n", - " 5\n", + " 4\n", + " 4\n", " \n", " \n", - " 5\n", + " 0\n", " 5\n", " \n", " \n", - " 1\n", - " 6\n", + " 8\n", + " 5\n", " \n", " \n", - " 4\n", + " 9\n", " 6\n", " \n", " \n", @@ -256,18 +256,18 @@ "text/plain": [ " result\n", "2 1\n", - "7 1\n", - "9 2\n", - "0 3\n", - "8 3\n", - "6 4\n", - "3 5\n", - "5 5\n", - "1 6\n", - "4 6" + "3 2\n", + "7 2\n", + "5 3\n", + "6 3\n", + "1 4\n", + "4 4\n", + "0 5\n", + "8 5\n", + "9 6" ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -278,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -314,47 +314,47 @@ " \n", " \n", " 1\n", - " 7\n", - " 1\n", + " 3\n", + " 2\n", " \n", " \n", " 2\n", - " 9\n", + " 7\n", " 2\n", " \n", " \n", " 3\n", - " 0\n", + " 5\n", " 3\n", " \n", " \n", " 4\n", - " 8\n", + " 6\n", " 3\n", " \n", " \n", " 5\n", - " 6\n", + " 1\n", " 4\n", " \n", " \n", " 6\n", - " 3\n", - " 5\n", + " 4\n", + " 4\n", " \n", " \n", " 7\n", - " 5\n", + " 0\n", " 5\n", " \n", " \n", " 8\n", - " 1\n", - " 6\n", + " 8\n", + " 5\n", " \n", " \n", " 9\n", - " 4\n", + " 9\n", " 6\n", " \n", " \n", @@ -364,18 +364,18 @@ "text/plain": [ " index result\n", "0 2 1\n", - "1 7 1\n", - "2 9 2\n", - "3 0 3\n", - "4 8 3\n", - "5 6 4\n", - "6 3 5\n", - "7 5 5\n", - "8 1 6\n", - "9 4 6" + "1 3 2\n", + "2 7 2\n", + "3 5 3\n", + "4 6 3\n", + "5 1 4\n", + "6 4 4\n", + "7 0 5\n", + "8 8 5\n", + "9 9 6" ] }, - "execution_count": 12, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -388,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -422,7 +422,7 @@ " \n", " \n", " 1\n", - " 1\n", + " 2\n", " \n", " \n", " 2\n", @@ -442,7 +442,7 @@ " \n", " \n", " 6\n", - " 5\n", + " 4\n", " \n", " \n", " 7\n", @@ -450,7 +450,7 @@ " \n", " \n", " 8\n", - " 6\n", + " 5\n", " \n", " \n", " 9\n", @@ -463,18 +463,18 @@ "text/plain": [ " result\n", "0 1\n", - "1 1\n", + "1 2\n", "2 2\n", "3 3\n", "4 3\n", "5 4\n", - "6 5\n", + "6 4\n", "7 5\n", - "8 6\n", + "8 5\n", "9 6" ] }, - "execution_count": 13, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -487,12 +487,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -518,12 +518,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -554,12 +554,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ "
" ] @@ -613,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -626,16 +626,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3.6" + "3.5" ] }, - "execution_count": 33, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -646,16 +646,16 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3.6" + "3.5" ] }, - "execution_count": 34, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -674,20 +674,9 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 20, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: 2, 2: 1, 3: 2, 4: 1, 5: 2, 6: 2}" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# your code here\n", "# value_counts()\n", @@ -703,33 +692,32 @@ " result_dict[result] += 1 \n", " return result_dict\n", "\n", - "freq_func(r['result'])" + "result_dict = freq_func(r['result'])\n" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1.6666666666666667" + "{1: 1, 2: 2, 3: 2, 4: 2, 5: 2, 6: 1}" ] }, - "execution_count": 36, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "\n", - "mean_func(result_dict.values())\n" + "result_dict" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -738,13 +726,14 @@ "1.6666666666666667" ] }, - "execution_count": 38, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "mean(result_dict.values())\n" + "\n", + "mean_func(result_dict.values())\n" ] }, { @@ -757,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -778,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -787,7 +776,7 @@ "(3.5, [5, 4])" ] }, - "execution_count": 42, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -798,22 +787,22 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(3.5, [5, 4])" + "3.5" ] }, - "execution_count": 43, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "median(r['result'])" + "np.median(r['result'])" ] }, { @@ -825,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -841,10 +830,10 @@ "\n", "num_list = [28, 12, 8, 27, 16, 31, 14, 13, 19, 1, 1, 22, 13]\n", "\n", - "med, median_indices = median(num_list)\n", + "med, median_indices = median_func(num_list)\n", "\n", - "Q2, Q1_indices = median(num_list[:median_indices[0]])\n", - "Q1, Q2_indices = median(num_list[median_indices[-1] + 1:])\n", + "Q2, Q1_indices = median_func(num_list[:median_indices[0]])\n", + "Q1, Q2_indices = median_func(num_list[median_indices[-1] + 1:])\n", "\n", "quartiles = [Q1, med, Q2]\n", "\n", @@ -853,23 +842,23 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "num_list = [28, 12, 8, 27, 16, 31, 14, 13, 19, 1, 1, 22, 13]\n", "\n", "def quantiles_func(x):\n", - " med, median_indices = median(x)\n", - " Q2, Q1_indices = median(x[:median_indices[0]])\n", - " Q1, Q2_indices = median(x[median_indices[-1] + 1:])\n", + " med, median_indices = median_func(x)\n", + " Q2, Q1_indices = median_func(x[:median_indices[0]])\n", + " Q1, Q2_indices = median_func(x[median_indices[-1] + 1:])\n", " quartiles = [Q1, med, Q2]\n", " return \"(Q1, median, Q3): {}\".format(quartiles)" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -878,7 +867,7 @@ "'(Q1, median, Q3): [13.0, 14, 21.5]'" ] }, - "execution_count": 47, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -899,7 +888,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -910,7 +899,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1028,7 +1017,7 @@ "[100 rows x 3 columns]" ] }, - "execution_count": 51, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }