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matplotlib.pyplot as plt\n", + "import random" ] }, { @@ -29,11 +33,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "\n", + "def dice(n):\n", + " rolls = []\n", + " for i in range(n):\n", + " value = random.randint(1, 6)\n", + " rolls.append(value)\n", + " return rolls\n", + "\n", + "data = pd.DataFrame(dice(10), columns = ['number'])\n", + "\n", + "#data" ] }, { @@ -45,11 +60,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "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", + "data.plot(kind = 'bar')\n", + "plt.show()" ] }, { @@ -61,21 +101,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "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", + "data.plot(kind = 'hist')\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nOn the first graph you have the index as rows and the value as columns, and on the second one you have the value as\\ncolumns and the rows the amount of times that value came out.\\n\\n'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", - "your comments here\n", + "On the first graph you have the index as rows and the value as columns, and on the second one you have the value as\n", + "columns and the rows the amount of times that value came out.\n", + "\n", "\"\"\"" ] }, @@ -91,11 +159,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "\n", + "def mean(col):\n", + " return col.sum()/col.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "number 4.3\n", + "dtype: float64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.3" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean(data['number'])" ] }, { @@ -107,11 +219,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1 2\n", + "2 1\n", + "5 3\n", + "6 4\n", + "Name: number, dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "pd.value_counts(data['number']).sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.3" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean(data['number'])" ] }, { @@ -122,13 +270,64 @@ "**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": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "def median(col):\n", + " n = col.count()\n", + " index = n // 2 \n", + " if n % 2 != 0: #1\n", + " return sorted(col)[ındex]\n", + " else: #2\n", + " return sum(sorted(col)[index - 1 : index + 1]) / 2\n", + "np.median(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(data['number'])" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "\"\"\"\n", + "We start by finding the middle element, this differs because depends if the column as even or odd numbers.\n", + "Then we code the option for the even (#2) and for the odd(#1)\n", + "\"\"\"" ] }, { @@ -140,11 +339,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "\n", + "def quartiles(col):\n", + " n = col.count()\n", + "\n", + " \n", + " index_quart = (n // 2) // 2\n", + " if (n // 2) % 2 != 0:\n", + " print('Q1 is ', sorted(col)[index_quart]) \n", + " else:\n", + " print('Q1 is ', sum(sorted(col)[index_quart - 1 : index_quart + 1]) / 2)\n", + " \n", + " \n", + " index = n // 2 \n", + " if n % 2 != 0:\n", + " print('Q2 is ', sorted(col)[index])\n", + " else:\n", + " print('Q2 is ', sum(sorted(col)[index - 1 : index + 1]) / 2)\n", + " \n", + " \n", + " index_quart = int((n // 2) * 1.5)\n", + " if ((n // 2)) % 2 != 0:\n", + " print('Q3 is ', sorted(col)[index_quart]) \n", + " else:\n", + " print('Q3 is ', sum(sorted(col)[index_quart - 1 : index_quart + 1]) / 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Q1 is 2\n", + "Q2 is 5.0\n", + "Q3 is 6\n" + ] + } + ], + "source": [ + "quartiles(data['number'])" ] }, { @@ -158,11 +400,111 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "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" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "dice = pd.read_csv('roll_the_dice_hundred.csv')\n", + "dice.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dice.plot(x = 'roll', y = 'value', kind = 'hist')\n", + "plt.show()" ] }, { @@ -171,9 +513,7 @@ "metadata": {}, "outputs": [], "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "#By doing this I'm able to see that theres more frequency towards the higher numbers" ] }, { @@ -185,11 +525,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3.74" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "mean(dice['value'])" ] }, { @@ -201,11 +554,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1 12\n", + "2 17\n", + "3 14\n", + "4 22\n", + "5 12\n", + "6 23\n", + "Name: value, dtype: int64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "pd.value_counts(dice['value']).sort_index()" ] }, { @@ -217,38 +589,144 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "dice.plot(x = 'roll', y = 'value', kind = 'hist')\n", + "plt.show()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 34, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Unnamed: 0 roll value\n", + "0 0 0 5\n", + "1 1 1 6\n", + "2 2 2 1\n", + "3 3 3 6\n", + "4 4 4 5" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?" + "# your code here\n", + "\n", + "roll_thousand = pd.read_csv('roll_the_dice_thousand.csv')\n", + "roll_thousand.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pop.plot(kind = 'hist')\n", + "plt.show()" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "\"\"\"\n", + "We can see that the std looks to be around 10 and the mean will be between 30-40 range\n", + "\"\"\"" ] }, { @@ -290,11 +868,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36.56\n", + "12.81008977329979\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "print(mean(pop['observation']))\n", + "print(np.std(pop['observation']))" ] }, { @@ -304,7 +894,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "It appears that I wasn't far off eheh :)\n", "\"\"\"" ] }, @@ -317,11 +907,100 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "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" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "pop2 = pd.read_csv('ages_population2.csv')\n", + "pop2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pop2.plot(kind = 'hist')\n", + "plt.show()" ] }, { @@ -333,12 +1012,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nYes, there seems to be a lower dispersion between the values compared to the before graph.\\n'" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", - "your comments here\n", + "Yes, there seems to be a lower dispersion between the values compared to the before graph.\n", "\"\"\"" ] }, @@ -351,21 +1041,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27.155\n", + "2.9683286543103704\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "print(mean(pop2['observation']))\n", + "print(np.std(pop2['observation']))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nWe can observe a lower mean and lower std\\n'" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", - "your comments here\n", + "We can observe a lower mean and lower std\n", "\"\"\"" ] }, @@ -381,13 +1094,86 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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observation
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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" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "pop3 = pd.read_csv('ages_population3.csv')\n", + "pop3.head()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -397,21 +1183,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "41.989\n", + "16.136631587788084\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "print(mean(pop3['observation']))\n", + "print(np.std(pop3['observation']))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pop3.plot(kind = 'hist')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nWe can see that the data is more BIAS to the right \\n'" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", - "your comments here\n", + "We can see that the data is more BIAS to the right \n", "\"\"\"" ] }, @@ -424,11 +1256,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "41.989\n", + "30.0\n", + "40.0\n", + "53.0\n", + "77.0\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "print(mean(pop3['observation']))\n", + "print(pop3['observation'].quantile(0.25))\n", + "print(pop3['observation'].quantile(0.5))\n", + "print(pop3['observation'].quantile(0.75))\n", + "print(pop3['observation'].quantile(1))" ] }, { @@ -436,11 +1286,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] + "source": [] }, { "cell_type": "markdown", @@ -451,11 +1297,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n", + "22.0\n", + "67.0\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "\n", + "print(pop3['observation'].quantile(0))\n", + "print(pop3['observation'].quantile(0.1))\n", + "print(pop3['observation'].quantile(0.9))" ] }, { @@ -463,11 +1323,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" - ] + "source": [] }, { "cell_type": "markdown", @@ -500,9 +1356,9 @@ ], "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 +1370,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/your-code/roll_the_dice_hundred.csv b/your-code/roll_the_dice_hundred.csv new file mode 100644 index 0000000..50975a2 --- /dev/null +++ b/your-code/roll_the_dice_hundred.csv @@ -0,0 +1,101 @@ +,roll,value +0,0,1 +1,1,2 +2,2,6 +3,3,1 +4,4,6 +5,5,5 +6,6,2 +7,7,2 +8,8,4 +9,9,1 +10,10,5 +11,11,6 +12,12,5 +13,13,4 +14,14,5 +15,15,4 +16,16,4 +17,17,6 +18,18,2 +19,19,4 +20,20,4 +21,21,6 +22,22,3 +23,23,6 +24,24,6 +25,25,4 +26,26,1 +27,27,4 +28,28,4 +29,29,2 +30,30,6 +31,31,5 +32,32,5 +33,33,2 +34,34,3 +35,35,6 +36,36,6 +37,37,2 +38,38,3 +39,39,6 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