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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,155 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from datascience import *\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "%matplotlib inline\n", | ||
| "import matplotlib.pyplot as plots\n", | ||
| "plots.style.use('fivethirtyeight')" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Lecture 22" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Python " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# write the sum_up_to(k) function - can use a for loop\n", | ||
| "\n", | ||
| "\n", | ||
| "\n", | ||
| "\n", | ||
| "\n", | ||
| "\n", | ||
| " " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# write the sum_up_to(k) function - without a for loop\n", | ||
| "\n", | ||
| "\n", | ||
| "\n", | ||
| "\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Tables" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/html": [ | ||
| "<table border=\"1\" class=\"dataframe\">\n", | ||
| " <thead>\n", | ||
| " <tr>\n", | ||
| " <th>Year Type</th> <th>Year</th> <th>Organization Group Code</th> <th>Organization Group</th> <th>Department Code</th> <th>Department</th> <th>Union Code</th> <th>Union</th> <th>Job Family Code</th> <th>Job Family</th> <th>Job Code</th> <th>Job</th> <th>Employee Identifier</th> <th>Salaries</th> <th>Overtime</th> <th>Other Salaries</th> <th>Total Salary</th> <th>Retirement</th> <th>Health/Dental</th> <th>Other Benefits</th> <th>Total Benefits</th> <th>Total Compensation</th>\n", | ||
| " </tr>\n", | ||
| " </thead>\n", | ||
| " <tbody>\n", | ||
| " <tr>\n", | ||
| " <td>Calendar </td> <td>2015</td> <td>2 </td> <td>Public Works, Transportation & Commerce</td> <td>WTR </td> <td>PUC Water Department </td> <td>21 </td> <td>Prof & Tech Engineers - Miscellaneous, Local 21 </td> <td>2400 </td> <td>Lab, Pharmacy & Med Techs</td> <td>2481 </td> <td>Water Qualitytech I/II</td> <td>21538 </td> <td>82146 </td> <td>0 </td> <td>0 </td> <td>82146 </td> <td>16942.2 </td> <td>12340.9 </td> <td>6337.73 </td> <td>35620.8 </td> <td>117767 </td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <td>Calendar </td> <td>2015</td> <td>2 </td> <td>Public Works, Transportation & Commerce</td> <td>DPW </td> <td>General Services Agency - Public Works</td> <td>12 </td> <td>Carpet, Linoleum and Soft Tile Workers, Local 12</td> <td>7300 </td> <td>Journeyman Trade </td> <td>7393 </td> <td>Soft Floor Coverer </td> <td>5459 </td> <td>32165.8 </td> <td>973.19 </td> <td>848.96 </td> <td>33987.9 </td> <td>0 </td> <td>4587.51 </td> <td>2634.42 </td> <td>7221.93 </td> <td>41209.8 </td>\n", | ||
| " </tr>\n", | ||
| " </tbody>\n", | ||
| "</table>\n", | ||
| "<p>... (42987 rows omitted)</p>" | ||
| ], | ||
| "text/plain": [ | ||
| "<IPython.core.display.HTML object>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# load data on SF employees\n", | ||
| "sf = Table.read_table('san_francisco_2015.csv')\n", | ||
| "sf.show(2)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# What is the Mayor's total compensation? \n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# what proportion of people are making more than $100,000?\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.7.3" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,199 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from datascience import *\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "%matplotlib inline\n", | ||
| "import matplotlib.pyplot as plots\n", | ||
| "plots.style.use('fivethirtyeight')" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Lecture 22" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Python " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "55" | ||
| ] | ||
| }, | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "def sum_up_to(k):\n", | ||
| " if k < 0:\n", | ||
| " return \"invalid input fool\"\n", | ||
| " \n", | ||
| " total = 0\n", | ||
| " for i in np.arange(k + 1):\n", | ||
| " total = total + i\n", | ||
| " return total\n", | ||
| "\n", | ||
| "\n", | ||
| "sum_up_to(10)\n", | ||
| " " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "55" | ||
| ] | ||
| }, | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "def sum_up_to2(k):\n", | ||
| " if k < 0:\n", | ||
| " return \"invalid input fool\"\n", | ||
| " \n", | ||
| " return np.sum(np.arange(k + 1))\n", | ||
| "\n", | ||
| "\n", | ||
| "sum_up_to2(10)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Tables" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/html": [ | ||
| "<table border=\"1\" class=\"dataframe\">\n", | ||
| " <thead>\n", | ||
| " <tr>\n", | ||
| " <th>Year Type</th> <th>Year</th> <th>Organization Group Code</th> <th>Organization Group</th> <th>Department Code</th> <th>Department</th> <th>Union Code</th> <th>Union</th> <th>Job Family Code</th> <th>Job Family</th> <th>Job Code</th> <th>Job</th> <th>Employee Identifier</th> <th>Salaries</th> <th>Overtime</th> <th>Other Salaries</th> <th>Total Salary</th> <th>Retirement</th> <th>Health/Dental</th> <th>Other Benefits</th> <th>Total Benefits</th> <th>Total Compensation</th>\n", | ||
| " </tr>\n", | ||
| " </thead>\n", | ||
| " <tbody>\n", | ||
| " <tr>\n", | ||
| " <td>Calendar </td> <td>2015</td> <td>2 </td> <td>Public Works, Transportation & Commerce</td> <td>WTR </td> <td>PUC Water Department </td> <td>21 </td> <td>Prof & Tech Engineers - Miscellaneous, Local 21 </td> <td>2400 </td> <td>Lab, Pharmacy & Med Techs</td> <td>2481 </td> <td>Water Qualitytech I/II</td> <td>21538 </td> <td>82146 </td> <td>0 </td> <td>0 </td> <td>82146 </td> <td>16942.2 </td> <td>12340.9 </td> <td>6337.73 </td> <td>35620.8 </td> <td>117767 </td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <td>Calendar </td> <td>2015</td> <td>2 </td> <td>Public Works, Transportation & Commerce</td> <td>DPW </td> <td>General Services Agency - Public Works</td> <td>12 </td> <td>Carpet, Linoleum and Soft Tile Workers, Local 12</td> <td>7300 </td> <td>Journeyman Trade </td> <td>7393 </td> <td>Soft Floor Coverer </td> <td>5459 </td> <td>32165.8 </td> <td>973.19 </td> <td>848.96 </td> <td>33987.9 </td> <td>0 </td> <td>4587.51 </td> <td>2634.42 </td> <td>7221.93 </td> <td>41209.8 </td>\n", | ||
| " </tr>\n", | ||
| " </tbody>\n", | ||
| "</table>\n", | ||
| "<p>... (42987 rows omitted)</p>" | ||
| ], | ||
| "text/plain": [ | ||
| "<IPython.core.display.HTML object>" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "sf = Table.read_table('san_francisco_2015.csv')\n", | ||
| "sf.show(2)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "379798.03" | ||
| ] | ||
| }, | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# What is the Mayor's total compensation? \n", | ||
| "sf.where('Job', 'Mayor').select(\"Total Compensation\") # in a Table\n", | ||
| "sf.where('Job', 'Mayor').column(\"Total Compensation\").item(0) # as a number" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 9, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "0.4875898485659122" | ||
| ] | ||
| }, | ||
| "execution_count": 9, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# what proportion of people are making more than $100,000?\n", | ||
| "sf.where('Total Compensation', are.above(100000)).num_rows/sf.num_rows" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.7.3" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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