{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "University of Regina Professor's salary.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "OrpM1wnUGzsO", "colab_type": "text" }, "source": [ "# Playing with the University of Regina Professor's salary using python (Data Science)." ] }, { "cell_type": "markdown", "metadata": { "id": "2jPzauAxHZYq", "colab_type": "text" }, "source": [ "**The dataset for this tutorial can be found here [University of Regina](https://www.uregina.ca/orp/assets/statistics/2018-07-11-employees-earning-100K-plus.pdf). Just download this pdf and convert this to excel with comma separated values (.CSV). To do this just go to youtube you'll find some great tutorials out there.**" ] }, { "cell_type": "markdown", "metadata": { "id": "ivosHEdsJTmn", "colab_type": "text" }, "source": [ "![alt text](http://i63.tinypic.com/i1b82b.jpg)\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "NrsnJt2Y2q0Y", "colab_type": "code", "outputId": "942d05d3-a420-400f-8ae5-c6a20279938e", "colab": { "resources": { "http://localhost:8080/nbextensions/google.colab/files.js": { "data": 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"ok": true, "headers": [ [ "content-type", "application/javascript" ] ], "status": 200, "status_text": "" } }, "base_uri": "https://localhost:8080/", "height": 75 } }, "source": [ "from google.colab import files\n", "uploaded = files.upload()" ], "execution_count": 4, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Saving UofR.csv to UofR.csv\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "nkgKRFhlKlnx", "colab_type": "text" }, "source": [ "\n", "\n", "---\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "Zfe1DDZX26zb", "colab_type": "code", "colab": {} }, "source": [ "import pandas as pd\n", "import numpy as np\n", "import io" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "E8ciYCwAKmwi", "colab_type": "text" }, "source": [ "\n", "\n", "---\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "vKN2B3r022Ip", "colab_type": "code", "colab": {} }, "source": [ "df2 = pd.read_csv(io.BytesIO(uploaded['UofR.csv']))\n", "# Dataset is now stored in a Pandas Dataframe" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "6N2WM65kKn29", "colab_type": "text" }, "source": [ "\n", "\n", "---\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "dkyzCLPS5LIw", "colab_type": "code", "outputId": "1b3823bf-e99a-48ee-ea56-b51b50e49cd7", "colab": { "base_uri": "https://localhost:8080/", "height": 202 } }, "source": [ "df2.head()" ], "execution_count": 7, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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1BrighamR. Mark174,973.00NaNNaN174,973.00
2BrittoSarah133,292.004,150.00NaN137,442.00
3BrotheridgeNeil164,260.00NaNNaN164,260.00
4BrownDouglas124,952.00NaNNaN124,952.00
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" ], "text/plain": [ " Last Name First Name ... Market\\nSupplement Total\n", "0 Bredohl Thomas ... NaN 135,110.00\n", "1 Brigham R. Mark ... NaN 174,973.00\n", "2 Britto Sarah ... NaN 137,442.00\n", "3 Brotheridge Neil ... NaN 164,260.00\n", "4 Brown Douglas ... NaN 124,952.00\n", "\n", "[5 rows x 6 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 7 } ] }, { "cell_type": "markdown", "metadata": { "id": "svhMuiv7KorZ", "colab_type": "text" }, "source": [ "\n", "\n", "---\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "6Jo7BGZy5eC4", "colab_type": "code", "colab": {} }, "source": [ "df2 = df2.replace(np.NaN, 0)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "U3IBf-88Foe0", "colab_type": "code", "outputId": "4cdc238e-001f-4a73-b1bc-91ad8282b8dc", "colab": { "base_uri": "https://localhost:8080/", "height": 355 } }, "source": [ "df2.head(10)" ], "execution_count": 15, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Last NameFirst NameCurrent\n", "SalaryAdministrative/\n", "Research StipendMarket\n", "SupplementTotal
0BredohlThomas135,110.0000135,110.00
1BrighamR. Mark174,973.0000174,973.00
2BrittoSarah133,292.004,150.000137,442.00
3BrotheridgeNeil164,260.0000164,260.00
4BrownDouglas124,952.0000124,952.00
5BrownJanine96,824.00011,180.00108,004.00
6BrunoPaul109,851.0000109,851.00
7BuehlerAlex109,761.0000109,761.00
8BundockChris106,571.0000106,571.00
9BurlinghamClay106,571.0000106,571.00
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" ], "text/plain": [ " Last Name First Name ... Market\\nSupplement Total\n", "0 Bredohl Thomas ... 0 135,110.00\n", "1 Brigham R. Mark ... 0 174,973.00\n", "2 Britto Sarah ... 0 137,442.00\n", "3 Brotheridge Neil ... 0 164,260.00\n", "4 Brown Douglas ... 0 124,952.00\n", "5 Brown Janine ... 11,180.00 108,004.00\n", "6 Bruno Paul ... 0 109,851.00\n", "7 Buehler Alex ... 0 109,761.00\n", "8 Bundock Chris ... 0 106,571.00\n", "9 Burlingham Clay ... 0 106,571.00\n", "\n", "[10 rows x 6 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 15 } ] }, { "cell_type": "markdown", "metadata": { "id": "7q5SXExfKqKt", "colab_type": "text" }, "source": [ "\n", "\n", "---\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "UvdHv1KzJxqV", "colab_type": "text" }, "source": [ "**Thanks to StackOverflow for helping me with the capatilize() - which capitalizes the first letter of the word in whichever format you write, whereas the str.contain() - is used to match the string that we are looking for. So this acts as a search engine, wherein you can search for the name of the prof that you want and you'll get your answer. Make sure that you enter the first name of the professor that you are looking for.**" ] }, { "cell_type": "code", "metadata": { "id": "HKXB-leQ_Tfl", "colab_type": "code", "outputId": "88565a10-fc03-4f30-f8ed-499f53e07f95", "colab": { "base_uri": "https://localhost:8080/", "height": 97 } }, "source": [ "name = input(\"Enter the first name of the prof that you want to search\")\n", "name = name.capitalize()\n", "\n", "df2[df2['First Name'].str.contains(name)]" ], "execution_count": 16, "outputs": [ { "output_type": "stream", "text": [ "Enter the first name of the prof that you want to searchCortney\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "
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Last NameFirst NameCurrent\n", "SalaryAdministrative/\n", "Research StipendMarket\n", "SupplementTotal
14ButzCortney164,008.0010,000.000174,008.00
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" ], "text/plain": [ " Last Name First Name ... Market\\nSupplement Total\n", "14 Butz Cortney ... 0 174,008.00\n", "\n", "[1 rows x 6 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 16 } ] }, { "cell_type": "markdown", "metadata": { "id": "7yXRElXOKrcH", "colab_type": "text" }, "source": [ "\n", "\n", "---\n", "\n" ] } ] }