{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "02rFSC_-6zKz" }, "source": [ "## Assignment 06\n", "### Note:\n", "- For visualization, you should use Matplotlib, seaborn, or Plotly Express \n", "- Use this notebook as your template and follow the instructions\n", "\n", "The first half of this assignment is similar to assignment 04.\n", "\n", "This gives you a chance to refresh.\n", "\n", "The second half is new and gives you a chance to perform additional practices.\n", "\n", "You also get a chance to use some of the Python libraries and techniques.\n", "\n", "The links to the zip file is:\n", "\n", "- https://collegescorecard.ed.gov/data (This web page contains the link to the zip file)\n", "\n", "- https://ed-public-download.app.cloud.gov/downloads/CollegeScorecard_Raw_Data_09012022.zip (The link to the zip file)\n", "\n", "You can run `!wget` command in Colab notebook to retrieve it directly, then run `!unzip` command to extract files (I have provided the cells to do so next for your convenience)\n", "\n", "Your folder structure should look like this in your Colab enviroment:\n", "\n", "- ...\n", "- 'MERGED1996_97_PP.csv',\n", "- 'MERGED2015_16_PP.csv',\n", "- ...\n", "- 'MERGED2017_18_PP.csv'\n", "- ...\n", "\n", "**Note: you should refresh the folder to see all files.**" ] }, { "cell_type": "markdown", "metadata": { "id": "RemUiHBXf5EK" }, "source": [ "## Step 1 - Retrieve the zip file" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IlnBrYreEoY2", "outputId": "b4e7bf52-5594-40c0-98ea-6b3f90a69436" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2022-10-07 14:45:58-- https://ed-public-download.app.cloud.gov/downloads/CollegeScorecard_Raw_Data_09012022.zip\n", "Resolving ed-public-download.app.cloud.gov (ed-public-download.app.cloud.gov)... 3.30.138.208, 160.1.161.208, 2600:1f12:18a:7d01:ad67:f64c:95d6:78ed, ...\n", "Connecting to ed-public-download.app.cloud.gov (ed-public-download.app.cloud.gov)|3.30.138.208|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 410294884 (391M) [application/zip]\n", "Saving to: ‘CollegeScorecard_Raw_Data_09012022.zip’\n", "\n", "CollegeScorecard_Ra 100%[===================>] 391.29M 44.1MB/s in 9.2s \n", "\n", "2022-10-07 14:46:08 (42.6 MB/s) - ‘CollegeScorecard_Raw_Data_09012022.zip’ saved [410294884/410294884]\n", "\n" ] } ], "source": [ "!wget https://ed-public-download.app.cloud.gov/downloads/CollegeScorecard_Raw_Data_09012022.zip" ] }, { "cell_type": "markdown", "metadata": { "id": "mY5iKlaef_E3" }, "source": [ "## Step 2 - Unzip the zip file" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WXcix8Q4EWzN", "outputId": "cdaf7075-844f-4e75-94b9-cb09c7c29285" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Archive: CollegeScorecard_Raw_Data_09012022.zip\n", " extracting: Crosswalks.zip \n", " inflating: data.yaml \n", " inflating: FieldOfStudyData1415_1516_PP.csv \n", " inflating: FieldOfStudyData1516_1617_PP.csv \n", " inflating: FieldOfStudyData1617_1718_PP.csv \n", " inflating: FieldOfStudyData1718_1819_PP.csv \n", " inflating: MERGED1996_97_PP.csv \n", " inflating: MERGED1997_98_PP.csv \n", " inflating: MERGED1998_99_PP.csv \n", " inflating: MERGED1999_00_PP.csv \n", " inflating: MERGED2000_01_PP.csv \n", " inflating: MERGED2001_02_PP.csv \n", " inflating: MERGED2002_03_PP.csv \n", " inflating: MERGED2003_04_PP.csv \n", " inflating: MERGED2004_05_PP.csv \n", " inflating: MERGED2005_06_PP.csv \n", " inflating: MERGED2006_07_PP.csv \n", " inflating: MERGED2007_08_PP.csv \n", " inflating: MERGED2008_09_PP.csv \n", " inflating: MERGED2009_10_PP.csv \n", " inflating: MERGED2010_11_PP.csv \n", " inflating: MERGED2011_12_PP.csv \n", " inflating: MERGED2012_13_PP.csv \n", " inflating: MERGED2013_14_PP.csv \n", " inflating: MERGED2014_15_PP.csv \n", " inflating: MERGED2015_16_PP.csv \n", " inflating: MERGED2016_17_PP.csv \n", " inflating: MERGED2017_18_PP.csv \n", " inflating: MERGED2018_19_PP.csv \n", " inflating: MERGED2019_20_PP.csv \n", " inflating: MERGED2020_21_PP.csv \n", " inflating: Most-Recent-Cohorts-Field-of-Study.csv \n", " inflating: Most-Recent-Cohorts-Institution.csv \n" ] } ], "source": [ "!unzip CollegeScorecard_Raw_Data_09012022.zip" ] }, { "cell_type": "markdown", "metadata": { "id": "3gWAI-4_gCs2" }, "source": [ "## Step 3 - Import Libraries" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "CfSasSG36zK1" }, "outputs": [], "source": [ "#(Write code below)\n", "import pandas as pd\n", "import os" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "iMpA2gPdub69", "outputId": "3e11affd-533e-4673-b089-ce058ee82270" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDINSTNMTUITIONFEE_INyear
1618172927American Indian OIC IncNaN2020
1251157669Empire Beauty School-DixieNaN2020
5117457527Ambria College of NursingNaN2019
2875213631United Lutheran SeminaryNaN2019
6098492689Texas Tech University Health Sciences Center-E...NaN2019
4688444936Turning Point Beauty CollegeNaN2019
5024455354Aveda Arts & Sciences Institute-San AntonioNaN2019
1567169983Kettering University44380.02019
1291159009Grambling State University7683.02019
4687447865Trendsetters School of Beauty & BarberingNaN2020
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID INSTNM \\\n", "1618 172927 American Indian OIC Inc \n", "1251 157669 Empire Beauty School-Dixie \n", "5117 457527 Ambria College of Nursing \n", "2875 213631 United Lutheran Seminary \n", "6098 492689 Texas Tech University Health Sciences Center-E... \n", "4688 444936 Turning Point Beauty College \n", "5024 455354 Aveda Arts & Sciences Institute-San Antonio \n", "1567 169983 Kettering University \n", "1291 159009 Grambling State University \n", "4687 447865 Trendsetters School of Beauty & Barbering \n", "\n", " TUITIONFEE_IN year \n", "1618 NaN 2020 \n", "1251 NaN 2020 \n", "5117 NaN 2019 \n", "2875 NaN 2019 \n", "6098 NaN 2019 \n", "4688 NaN 2019 \n", "5024 NaN 2019 \n", "1567 44380.0 2019 \n", "1291 7683.0 2019 \n", "4687 NaN 2020 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is not a good practice. A lot of repetitions.\n", "# Not recommended\n", "\n", "df_2020 = pd.read_csv(\"/content/MERGED2020_21_PP.csv\", usecols=['UNITID', 'INSTNM', 'TUITIONFEE_IN'])\n", "df_2020[\"year\"] = 2020\n", "df_2019 = pd.read_csv(\"/content/MERGED2019_20_PP.csv\", usecols=['UNITID', 'INSTNM', 'TUITIONFEE_IN'])\n", "df_2019[\"year\"] = 2019\n", "#.... (more code)\n", "# df_2020 = pd.read_csv(\"/content/MERGED2020_21_PP.csv\")\n", "# df_all = pd.concat([df_1996, df_1997, ...., df_2020])\n", "df_all = pd.concat([df_2020, df_2019])\n", "\n", "df_all.sample(10)" ] }, { "cell_type": "markdown", "metadata": { "id": "zNVWs_R9gKrr" }, "source": [ "## Step 4 - Display the current working directory using os.getcwd()\n", "\n", "You would need to import a standard Python library called os which stands for operating system. so place that import statement in the previous cell. Since your notebook and your data files may or may not in the same folder, you want to make sure what the current working folder is and how to access a data file in a different folder." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "duwKxi1L6zK5", "outputId": "2eee998e-d4f3-48fe-faf8-a6ff4bbb9a14" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'/content'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#(Write code below)\n", "os.getcwd()" ] }, { "cell_type": "markdown", "metadata": { "id": "SxFHpsKSgxzv" }, "source": [ "## Step 5 - Get the list of file names\n", "\n", "os library has a method call listdir which generates a list of files in a directory/folder. Use this method to assign the contents (list of file names) of the data folder to a variable and display it. If necessary, you can use ../ construct to traverse to the parent folder and then to another foloder parallel to the current folder\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LMBN0HsY6zLA", "outputId": "6e38f706-5468-4029-d033-ada5c2b96ba1" }, "outputs": [ { "data": { "text/plain": [ "['.config',\n", " 'MERGED2014_15_PP.csv',\n", " 'MERGED2005_06_PP.csv',\n", " 'MERGED2004_05_PP.csv',\n", " 'Most-Recent-Cohorts-Institution.csv',\n", " 'MERGED2000_01_PP.csv',\n", " 'FieldOfStudyData1617_1718_PP.csv',\n", " 'MERGED2019_20_PP.csv',\n", " 'MERGED2015_16_PP.csv',\n", " 'MERGED2018_19_PP.csv',\n", " 'data.yaml',\n", " 'MERGED2007_08_PP.csv',\n", " 'FieldOfStudyData1516_1617_PP.csv',\n", " 'MERGED2008_09_PP.csv',\n", " 'FieldOfStudyData1415_1516_PP.csv',\n", " 'MERGED2003_04_PP.csv',\n", " 'MERGED2017_18_PP.csv',\n", " 'MERGED2016_17_PP.csv',\n", " 'MERGED2012_13_PP.csv',\n", " 'MERGED2006_07_PP.csv',\n", " 'MERGED1996_97_PP.csv',\n", " 'MERGED2002_03_PP.csv',\n", " 'FieldOfStudyData1718_1819_PP.csv',\n", " 'MERGED2013_14_PP.csv',\n", " 'CollegeScorecard_Raw_Data_09012022.zip',\n", " 'MERGED2011_12_PP.csv',\n", " 'MERGED2020_21_PP.csv',\n", " 'Crosswalks.zip',\n", " 'MERGED2009_10_PP.csv',\n", " 'MERGED1997_98_PP.csv',\n", " 'Most-Recent-Cohorts-Field-of-Study.csv',\n", " 'MERGED1999_00_PP.csv',\n", " 'MERGED2010_11_PP.csv',\n", " 'MERGED1998_99_PP.csv',\n", " 'MERGED2001_02_PP.csv',\n", " 'sample_data']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#(Write code below)\n", "name_list = os.listdir()\n", "name_list" ] }, { "cell_type": "markdown", "metadata": { "id": "XtKgf-SMhL0z" }, "source": [ "## Step 6 - Process only the yearly data files\n", "\n", "The folder contains files that are not the yearly data files. Write code to remove the unwanted files from the list variable.\n", "\n", "Note: don't remove/delete these files from the folder in the folder.\n", "For example, use the file extension to only use the csv files or use the name patter - data file name begins with \"MERGED\". You can use the concept of list comprehension to write just *one* line of code as well as using a for loop, your choice." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UCDRqtrf6zLL", "outputId": "754bfd17-99a5-42bc-c12a-03e2bfab4a11" }, "outputs": [ { "data": { "text/plain": [ "[1, 9, 36, 64]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#(Write code below)\n", "# using loop\n", "x = [1,3,6,8]\n", "y = []\n", "for num in x:\n", " y.append(num ** 2)\n", "\n", "y" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OsTZMRfjzpwB", "outputId": "1d0335ea-bd6b-4d4a-b94f-c0add1ff6858" }, "outputs": [ { "data": { "text/plain": [ "[36, 64]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# using loop with condition\n", "x = [1,3,6,8]\n", "y = []\n", "\n", "for num in x:\n", " if num % 2 == 0:\n", " y.append(num ** 2)\n", "\n", "y" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5eISR8FjyzyH", "outputId": "89e807f1-7615-413e-dab4-bb2461ec1d71" }, "outputs": [ { "data": { "text/plain": [ "[1, 9, 36, 64]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# using List comprehension\n", "y = [num **2 for num in x]\n", "y" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "B6k0ES8GzZQ8", "outputId": "e22dc168-3562-498a-dc28-89da130ba6d3" }, "outputs": [ { "data": { "text/plain": [ "[36, 64]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# using List comprehension with condition\n", "y = [num **2 for num in x if num % 2 == 0]\n", "y" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "j08cIjcf0ZCw", "outputId": "6802b89f-edcd-4554-e2a7-ff7be22e4308" }, "outputs": [ { "data": { "text/plain": [ "['MERGED2014_15_PP.csv',\n", " 'MERGED2005_06_PP.csv',\n", " 'MERGED2004_05_PP.csv',\n", " 'MERGED2000_01_PP.csv',\n", " 'MERGED2019_20_PP.csv',\n", " 'MERGED2015_16_PP.csv',\n", " 'MERGED2018_19_PP.csv',\n", " 'MERGED2007_08_PP.csv',\n", " 'MERGED2008_09_PP.csv',\n", " 'MERGED2003_04_PP.csv',\n", " 'MERGED2017_18_PP.csv',\n", " 'MERGED2016_17_PP.csv',\n", " 'MERGED2012_13_PP.csv',\n", " 'MERGED2006_07_PP.csv',\n", " 'MERGED1996_97_PP.csv',\n", " 'MERGED2002_03_PP.csv',\n", " 'MERGED2013_14_PP.csv',\n", " 'MERGED2011_12_PP.csv',\n", " 'MERGED2020_21_PP.csv',\n", " 'MERGED2009_10_PP.csv',\n", " 'MERGED1997_98_PP.csv',\n", " 'MERGED1999_00_PP.csv',\n", " 'MERGED2010_11_PP.csv',\n", " 'MERGED1998_99_PP.csv',\n", " 'MERGED2001_02_PP.csv']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_list = [file_name for file_name in name_list if file_name.startswith(\"MERGED\")]\n", "\n", "yearly_list" ] }, { "cell_type": "markdown", "metadata": { "id": "UbrfZBRThtG8" }, "source": [ "## Step 7 - Load data files\n", " \n", "Now that you have a clean list of the yearly files, you want to loop through them and read them into a dataframe one at a time. You only load six columns: [\"UNITID\", \"INSTNM\", \"STABBR\", \"REGION\", \"ADM_RATE\", \"TUITIONFEE_IN\"].\n", "\n", "You should use \"usecols\" option of Pandas to avoid reading unwanted columns. You also want to add a new column call \"YEAR\" to differentiate the data frames from each other. The YEAR variable should be yyyy format so tht you can convert them into integer. If you use the format yyyy-yy (such as 1997-98 school year), you will not be able to convert them directly to integer. If you use scatter plot, the YEAR needs to be converted to integer or float. \n", "\n", "You would use an empty list and append the yearly dataframes to the list. After all data files are loaded and appended to the list, you would use Pandas to concatenate them into a new single data frame.\n", "\n", "Note: this exercise incorporates many techques we learned before:\n", "\n", "- list (creating an empty, append an item to the list)\n", "- for loop \n", "- read only the needed columns from a file (using usecols option)\n", "- add a new column to a data frame\n", "- concatenate multiple dataframes into a single one\n", "\n", "This exercise may appear challenging but it worths the effort. You will learn a lot and love it. I promise." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "4JleOHyl2QP6", "outputId": "c48119e5-8863-4700-988b-96b964d15857" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'2020'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = \"MERGED2020_21_PP.csv\"\n", "\n", "x[6:10]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rtrB8C_R6zLS", "outputId": "0481b5a0-669e-4385-8a53-3f22a002b3d2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "processing file MERGED1996_97_PP.csv\n", "(7007, 4)\n", "processing file MERGED1997_98_PP.csv\n", "(13941, 4)\n", "processing file MERGED1998_99_PP.csv\n", "(20643, 4)\n", "processing file MERGED1999_00_PP.csv\n", "(27252, 4)\n", "processing file MERGED2000_01_PP.csv\n", "(33906, 4)\n", "processing file MERGED2001_02_PP.csv\n", "(40631, 4)\n", "processing file MERGED2002_03_PP.csv\n", "(47283, 4)\n", "processing file MERGED2003_04_PP.csv\n", "(53956, 4)\n", "processing file MERGED2004_05_PP.csv\n", "(60703, 4)\n", "processing file MERGED2005_06_PP.csv\n", "(67602, 4)\n", "processing file MERGED2006_07_PP.csv\n", "(74553, 4)\n", "processing file MERGED2007_08_PP.csv\n", "(81524, 4)\n", "processing file MERGED2008_09_PP.csv\n", "(88579, 4)\n", "processing file MERGED2009_10_PP.csv\n", "(95796, 4)\n", "processing file MERGED2010_11_PP.csv\n", "(103266, 4)\n", "processing file MERGED2011_12_PP.csv\n", "(111012, 4)\n", "processing file MERGED2012_13_PP.csv\n", "(118874, 4)\n", "processing file MERGED2013_14_PP.csv\n", "(126743, 4)\n", "processing file MERGED2014_15_PP.csv\n", "(134509, 4)\n", "processing file MERGED2015_16_PP.csv\n", "(142175, 4)\n", "processing file MERGED2016_17_PP.csv\n", "(149413, 4)\n", "processing file MERGED2017_18_PP.csv\n", "(156525, 4)\n", "processing file MERGED2018_19_PP.csv\n", "(163332, 4)\n", "processing file MERGED2019_20_PP.csv\n", "(170026, 4)\n", "processing file MERGED2020_21_PP.csv\n", "(176707, 4)\n" ] } ], "source": [ "#(Write code here)\n", "\n", "# df_all = pd.DataFrame() this empty dataframe does not work for the append method.\n", "\n", "yearly_list.sort()\n", "\n", "print(\"processing file\", yearly_list[0])\n", "df_all = pd.read_csv(\"/content/\" + yearly_list[0], usecols=['UNITID', 'INSTNM', 'TUITIONFEE_IN'])\n", "df_all[\"year\"] = yearly_list[0][6:10]\n", "print(df_all.shape)\n", "\n", "for yearly_file in yearly_list[1:]:\n", " print(\"processing file\", yearly_file)\n", " df1 = pd.read_csv(\"/content/\" + yearly_file, usecols=['UNITID', 'INSTNM', 'TUITIONFEE_IN'])\n", " df1[\"year\"] = yearly_file[6:10]\n", " df_all = df_all.append(df1, ignore_index = True)\n", " print(df_all.shape)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "M-OsDFXFiZd-" }, "source": [ "## Step 8 - Explore the new dataframe \n", "\n", "For example, # of observations, varibles, head, tail, sample, missing values, statistics, etc." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "riJxj3vU6zLY", "outputId": "c0cfeed6-8cdf-426d-9555-3ffe026aa902" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 176707 entries, 0 to 176706\n", "Data columns (total 4 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 UNITID 176707 non-null int64 \n", " 1 INSTNM 176707 non-null object \n", " 2 TUITIONFEE_IN 86867 non-null float64\n", " 3 year 176707 non-null object \n", "dtypes: float64(1), int64(1), object(2)\n", "memory usage: 5.4+ MB\n" ] } ], "source": [ "#(Write code here)\n", "df_all.info()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "fQm-XtG78eAQ", "outputId": "13c6de5b-5bc8-4bf4-a239-bdefd59d6791" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDINSTNMTUITIONFEE_INyear
0100636Community College of the Air ForceNaN1996
1100654Alabama A & M UniversityNaN1996
2100663University of Alabama at BirminghamNaN1996
3100672ALABAMA AVIATION AND TECHNICAL COLLEGENaN1996
4100690Amridge UniversityNaN1996
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID INSTNM TUITIONFEE_IN year\n", "0 100636 Community College of the Air Force NaN 1996\n", "1 100654 Alabama A & M University NaN 1996\n", "2 100663 University of Alabama at Birmingham NaN 1996\n", "3 100672 ALABAMA AVIATION AND TECHNICAL COLLEGE NaN 1996\n", "4 100690 Amridge University NaN 1996" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_all.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 300 }, "id": "0Yc3z2Gf8eRc", "outputId": "4ce37635-edfd-4e18-94f5-b25edccf9f3c" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDTUITIONFEE_IN
count1.767070e+0586867.000000
mean1.054338e+0612037.588290
std4.959072e+0610462.970227
min1.006360e+050.000000
25%1.641730e+053936.000000
50%2.136310e+059422.000000
75%4.137780e+0516490.000000
max4.957672e+0774787.000000
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID TUITIONFEE_IN\n", "count 1.767070e+05 86867.000000\n", "mean 1.054338e+06 12037.588290\n", "std 4.959072e+06 10462.970227\n", "min 1.006360e+05 0.000000\n", "25% 1.641730e+05 3936.000000\n", "50% 2.136310e+05 9422.000000\n", "75% 4.137780e+05 16490.000000\n", "max 4.957672e+07 74787.000000" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_all.describe()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "RUdu61fI8efI", "outputId": "6491f575-90af-415e-e035-2a3c861b8a55" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDINSTNMTUITIONFEE_INyear
17670249576719Pennsylvania State University-Penn State Wilke...13604.02020
17670349576720Pennsylvania State University-Penn State York14486.02020
17670449576721Pennsylvania State University-Penn State Great...NaN2020
17670549576722Pennsylvania State University-Penn State Harri...15216.02020
17670649576723Pennsylvania State University-Penn State Brand...14486.02020
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID INSTNM \\\n", "176702 49576719 Pennsylvania State University-Penn State Wilke... \n", "176703 49576720 Pennsylvania State University-Penn State York \n", "176704 49576721 Pennsylvania State University-Penn State Great... \n", "176705 49576722 Pennsylvania State University-Penn State Harri... \n", "176706 49576723 Pennsylvania State University-Penn State Brand... \n", "\n", " TUITIONFEE_IN year \n", "176702 13604.0 2020 \n", "176703 14486.0 2020 \n", "176704 NaN 2020 \n", "176705 15216.0 2020 \n", "176706 14486.0 2020 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_all.tail()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "OIr5Up-P8uDI", "outputId": "53ceba5c-f5c7-4663-8a53-0e225d7ffb36" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDINSTNMTUITIONFEE_INyear
47356102553University of Alaska Anchorage3232.02003
64385214546Pennsylvania Academy of Cosmetology Arts and S...NaN2005
166483220613Lee University18770.02019
45161233842Union Presbyterian SeminaryNaN2002
105960194736Rabbinical College of Long IslandNaN2011
81186449223Regency Beauty Institute-Fairview HeightsNaN2007
161362445364North-West College-RiversideNaN2018
91317194073New York College of Podiatric MedicineNaN2009
53165420796EMPIRE BEAUTY SCHOOL-BALTIMORENaN2003
87282437103Baton Rouge Community College1884.02008
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID INSTNM \\\n", "47356 102553 University of Alaska Anchorage \n", "64385 214546 Pennsylvania Academy of Cosmetology Arts and S... \n", "166483 220613 Lee University \n", "45161 233842 Union Presbyterian Seminary \n", "105960 194736 Rabbinical College of Long Island \n", "81186 449223 Regency Beauty Institute-Fairview Heights \n", "161362 445364 North-West College-Riverside \n", "91317 194073 New York College of Podiatric Medicine \n", "53165 420796 EMPIRE BEAUTY SCHOOL-BALTIMORE \n", "87282 437103 Baton Rouge Community College \n", "\n", " TUITIONFEE_IN year \n", "47356 3232.0 2003 \n", "64385 NaN 2005 \n", "166483 18770.0 2019 \n", "45161 NaN 2002 \n", "105960 NaN 2011 \n", "81186 NaN 2007 \n", "161362 NaN 2018 \n", "91317 NaN 2009 \n", "53165 NaN 2003 \n", "87282 1884.0 2008 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_all.sample(10)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "4FgXDHHv8uOd", "outputId": "c5b1e4e3-47f1-469f-c1a0-8f35352c6b83" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDINSTNMTUITIONFEE_INyear
0FalseFalseTrueFalse
1FalseFalseTrueFalse
2FalseFalseTrueFalse
3FalseFalseTrueFalse
4FalseFalseTrueFalse
...............
176702FalseFalseFalseFalse
176703FalseFalseFalseFalse
176704FalseFalseTrueFalse
176705FalseFalseFalseFalse
176706FalseFalseFalseFalse
\n", "

176707 rows × 4 columns

\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID INSTNM TUITIONFEE_IN year\n", "0 False False True False\n", "1 False False True False\n", "2 False False True False\n", "3 False False True False\n", "4 False False True False\n", "... ... ... ... ...\n", "176702 False False False False\n", "176703 False False False False\n", "176704 False False True False\n", "176705 False False False False\n", "176706 False False False False\n", "\n", "[176707 rows x 4 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_all.isnull()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ViA5VxKz8uc8", "outputId": "8e88cbc2-9943-428d-a820-9c6aa61eee0f" }, "outputs": [ { "data": { "text/plain": [ ".sum of UNITID INSTNM TUITIONFEE_IN year\n", "0 False False True False\n", "1 False False True False\n", "2 False False True False\n", "3 False False True False\n", "4 False False True False\n", "... ... ... ... ...\n", "176702 False False False False\n", "176703 False False False False\n", "176704 False False True False\n", "176705 False False False False\n", "176706 False False False False\n", "\n", "[176707 rows x 4 columns]>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_all.isnull().sum" ] }, { "cell_type": "markdown", "metadata": { "id": "odf117B-ip7D" }, "source": [ "## Step 9 - UMBC \n", "\n", "The dataframe contains many years of data of all U.S. colleges. let's just look at UMBC. Filter/query the dataframe to retrieve only rows that belong to UMBC (one row represent one year). Save the UMNC data to a new data frame. using a new variable for the UMBC data frame, so that the old big data frame is still available for later use." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 833 }, "id": "NQjKoDmU6zLd", "outputId": "e4f53c54-ec01-470b-aa50-ce67668f93f5" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDINSTNMTUITIONFEE_INyear
2151163268University of Maryland-Baltimore CountyNaN1996
9056163268University of Maryland-Baltimore CountyNaN1997
15923163268University of Maryland-Baltimore CountyNaN1998
22598163268University of Maryland-Baltimore CountyNaN1999
29178163268University of Maryland-Baltimore County5490.02000
35821163268University of Maryland-Baltimore County5910.02001
42524163268University of Maryland-Baltimore County6362.02002
49156163268University of Maryland-Baltimore County7388.02003
55786163268University of Maryland-Baltimore County8020.02004
62522163268University of Maryland-Baltimore County8520.02005
69401163268University of Maryland-Baltimore County8622.02006
76332163268University of Maryland-Baltimore County8708.02007
83291163268University of Maryland-Baltimore County8780.02008
90324163268University of Maryland-Baltimore County8872.02009
97506163268University of Maryland-Baltimore County9171.02010
104960163268University of Maryland-Baltimore County9467.02011
112672163268University of Maryland-Baltimore County9764.02012
120503163268University of Maryland-Baltimore County10068.02013
128353163268University of Maryland-Baltimore County10384.02014
136064163268University of Maryland-Baltimore County11006.02015
143685163268University of Maryland-Baltimore County11264.02016
150891163268University of Maryland-Baltimore County11518.02017
157940163268University of Maryland-Baltimore County11778.02018
164723163268University of Maryland-Baltimore County12028.02019
171401163268University of Maryland-Baltimore County9420.02020
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID INSTNM TUITIONFEE_IN year\n", "2151 163268 University of Maryland-Baltimore County NaN 1996\n", "9056 163268 University of Maryland-Baltimore County NaN 1997\n", "15923 163268 University of Maryland-Baltimore County NaN 1998\n", "22598 163268 University of Maryland-Baltimore County NaN 1999\n", "29178 163268 University of Maryland-Baltimore County 5490.0 2000\n", "35821 163268 University of Maryland-Baltimore County 5910.0 2001\n", "42524 163268 University of Maryland-Baltimore County 6362.0 2002\n", "49156 163268 University of Maryland-Baltimore County 7388.0 2003\n", "55786 163268 University of Maryland-Baltimore County 8020.0 2004\n", "62522 163268 University of Maryland-Baltimore County 8520.0 2005\n", "69401 163268 University of Maryland-Baltimore County 8622.0 2006\n", "76332 163268 University of Maryland-Baltimore County 8708.0 2007\n", "83291 163268 University of Maryland-Baltimore County 8780.0 2008\n", "90324 163268 University of Maryland-Baltimore County 8872.0 2009\n", "97506 163268 University of Maryland-Baltimore County 9171.0 2010\n", "104960 163268 University of Maryland-Baltimore County 9467.0 2011\n", "112672 163268 University of Maryland-Baltimore County 9764.0 2012\n", "120503 163268 University of Maryland-Baltimore County 10068.0 2013\n", "128353 163268 University of Maryland-Baltimore County 10384.0 2014\n", "136064 163268 University of Maryland-Baltimore County 11006.0 2015\n", "143685 163268 University of Maryland-Baltimore County 11264.0 2016\n", "150891 163268 University of Maryland-Baltimore County 11518.0 2017\n", "157940 163268 University of Maryland-Baltimore County 11778.0 2018\n", "164723 163268 University of Maryland-Baltimore County 12028.0 2019\n", "171401 163268 University of Maryland-Baltimore County 9420.0 2020" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#(Write code here)\n", "df_UMBC=df_all[df_all[\"INSTNM\"].str.contains(\"University of Maryland-Baltimore County\")]\n", "df_UMBC" ] }, { "cell_type": "markdown", "metadata": { "id": "JEwabb0PjCmT" }, "source": [ "## Step 10 - Explore the new dataframe\n", "\n", "For example, # of observations, varibles, head, tail, sample, missing values, statistics, etc" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d5X87ugp6zLt", "outputId": "451b2bc1-4aee-4d7a-cdd9-3554eef1ae5c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 25 entries, 2151 to 171401\n", "Data columns (total 4 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 UNITID 25 non-null int64 \n", " 1 INSTNM 25 non-null object \n", " 2 TUITIONFEE_IN 21 non-null float64\n", " 3 year 25 non-null object \n", "dtypes: float64(1), int64(1), object(2)\n", "memory usage: 1000.0+ bytes\n" ] } ], "source": [ "#(Write code here)\n", "df_UMBC.info()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "wBF9yUNE9sGF", "outputId": "2865a666-20fc-4d4c-bef4-d377fa83a72d" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDINSTNMTUITIONFEE_INyear
2151163268University of Maryland-Baltimore CountyNaN1996
9056163268University of Maryland-Baltimore CountyNaN1997
15923163268University of Maryland-Baltimore CountyNaN1998
22598163268University of Maryland-Baltimore CountyNaN1999
29178163268University of Maryland-Baltimore County5490.02000
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID INSTNM TUITIONFEE_IN year\n", "2151 163268 University of Maryland-Baltimore County NaN 1996\n", "9056 163268 University of Maryland-Baltimore County NaN 1997\n", "15923 163268 University of Maryland-Baltimore County NaN 1998\n", "22598 163268 University of Maryland-Baltimore County NaN 1999\n", "29178 163268 University of Maryland-Baltimore County 5490.0 2000" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_UMBC.head()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "qxU20vIu9sJO", "outputId": "77768fe0-2c44-4d8d-de3c-dac1370bc473" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDINSTNMTUITIONFEE_INyear
143685163268University of Maryland-Baltimore County11264.02016
150891163268University of Maryland-Baltimore County11518.02017
157940163268University of Maryland-Baltimore County11778.02018
164723163268University of Maryland-Baltimore County12028.02019
171401163268University of Maryland-Baltimore County9420.02020
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID INSTNM TUITIONFEE_IN year\n", "143685 163268 University of Maryland-Baltimore County 11264.0 2016\n", "150891 163268 University of Maryland-Baltimore County 11518.0 2017\n", "157940 163268 University of Maryland-Baltimore County 11778.0 2018\n", "164723 163268 University of Maryland-Baltimore County 12028.0 2019\n", "171401 163268 University of Maryland-Baltimore County 9420.0 2020" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_UMBC.tail()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 300 }, "id": "GiXIOU9V9sL3", "outputId": "67fc505b-fa89-426b-868b-c52e969699e6" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDTUITIONFEE_IN
count25.021.000000
mean163268.09168.571429
std0.01854.082079
min163268.05490.000000
25%163268.08520.000000
50%163268.09171.000000
75%163268.010384.000000
max163268.012028.000000
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID TUITIONFEE_IN\n", "count 25.0 21.000000\n", "mean 163268.0 9168.571429\n", "std 0.0 1854.082079\n", "min 163268.0 5490.000000\n", "25% 163268.0 8520.000000\n", "50% 163268.0 9171.000000\n", "75% 163268.0 10384.000000\n", "max 163268.0 12028.000000" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_UMBC.describe()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 833 }, "id": "oqGRM7UI9sOb", "outputId": "3ed09b2f-a5da-4520-dd80-4ea3bb4bef15" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDINSTNMTUITIONFEE_INyear
2151FalseFalseTrueFalse
9056FalseFalseTrueFalse
15923FalseFalseTrueFalse
22598FalseFalseTrueFalse
29178FalseFalseFalseFalse
35821FalseFalseFalseFalse
42524FalseFalseFalseFalse
49156FalseFalseFalseFalse
55786FalseFalseFalseFalse
62522FalseFalseFalseFalse
69401FalseFalseFalseFalse
76332FalseFalseFalseFalse
83291FalseFalseFalseFalse
90324FalseFalseFalseFalse
97506FalseFalseFalseFalse
104960FalseFalseFalseFalse
112672FalseFalseFalseFalse
120503FalseFalseFalseFalse
128353FalseFalseFalseFalse
136064FalseFalseFalseFalse
143685FalseFalseFalseFalse
150891FalseFalseFalseFalse
157940FalseFalseFalseFalse
164723FalseFalseFalseFalse
171401FalseFalseFalseFalse
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID INSTNM TUITIONFEE_IN year\n", "2151 False False True False\n", "9056 False False True False\n", "15923 False False True False\n", "22598 False False True False\n", "29178 False False False False\n", "35821 False False False False\n", "42524 False False False False\n", "49156 False False False False\n", "55786 False False False False\n", "62522 False False False False\n", "69401 False False False False\n", "76332 False False False False\n", "83291 False False False False\n", "90324 False False False False\n", "97506 False False False False\n", "104960 False False False False\n", "112672 False False False False\n", "120503 False False False False\n", "128353 False False False False\n", "136064 False False False False\n", "143685 False False False False\n", "150891 False False False False\n", "157940 False False False False\n", "164723 False False False False\n", "171401 False False False False" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_UMBC.isnull()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "t3sD7pT69sRS" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6j-aFVt69sUe" }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "id": "yhkk1v-KjNu-" }, "source": [ "## Step 11 - Plot UMBC's in-state tuition overtime Bar Chart" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "id": "5Poy4ciz6zLy", "outputId": "149cbe5c-2fbc-4edc-de90-b65dab988624" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#(Write code here)\n", "import seaborn as sns\n", "sns.barplot(x='year',y='TUITIONFEE_IN',data=df_UMBC)" ] }, { "cell_type": "markdown", "metadata": { "id": "85qFArw4jXnP" }, "source": [ "## Step 12 - Plot UMBC's in-state tuition overtime using line Chart" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "id": "afOQjc1w6zL4", "outputId": "dde44d64-2e8b-4032-bab0-a9d3529ef796" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#(Write code here)\n", "sns.lineplot(x='year',y='TUITIONFEE_IN',data=df_UMBC)" ] }, { "cell_type": "markdown", "metadata": { "id": "5FH2h2_ajlKE" }, "source": [ "## Step 13 - Tuition Growth Rate\n", "\n", "Now let's look at the tuition growth rate year over year. We need to calculate UMBC tuition change percentage each year. First convert the TUITIONFEE_IN column to a Python List" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xQLB2f7J6zL-", "outputId": "810c269f-663d-449f-83a5-f6902af9473e" }, "outputs": [ { "data": { "text/plain": [ "[0,\n", " nan,\n", " nan,\n", " nan,\n", " nan,\n", " 7.650273224043716,\n", " 7.648054145516074,\n", " 16.12700408676517,\n", " 8.554412560909583,\n", " 6.234413965087282,\n", " 1.1971830985915493,\n", " 0.9974483878450475,\n", " 0.8268259072117593,\n", " 1.0478359908883828,\n", " 3.370153291253381,\n", " 3.227565151019518,\n", " 3.1372134783986483,\n", " 3.11347808275297,\n", " 3.1386571315057608,\n", " 5.989984591679507,\n", " 2.344175904052335,\n", " 2.254971590909091,\n", " 2.2573363431151243,\n", " 2.122601460349805,\n", " -21.68274027269704]" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#(Write code here)\n", "tuition_Growth = df_UMBC['TUITIONFEE_IN'].tolist()\n", "percent_Growth = [0]\n", "for i in range(1,len(tuition_Growth)):\n", " difference = (tuition_Growth[i]-tuition_Growth[i-1])/tuition_Growth[i-1]\n", " percent_Growth.append(difference*100)\n", "percent_Growth " ] }, { "cell_type": "markdown", "metadata": { "id": "e4OhAzjlj3tw" }, "source": [ "## Step 14 - Loop through the list and calculate the % change each year over the prior year\n", "\n", "This takes some effort. Not hard, just some abstract/logical thinking and some experiments. Have fun on this one." ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LRUyQCtJ6zMB", "outputId": "704c1874-2f50-408b-d67a-a40b90d7aa6c" }, "outputs": [ { "data": { "text/plain": [ "[0,\n", " nan,\n", " nan,\n", " nan,\n", " nan,\n", " 7.650273224043716,\n", " 7.648054145516074,\n", " 16.12700408676517,\n", " 8.554412560909583,\n", " 6.234413965087282,\n", " 1.1971830985915493,\n", " 0.9974483878450475,\n", " 0.8268259072117593,\n", " 1.0478359908883828,\n", " 3.370153291253381,\n", " 3.227565151019518,\n", " 3.1372134783986483,\n", " 3.11347808275297,\n", " 3.1386571315057608,\n", " 5.989984591679507,\n", " 2.344175904052335,\n", " 2.254971590909091,\n", " 2.2573363431151243,\n", " 2.122601460349805,\n", " -21.68274027269704]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ " #(Write code here)\n", "list = []\n", "for i in percent_Growth:\n", " list.append(i)\n", "list" ] }, { "cell_type": "markdown", "metadata": { "id": "vLogjxM1kSDH" }, "source": [ "## Step 15 - Round up the percentage to two decimal points\n", "\n", "The resulting number has many decimal points which are unnecessary and not visually appealing. You can use for loop. Or better, use list comprehension for simplicity/brevity." ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9sxMk1hc6zML", "outputId": "622972d4-9c52-4865-fc3a-c41538aa8677" }, "outputs": [ { "data": { "text/plain": [ "[0,\n", " nan,\n", " nan,\n", " nan,\n", " nan,\n", " 7.65,\n", " 7.65,\n", " 16.13,\n", " 8.55,\n", " 6.23,\n", " 1.2,\n", " 1.0,\n", " 0.83,\n", " 1.05,\n", " 3.37,\n", " 3.23,\n", " 3.14,\n", " 3.11,\n", " 3.14,\n", " 5.99,\n", " 2.34,\n", " 2.25,\n", " 2.26,\n", " 2.12,\n", " -21.68]" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#(Write code here)\n", "rounded_list = []\n", "for i in percent_Growth:\n", " rounded_list.append(round(i,2))\n", "rounded_list" ] }, { "cell_type": "markdown", "metadata": { "id": "cWK8vDitkLpx" }, "source": [ "## Step 16 - Add the list of the percentages to the dataframe as a new column (\"PCT_CHANGE\")\n", "\n", "Not as hard as you may think. If you get stuck, you think too hard. Google it and you will find the answer." ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 937 }, "id": "_04rVaZ16zMR", "outputId": "b24b01fa-9c3e-4211-c62b-79477e1f3649" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " \n" ] }, { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
UNITIDINSTNMTUITIONFEE_INyearPCT_CHANGE
2151163268University of Maryland-Baltimore CountyNaN19960.00
9056163268University of Maryland-Baltimore CountyNaN1997NaN
15923163268University of Maryland-Baltimore CountyNaN1998NaN
22598163268University of Maryland-Baltimore CountyNaN1999NaN
29178163268University of Maryland-Baltimore County5490.02000NaN
35821163268University of Maryland-Baltimore County5910.020017.65
42524163268University of Maryland-Baltimore County6362.020027.65
49156163268University of Maryland-Baltimore County7388.0200316.13
55786163268University of Maryland-Baltimore County8020.020048.55
62522163268University of Maryland-Baltimore County8520.020056.23
69401163268University of Maryland-Baltimore County8622.020061.20
76332163268University of Maryland-Baltimore County8708.020071.00
83291163268University of Maryland-Baltimore County8780.020080.83
90324163268University of Maryland-Baltimore County8872.020091.05
97506163268University of Maryland-Baltimore County9171.020103.37
104960163268University of Maryland-Baltimore County9467.020113.23
112672163268University of Maryland-Baltimore County9764.020123.14
120503163268University of Maryland-Baltimore County10068.020133.11
128353163268University of Maryland-Baltimore County10384.020143.14
136064163268University of Maryland-Baltimore County11006.020155.99
143685163268University of Maryland-Baltimore County11264.020162.34
150891163268University of Maryland-Baltimore County11518.020172.25
157940163268University of Maryland-Baltimore County11778.020182.26
164723163268University of Maryland-Baltimore County12028.020192.12
171401163268University of Maryland-Baltimore County9420.02020-21.68
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " UNITID INSTNM TUITIONFEE_IN year \\\n", "2151 163268 University of Maryland-Baltimore County NaN 1996 \n", "9056 163268 University of Maryland-Baltimore County NaN 1997 \n", "15923 163268 University of Maryland-Baltimore County NaN 1998 \n", "22598 163268 University of Maryland-Baltimore County NaN 1999 \n", "29178 163268 University of Maryland-Baltimore County 5490.0 2000 \n", "35821 163268 University of Maryland-Baltimore County 5910.0 2001 \n", "42524 163268 University of Maryland-Baltimore County 6362.0 2002 \n", "49156 163268 University of Maryland-Baltimore County 7388.0 2003 \n", "55786 163268 University of Maryland-Baltimore County 8020.0 2004 \n", "62522 163268 University of Maryland-Baltimore County 8520.0 2005 \n", "69401 163268 University of Maryland-Baltimore County 8622.0 2006 \n", "76332 163268 University of Maryland-Baltimore County 8708.0 2007 \n", "83291 163268 University of Maryland-Baltimore County 8780.0 2008 \n", "90324 163268 University of Maryland-Baltimore County 8872.0 2009 \n", "97506 163268 University of Maryland-Baltimore County 9171.0 2010 \n", "104960 163268 University of Maryland-Baltimore County 9467.0 2011 \n", "112672 163268 University of Maryland-Baltimore County 9764.0 2012 \n", "120503 163268 University of Maryland-Baltimore County 10068.0 2013 \n", "128353 163268 University of Maryland-Baltimore County 10384.0 2014 \n", "136064 163268 University of Maryland-Baltimore County 11006.0 2015 \n", "143685 163268 University of Maryland-Baltimore County 11264.0 2016 \n", "150891 163268 University of Maryland-Baltimore County 11518.0 2017 \n", "157940 163268 University of Maryland-Baltimore County 11778.0 2018 \n", "164723 163268 University of Maryland-Baltimore County 12028.0 2019 \n", "171401 163268 University of Maryland-Baltimore County 9420.0 2020 \n", "\n", " PCT_CHANGE \n", "2151 0.00 \n", "9056 NaN \n", "15923 NaN \n", "22598 NaN \n", "29178 NaN \n", "35821 7.65 \n", "42524 7.65 \n", "49156 16.13 \n", "55786 8.55 \n", "62522 6.23 \n", "69401 1.20 \n", "76332 1.00 \n", "83291 0.83 \n", "90324 1.05 \n", "97506 3.37 \n", "104960 3.23 \n", "112672 3.14 \n", "120503 3.11 \n", "128353 3.14 \n", "136064 5.99 \n", "143685 2.34 \n", "150891 2.25 \n", "157940 2.26 \n", "164723 2.12 \n", "171401 -21.68 " ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#(Write code here)\n", "df_UMBC['PCT_CHANGE'] = rounded_list\n", "df_UMBC" ] }, { "cell_type": "markdown", "metadata": { "id": "aVcm3NltknpE" }, "source": [ "## Step 17 - Finaly, we can make the plot tuition growth rate year over year - bar first, then line chart" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "id": "5Evbjx-g6zMX", "outputId": "e8b795de-53eb-4002-e1e4-d198cdb0528c" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# (write code here)\n", "\n", "sns.barplot(x='year',y='PCT_CHANGE', data=df_UMBC)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "id": "Lgq9RhHg_0O8", "outputId": "87b55e1e-9a21-416c-cf64-6012153a9ee8" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZwcVbn/8c8ze5aZhCwzCVkIJIGYhUQSwKAmBATiBsIFRQVB0IjCT0XUK3q9V64/4F4VUX8ii4AgqAgiyKKskgASlgmQhYSEBEJISDKTfZbM2s/vj6rJdIaemZ5Jd1fPzPf9es1ruqvr9Hmqt6fOqVOnzN0RERFpKyfqAEREJDspQYiISEJKECIikpAShIiIJKQEISIiCeVFHUCqDBs2zMeNGxd1GCIiPcqSJUu2ufvwRI/1mgQxbtw4ysvLow5DRKRHMbO323tMXUwiIpKQEoSIiCSkBCEiIgkpQYiISEJKECIikpAShIiIJKQEISIiCSlBSNq9s6OWR1ZsjjoMEekiJQhJu2sfX8NFd77M1j11UYciIl2gBCFpFYs5i9ZUAvDYyq0RRyMiXaEEIWm1fNNuttc0YAaPrtgSdTgi0gWRJwgzu9XMKsxsRdyyH5nZJjN7Nfz7WJQxSvctXF2JGZx99BgWv7mdXbUNUYckIkmKPEEAtwHzEyy/1t1nhH9/z3BMkiIL11QwffRgzj56LM0x54lVFVGHJCJJijxBuPvTwI6o45DU21HTwKvv7OL4I4Zz5OhBjBxUxCPqZhLpMSJPEB24xMyWhV1QByVawcwWmFm5mZVXVlZmOj7pxDNvVOIOxx9RiplxypQRPP1GJTX1TVGHJiJJyNYEcT0wHpgBbAauSbSSu9/k7rPcfdbw4QmvdyERWri6kiEDCjhy1CAA5k8dQUNTjIWrlcxFeoKsTBDuvtXdm909BvwWOCbqmKRrWoa3zpk4jJwcA+DocUMYOqCAR15TN5NIT5CVCcLMRsbdPR1Y0d66kp2WbdrNjpoG5k0q3bcsN8c4aXIZT71eQX1Tc4TRiUgyIk8QZvYnYDFwhJltNLMLgZ+Y2XIzWwbMAy6NNEjpsoWrKzCDD0/cv+vvlKkjqK5v4rm12yOKTESSFfk1qd39swkW35LxQCSlFq6uZProwQwZULDf8uPGD6W4MI9HVmzZr3UhItkn8haE9D47ahpYujEY3tpWYV4uJ7yvlMdXbaWpORZBdCKSLCUISbn44a2JzJ8ygh01Dby0fmeGIxORrlCCkJR76vUKhsYNb21r7hHDKczL4VGNZhLJakoQklKxmPP0G9uYc/jwfcNb2+pfkMfcw4fzyIotxGKe4QhFJFlKEJJSLcNbEx1/iDd/6gi27Klj2abdGYpMRLpKCUJSqr3hrW2dOKmMvBzT3EwiWUwJQlLqqdWVzBjz3uGtbQ3qn8/s8UN5ZMVm3NXNJJKNlCAkZbZX17Ns4y6OPzy58xtOmTKC9dtrWbO1Os2RiUh3KEFIyjzzxrZweGtyEyeePLksuNKcRjOJZCUlCEmZhauD4a3T2hne2lZpSREzxx6k4xAiWUoJQlKiOYnhrYnMnzqClZv3sGF7bRqjE5HuUIKQlFi2cVdSw1vbOmXKCEDdTCLZSAlCUmLh6kpyDOZ0Mry1rTFD+jPl4BJdI0IkCylBSEosXFPJ9DGDOaiT4a2JzJ8ygiVv76RiT10aIhOR7lKCkAPW1eGtbc2fGnYzrdyayrBE5AApQcgBaxneOm9S964LPqF0IIcNH8CjGs0kklWUIOSAPbW6gmEDC5h6cHLDW9syM+ZPGcHiN7ezq7YhxdGJSHdFniDM7FYzqzCzFXHLhpjZ42b2Rvj/oChjlPY1x5yn11QyZ2LXhre2NX/qCJpjzhOrKlIYnYgciMgTBHAbML/Nsu8BT7r7RODJ8L5koWUbd7GztpG5XRze2ta0UYM4eFCRTpoTySKRJwh3fxrY0WbxacDt4e3bgU9lNChJWneHt7ZlZpwydQTPvFFJTX1TiqITkQMReYJoR5m7bw5vbwHKEq1kZgvMrNzMyisrKzMXneyzcHUFM7o5vLWt+VNGUN8UY9EavZci2SBbE8Q+HswFnXA+aHe/yd1nufus4cMPbA9Wum57dT3LNu1u99rTXTVr3BCGDihQN5NIlsjWBLHVzEYChP915DILPf1GZZdmb+1Mbo5x8pQy/vl6BfVNzSl5ThHpvmxNEA8A54W3zwP+FmEs0o6FqysPaHhrIidPGUF1fRPPrd2esucUke6JPEGY2Z+AxcARZrbRzC4E/gc4yczeAD4S3pcskqrhrW0dN34oxYV56mYSyQJ5UQfg7p9t56ETMxqIdMnScHjr8ZNSc/yhRWFeLie8r5THV23lyuYYebmR78OI9Fn69km3tA5vHZby554/ZQQ7ahp4af3OlD+3iCRPCUK6ZVE4vHVw/wMf3trW3COGU5iXw+8XrycWSziATUQyQAlCuizVw1vb6l+Qx8XzJvCPFVv49j1LaWqOpaUeEelY5McgpOdpGd46L00JAuDrJ04kx+Bnj62hrqmZX3zm/RTkaX9GJJOUIKTLnno9GN465eCStNZzyQkT6VeQx48fWsnehnKuP2cmRfm5aa1TRFopQfRxO2saqKiqT3p9x3nmjUrmTSpN6fDW9lz4oUPpl5/LD+5fzgW3vcRvvzCLAYX62Ipkgr5pfVRjc4xbn32La59YQ11j1/v4T0jx8NaOfO7YsfQryOGyu5fyhVtf5HdfPJqSovyM1S/SVylB9EFL39nF9/66nFWb9/CR95Vx+vtHYV1oDBTl5zC3m5cX7a7T3z+aorxcvn7XK3zut8/z+wuOZUgKJggUkfYpQfQh1fVN/OzR1dy+eD2lxYXccM5RnDJlBNaV7BChj04byU35uXzlziWcfdNi7vzSsZQWF0UdlkivpWEhfcTjK7dy0s8Xcfvi9Zxz7CE8/q25zJ86ssckhxbzJpVy2/lHs3HnXj5z4/O8u2tv1CGJ9FpKEL3c1j11XHTHEr78+3JKivL5y0XH8eNPTe3RffjHTRjGHRcew7aqes66YTFvb6+JOiSRXkkJopeKxZw7nn+bj1yziKdWV/CdU47goa9/iJmH9I7Le888ZAh/WvABahuaOOuGxaytqIo6JJFeRwmiF1q9pYozb3iOH96/giPHDOLRb87h4nkTyO9lE99NHTWIuxbMxoHP3Pg8r727O+qQRHqV3vWL0cfVNTbz00df5+O/eoa3ttXw809P584Lj2XcsAFRh5Y2R4wo5u6vzKYwL4fP3vQ8ayuqow5JpNdQgugl/rV2G/N/8TTXPbWO02aM4snLjueMo0b3uIPQ3XHosAH8+SuziTlc+8SaqMORDNtV28C9SzZqzq400DDXHm5HTQP/9+GV/PXlTYwb2p8/fOlYPjgh9VNwZ7sxQ/pz7uxDuGHROtZVVjN++MCoQ5IMqGts5ku3l1P+9k4qqur56vHjow6pV1ELoodyd+5dspETr1nIA6++y8XzxvPIN+f0yeTQ4sIPHUphXg7XL1wXdSiSAe7Ov9+7jPK3dzJpRDHXPr6GN7ZqsEIqZXWCMLP1ZrbczF41s/Ko48kW67fVcM4tL3DZPUs5dNgAHv76h/nOKZP6/ER2wwYW8tljxnLfK5t4Z0dt1OFImv3qybX87dV3+fbJh3Pnl45lQGEu3/7LMnU1pVBWJ4jQPHef4e6zog4kao3NMa57ai2n/OJplr2zmx9/aip/ueg4jhhRHHVoWWPBnMPIMbjxabUierO/vbqJa59YwxlHjeLieRMYNrCQ/z5tKkvf2cUtz74VdXi9Rk9IEAIseXsnn/jVs/z00dWcMKmUJy6by7kfOCQjM6r2JCMH9ePMmWO4+6WNbN1TF3U4kgbl63fwnXuWccyhQ7j6jGn7BmJ84siRzJ8ygmseX6PRbCmS7QnCgcfMbImZLWj7oJktMLNyMyuvrKyMILz021PXyA/vX8GZNzzHnrpGfvuFWVx/zkzKSjQHUXu+Onc8ze789uk3ow5FUmzD9loW3LGEgwcXceM5MynMa+1WNTN+/Kmp9C/I5Tt/WUqzLld7wLI9QXzI3Y8CPgpcbGZz4h9095vcfZa7zxo+fHg0EaaJu/PIis2c9PNF3PnC25x/3Dge/9ZcTppcFnVoWW/s0P6cNv1g/vDCBnbUNEQdjqTI7r2NfPG2F2mOObeefzQHJZjNd3hxIVecOoVXNuzilme1g3CgsjpBuPum8H8FcB9wTLQRZcbm3XtZcMcSLrrzZYYMKOT+r32Q//rkFAbqQjlJ+9q88dQ1NXOr+qN7hcbmGF/7wxI27KjlxnNnclgHw5hPnX4wJ08u42ePqavpQGVtgjCzAWZW3HIbOBlYEW1U6dUcc27711t85JpFPPNGJZd/dBIPXPJBpo8ZHHVoPc6E0mI+OnUEtz+3nt17G6MORw6Au/PD+1fwr7Xbuer0aXzgsKEdrm9m/N/Tp9IvP5fvqqvpgGRtggDKgGfNbCnwIvCwuz8ScUxps/LdPZxx/XP86MGVzBw3hMcvnctX5o7vdfMnZdLXjp9AVX0TdyxeH3UocgB++8yb3PXSO1w8bzxnzRqTVJnS4iKuOHUKL2/Yxe/+pVZkd2Vtn4W7vwlMjzqOdNvb0MwvnlzDzc+8xeB++fzy7BmcOv3gPjFFRrpNHTWIEyaVcsuzb/HFDx6qa1n3QI++toWr//E6H582kstOOqJLZU+bcTAPLdu8b+RfR91Skph2TyO0aE0lJ/9iETcuepMzjxrNk5fN5bQZo5QcUujieRPYWdvIn17cEHUo0kXLN+7mm3e9yvTRg7nm09O7PKTbzLjq9KkU5efy3b8sU1dTNyhBRGBbdT3fuOsVzrv1RfJzc7hrwQf43zOPZHB/XWM51WYechDHjR/KjU+/SV1jc9ThSJI2797Lhbe/xJABBfz2C7O6PUtAaUkRPzp1MuVv7+S259anNsg+QG3uDHJ37lmykSsfXkVtQxPfOHEiX5s3fr+x3JJ6l8ybwOdufoF7lmzk3A8cEnU4Kdccc37y6Ou89NYOiovyKS7KC//yKS6Mu12Ux8CiPEr2rZNP/4JcCvNysqrVWlPfxAW3lVPb0My9Xz2W4cWFB/R8n5oxioeXbeanj77OCZNKObQXT3+fakoQGfJmZTXfv285z7+5g2PGDeGqM6YyoVRTZGTC7PFDOWrsYG5YuI6zjx7Tqw78NzbHuOzupTyw9F2OGjuYnbUNbNhRS1VdE1V1jdQ3JTcvUVF+DkX5ufTLz6Vo318ORXm59CvI3fd4YV4ueTlGbo6RY0ZuDuTkGLlm5OXYvts54Tq5ZphBU8xpjjmxmNPsrf+bWpbFIObBOive3c2arVXcev7RKZlGxsy48vRpnPTzRXznnqX8+SuzydUMBEk5oARhZnnu3pSqYHqjhqYYNyxax6+fWktRXg5XnzGNz8waoykyMsjMuOSECVxwWzn3v7Ip6ZEw2a6+qZlL/vgKj6/cyr/Pn5RwquuGphjV9UGyqKprYk9dI9V1TfsSyN7GGHWNzdQ1NrN33//WZXWNzVRUNVLXGGNvQzP1TTGaY7Hgx96D1kvLD35TF/v4c+OSSJBwgmUFeTlcffo05h6eupNfy0qK+K9PTuGye5Zy+3PrueBDh6bsuXuzThOEmT3r7h8Kb9/h7ufGPfwicFS6guvpXlq/g8v/upy1FdV84siR/OcnJ1NarCkyojDviFImjyzh+oXrOOOo0T1+D3JvQzML7ijnmTe28aNPTub8Dyb+wSvIy2FIXgFDEpx1nA4tLYMggQT/HYLWhbUmhCh2kM44ahQPL9/MT8Kupt58pcVUSaatHf8qTmnzWM/+lqXJ7r2NXP7X5Zx1w2L2NjTzu/OP5tefO0rJIUItrYg3t9Xw9+Wbow7ngFTXN3He717k2bXb+Mm/HdlucohCTo6Rnxt0R/UvCI5zlBTl078gj6L8XPJzcyJrPQejmqaRn5vDd/+yjJhGNXUqmS6mjl5FvcJx3J2Hl2/migdXsr26ni9/+FAuPelw+hfoUE82mD9lBOOHD+C6p9by8Wkje2Q33+7aRr7wuxdZsWk3v/jMDE6bMSrqkHqUEYOK+M9PTOY7f1nGFQ++xvQxg/cdeynMz9l3DKZffnjsJS+XooIcCnKz60B+piTzyzXYzE4naG0MNrMzwuUGDEpbZD3Mxp21/OffXuOfr1cwbdQgfnf+0UwdpZcnm+TkGBfPm8C37l7Kk69X9LiJD7dV13PuLS+yrqKa6z9/FCdPGRF1SD3SmTNH8+SqCm5f/DYsfjupMmZQlJdLjgV7xe7gePgfaHPfPehaM2BAYevIsYEdjCoLHstn0ohixgzpn7bt7wpz77gRYGa/6+hxd/9iSiPqplmzZnl5eeYvOtfUHOO259bz88fXAPCtkw7n/OPGkdeLRsr0Jk3NMeZdszCcBPG4HrNXuGV3HZ+/+Xk27drLjefOSukB3L7I3dm6p77Nwflm6htj+92va3PAPubBj75Z0GVlAAZGMFpr32Ph/Zg7NfXN+wYFVNU1UVUfP1CgiYY2V8AbN7Q/C78zL2OvhZktae+CbJ22ILIlAWSjFZt2872/LmPFpj2cMKmU/z5tCqMPyo7ML4nl5ebw1bkT+P59y3l27TY+PDH7f2jf2VHL529+ge3V9dz+xWM4tpPJ6qRzZsaIQdlxTLC+qXlfsrhx0TruWbKRWMyzogs0mVFMo4Fx7v5seP9bQMukJn9097VpjC8r1dQ3ce3ja7j1X28xdGAh133uKD42bUSP2Rvt6/5t5ih+9eQb/Pqfa7M+QbxZWc05N79AdX0Td37pWN4/9qCoQ5IUK8zLpXBgLsMGFvK+kSU0x5ztNQ0HfIJgKiTTD/JTIH6+6a8ANQRdbVekI6hs9tTrFZx87dPc/OxbnH3MWJ741lw+fuRIJYcepDAvlwVzDuOFt3bw0vodUYfTrtVbqvj0jc9T3xTjrgWzlRz6gLKSIClky+VykzlIfYS7PxR3v9bdrwEws2fSE1b2qaiq44oHV/Lwss1MLB3IPRfN5uhxQ6IOS7rps8eM5bqn1nL131fxhdnjGDIgOFdg6MDgf9TTnyzfuJtzb32Bwrwc/vClD+is+z6i5VLCFVV1ZMMYoGQSRNuOuhPjbg9LYSxZKRZz/lz+Dlf/fRV1jTEuO+lwvjJ3PAV5Ogjdk/UryOXSkw7nP+5fwcsbXn3P48WFeQwJk8XQMHkMGVDI0AEFHDSggMH98hncP/gb1K+AQf3yu/SZcHeq65uorKpnW3UD26rrg7+qeiqr63lo6WZK+uXzxy8fyyFDdUJXX9GSILbuqY84kkAyCaLKzA539zUA7r4DwMwmAVXpDC5qayuquPyvy3lp/U4+cNgQrjp9muaU70XO+cAhfPLIg9lWU8+Omga2V9ezvaaBHdUNwf/wb9OuOpZv2s2OmgYam9sf9TegIJfB/YNkMSgugRQX5VNV10hlVQOVYRLYVl2fcJ6kHIMhAwqZOmoQ13x6OgcP7pfOl0CyTMtxh57UxfRfwENmdiXwcrhsJvB94BvpCixKdY3N/GbhOq5fuJYBhXn85MwjOWvmaB1n6IUG9c9nUP98xidxrNrd2VPXxO7aRnbtbWBXbSO79jayu7b19q7aRnbvbWD33kbWVlSza28je/Y2UlyUz7CBBQwvLmT8sAEMKy5k2MAChg0sZHhxIcMGBn9DBhT0+GlApPvyc3MYNrCg57Qg3P2R8OS47wJfDxevAM5w97ReI9rM5gO/BHKBm939f9JZH8Dzb27n+/ct583KGj4142D+4xOTGTYw+tEEEj0z29c6GIuGM0t6lBYX9agWBGEi+EKaY9mPmeUC1wEnARuBl8zsAXdfmY76dtU2cNXfV3F3+UbGDOnH7y84hjk6GUlEMqyspLDnJIjwTOr2Ol7d3S9MbUj7HAOsDa9NjZndBZwGpDRBuDsPLH2XHz+0kp21jVw0dzzfOHEi/Qp0ER8RybyykiKWb9oTdRhAci2IhxIsGwNcStD1ky6jgHfi7m8Ejk11Jfe/uolL/7yU6WMG8/sLpjH54JJUVyEikrTSkiK219TT2ByL/OJWyRyDuLfltpkdRnBweg7wP8At6Qutc2a2AFgAMHbs2G49x8enHUxTs/eKawSISM9XVlKIezA548hB0Y5iSyo9mdkkM7sTeBB4Fpjs7te7e0MaY9tE0FJpMTpcto+73+Tus9x91vDh3TteUJCXw1mzxig5iEhWKCvOnnMhOk0QZnYP8HdgMXA88ABQYmZDzCydpxK/BEw0s0PNrAA4O6xbRKTXaplEMBsOVCdzDOJogoPU3wYuC5e17G47cFga4sLdm8zsEuBRgmMdt7r7a+moS0QkW5SG8zFV9IQE4e7jMhBHe3X/naD1IiLSJwwdUEhujvWMLqZEzGy8mf3QzLRHLyKSQrk5xvCB2XEuRNIJwswONrNLzewl4LWw7Nlpi0xEpI8qKylka1UPaEGY2QIzewpYCAwFLgQ2u/sV7r48zfGJiPQ5pSVFbN0dfQsimYPUvyYYwfQ5dy8HMLOOL2QtIiLdVlZSmBUXs0omQYwEzgKuMbMRwN1AflqjEhHpw8qKi9hV20hdYzNF+dFN+9NpF5O7b3f3G9x9LsHFgnYBW81slZldlfYIRUT6mJYLB1VGfByiS6OY3H2ju1/j7rOAU4HoO8lERHqZ0iy5NnUys7me0cHDab0ehIhIX9R6NnW0LYhkjkF8ss3tB+PuO/DXlEYkItLHtc7HlOUtCHf/YsttM3sl/r6IiKTe4P75FOTmsLUq2gTR1TOpNbxVRCTNzIzSkkIqIu5iivZqFCIiklBZSfTXpk7mIPWDtLYcDjOz/abcdvdT0xGYiEhfVlZSyOotVZHGkMxB6p/F3b4mXYGIiEir0uIinl6zLdIYkkkQK4Hh7r4yfqGZTQYq0xKViEgfV1ZSRHV9E9X1TQwsTOanOvWSOQbx/4BhCZYPBX6Z2nBERASCLiaI9sJBySSICe7+dNuF7v4McGTqQxIRkZbpNqI8WS6ZBFHcwWOatE9EJA32tSAiPBcimQSx1sw+1nahmX0UeDP1IYGZ/cjMNpnZq+Hfe+oXEenNWlsQ0SWIZI58fBN42Mw+DSwJl80CZgOfSFdgwLXu/rPOVxMR6X0GFubRvyA3u7uY3P0NYBqwCBgX/i0CjnT3NekMTkSkrzKzyE+WS2rslLvXA7/raB0zW+zus1MSVeASM/sCUA5c5u47E9S5AFgAMHbs2BRWLSISvdLiaKfbSOVUG0VdWdnMnjCzFQn+TgOuB8YDM4DNtHOCnrvf5O6z3H3W8OHDD3gDRESySVlJUaQT9qXy7IsuTeTn7h9JZj0z+y3wULciEhHpwcpKCtm6pw53x8wyXn9WTtZnZiPj7p6OLkwkIn1QWUkRdY0x9uxtiqT+VLYgUpnefmJmMwhaJeuBr6TwuUVEeoTSlqGuVXUM6p/5086Smc31MXc/OYnnOjcF8QDg7il7LhGRnqqsuPXa1IeXdXTOcnok08WU1NFfd1c3kIhICkU93UYyXUyDzOyM9h50d12TWkQkDaI+mzqpBEFwxnSiYwwOKEGIiKRBv4JcSoryIpvRNZkE8ba7X5D2SERE5D2Cs6mj6WJK5hhE5gffiogIEO3JcskkiPPM7INtF5rZB81sfBpiEhGRUGlJdNNtJJMgrgL2JFi+B/hFasMREZF4ZSVFVFTVEYt1abKKlEgmQZS5+/K2C8Nl41IekYiI7FNWXEhjs7OztiHjdSeTIAZ38Fi/VAUiIiLv1TLUdUsEI5mSSRDlZvbltgvN7Eu0XkBIRETSoGW6jSiOQyR7Rbn7zOzz7H9FuQKCifRERCRNWq5NHcXJcp0mCHffChxnZvOAqeHih939n2mNTEREKC2ObrqNZCbrKwIuAiYAy4Fb3D2auWdFRPqYgrwchg4oiORciGSOQdxO0KW0HPgo8LO0RiQiIvspLSmKZLqNZI5BTHb3aQBmdgvwYnpDEhGReMGV5TLfxZRMC6Kx5Ya6lkREMq+suCg7D1ID082s5UxqA/qF9w1wdy9JW3QiIkJZSSHbqutpao6Rl5u5K0V3WpO757p7SfhX7O55cbe7nRzM7Cwze83MYmY2q81jl5vZWjNbbWandLcOEZHeoLSkiJjD9prMnk2duVT0XiuAM4Cn4xea2WTgbGAKMB/4jZnlZj48EZHssO9s6t2Z7WaKLEG4+yp3X53godOAu9y93t3fAtYCx2Q2OhGR7BHVyXJRtiDaMwp4J+7+xnDZe5jZAjMrN7PyysrKjAQnIpJp+y49WpXZkUzJHKTuNjN7AhiR4KEfuPvfDvT53f0m4CaAWbNmZX4uXBGRDBg6oIAcI+PnQqQ1Qbj7R7pRbBMwJu7+6HCZiEiflJebw/DiQnUxAQ8AZ5tZoZkdCkxEJ+eJSB8XxbWpI0sQZna6mW0EZgMPm9mjAO7+GnA3sBJ4BLjY3ZujilNEJBuURnCyXFq7mDri7vcB97Xz2JXAlZmNSEQke5WVFPLyhp0ZrTMbu5hERKSNspIidtQ0UN+UuQ4VJQgRkR6g5VyIygwOdVWCEBHpAVouPZrJA9VKECIiPUDZvivLZe5AtRKEiEgPEMV0G0oQIiI9wEH9C8jPNXUxiYjI/nJyjNLizF56VAlCRKSHKCspZGuVEoSIiLSR6ek2lCBERHqIIEGoBSEiIm2UlhRSVddEbUNTRupTghAR6SFazoWoyFA3kxKEiEgPse/KchnqZlKCEBHpIfadLJeh+ZiUIEREeoh98zHtVgtCRETilBTlUZSfoy4mERHZn5kFQ117exeTmZ1lZq+ZWczMZsUtH2dme83s1fDvhqhiFBHJNpk8FyKyS44CK4AzgBsTPLbO3WdkOB4RkaxXVlLE8o27MlJXZC0Id1/l7qujql9EpCcqKy5k65563D3tdWXrMYhDzewVM1tkZh9ubyUzW2Bm5WZWXllZmcn4REQiUVZSxN7GZqrq0382dVq7mMzsCWBEgod+4O5/a6fYZmCsu283s5nA/WY2xd33tF3R3W8CbgKYNWtW+tOpiEjESsNzISr21FFSlJ/WutKaINz9I8vm5+YAABDMSURBVN0oUw/Uh7eXmNk64HCgPMXhiYj0OGVx16aeUFqc1rqyrovJzIabWW54+zBgIvBmtFGJiGSHTE63EeUw19PNbCMwG3jYzB4NH5oDLDOzV4G/ABe5+46o4hQRySalxS3Xpk7/uRCRDXN19/uA+xIsvxe4N/MRiYhkvwGFeRQX5vXuFoSIiHRPaUmhEoSIiLzXiEGZOZtaCUJEpIcpK87MtamVIEREepjSkiIqqurSfja1EoSISA9TVlJIY7Ozs7YxrfUoQYiI9DCZOhdCCUJEpIfZd+lRJQgREYlXWhy0ICrSfKBaCUJEpIcpVQtCREQSKczL5aD++WytUoIQEZE2ykqK2LJbXUwiItJGWXguRDopQYiI9EBlGZiPSQlCRKQHKisporKqnuZY+s6mVoIQEemBSkuKiDlsr07fcQglCBGRHqgsAxcOUoIQEemBMjHdRpSXHP2pmb1uZsvM7D4zGxz32OVmttbMVpvZKVHFKCKSrfYliDSOZIqyBfE4MNXdjwTWAJcDmNlk4GxgCjAf+I2Z5UYWpYhIFho2sACzXtrF5O6PuXtTePd5YHR4+zTgLnevd/e3gLXAMVHEKCKSrfJycxg2sJCK3tjF1MYFwD/C26OAd+Ie2xguew8zW2Bm5WZWXllZmeYQRUSyS7rPhUhrgjCzJ8xsRYK/0+LW+QHQBPyhq8/v7je5+yx3nzV8+PBUhi4ikvXKiovYksYupry0PTPg7h/p6HEzOx/4BHCit147bxMwJm610eEyERGJUzaoiFff2ZW2549yFNN84LvAqe5eG/fQA8DZZlZoZocCE4EXo4hRRCSblRUXsb2mgYamWFqeP60tiE78GigEHjczgOfd/SJ3f83M7gZWEnQ9XezuzRHGKSKSlVquLFdZXc+owf1S/vyRJQh3n9DBY1cCV2YwHBGRHif+ZLl0JIhsGcUkIiJd1HJluXQNdVWCEBHpoVpbEOkZyaQEISLSQw3pX0BejqXtXAglCBGRHionxzhtxigOHTYgLc8f5SgmERE5QNd8enranlstCBERSUgJQkREElKCEBGRhJQgREQkISUIERFJSAlCREQSUoIQEZGElCBERCQha71OT89mZpXA290sPgzYloVlsjWu7pRRXIornWUUV9fLtDjE3RNfktPd+/wfUJ6NZbI1rt60LYpLcfXFuJL9UxeTiIgkpAQhIiIJKUEEbsrSMtkaV3fKKK7sq6M7ZRRX9tXR3TKd6jUHqUVEJLXUghARkYSUIEREJLF0DI3Khj/gVqACWBG3bDqwGFgOPAiUhMsLgN8BO4BG4K24MjOBrUA9UAWcE1dmDdAE7AWOD5fPBmrCZbuB7cAvOqnj3+PKVADj4ur4M1AN1AFvAd8IH7sgjKc+XH5QuNzCba8Ny6yLK/O1cHkDsCLJMk+G8dYAj8eVGQNsDJ+rDvhpuHwOsCvu9bqgozqAQ4ClcduyKsm4hgD/DGPbkERcQwCPi+sfSdQxLXz/GsL34MhOtmUewWerZVuagc8nEdf6cP1q4Mbw+TuK65fhuvXAsrhtnwQsAWIEn9nX4sqcGb6HDcAbcWWOJ/icxoAtbV7fTQSf7+rO6gi375m4uF5PokxRuLzl9VqXZFyPh9uwB3g0iW3fEFfH7iRfr3Hhspb3/uROtuUI9n/vm4DLk9iW1bS+938JX5OO4ro87jVeHbctnyf4LCwHngOmx/2+zA/XXQt8r0u/o1H/kKcxQcwBjmL/BPESMDfuB/bH4e2LCX685wAnEvxQ54SPbQJ+H96+EPhZXJm/h3WsCt/QnAR1bA6fN2EdBFf1awRODcv8A1gUV8ddYR2lwCsESWk2wZejJf4lwF3h7Y8R/HAeBXwgjKelzC7gCuAgwsTVSZnJYf23AQ8B3wP+N+4D+S+CH7ITwu2ZDPyW1h+/K8MP8+AO6pgOXBM+90BgJ3BdEnH9JKz/jwRfiM7i+glQH64Tvx0d1bEBuDlc7z+Ba5KM63sEX/7auDLtxXUnQYLIJfjybyL4QWmvjovC9b8PDADeAa4N6ygl+DG4Bfg2UByWmRq+rj8h2OnYHLddU4FzwvfqB22244aw/i1xr1d7dcwBbg+3vRiojKujvTKTgWvDMvnh631HEnF9D/gW8CqwNok6dgP/neC976jMCuDecL0f0Ppd6ahMS2y5BIni+k625TcEO4z9wnKvA+d3UMep4ev6Q4LfjTeAG8M6jqM1WXwUeCG8nUuQeA8L3/ulwOQ+nyDCF2cc+yeI3bQemB8DrAxvXwecG1emGjgmvB8DBrRXpqUOgj3tY9rUMYfgx9/aq4PgixEj2JM24A7g3bZxhfefBBYRJI4aYGS4/BJgZ3j7RuCzcWVWA48AVxEkiJYy8fW0V+ak8PYZBAliJLC6nTJVwGfC9VvqGEmw9zsxiTpGAkMJ9rLXJhHXeuB+gi/UrUnGVRMXV3vrt9RxIcEP/MgulInflgXAvUnE9TbBl7YfwZe4DnhfB3XcRHDGbEtcf2x5H+PW/RHw7fD234Cvs//n5SqgMlGZcP347RhH8MO1upM69pUJlz0CvJNsGaA/wR7w20nEdRTBd+FMoDqJOhqBKW3fxw7KnEbQcnjPe5/ktpwMvNhembj11xHsEAwBRhPXUmmnjh+z/3f4f4GKBL97BwGbwtuzCVtZ4f3LCVs2yfz1tWMQrxG8+QBnEfzgQ/AFPdXM8gjeqH7AGDMbTPDh+qOZvUzQLTU2vgxBhs4n6Ioa06aOywH34J1JWIe7NxLsCawC3iXYEyhuG5eZHQocTbDn8WBYvjB8vnkEe98Aowj2KltsA2YQ/EAUuvvmcPkagg9SR2VeAMoI9nIg2JMsa1vGzMaFr8EWoCyujjHh8nWd1DECeDR8/GqCaQM6iuvF8DW8JFxem0xc4etVDtwXlu+ojtqw7HVm9grBHut76kj0eoXbfzZBi7GzuAYRdJlsJmgh4u6rOqjj7wQtst1mNoygdXEQCYT1vJ+g9ZAX976sAkoSFDkoXD9+OyDoLilLsH58HfvKhMsm0/o57rAMQau5Ivw/qLO4CFpP3yV4TQqTqCMHuMPMlgCfTGJbtoVlrg7f+x8nUabte//7dsrEb8tQglbHBoL3vsDdH+ugjr8RvKYNZtafYAc00et1IcFrCe/9HG0MlyWlryWIC4CvhR+UYoK9BAj2QDcC5QRdCbUE/cd5BM2yQwj6r7cQfLHjyzxIsNfwXFgmvo6Z4XO1W4eZ5RP8AC8hSBDbCJJO2zL/L4zhFnffEJb/M0G/7/rwsf2Y2UCCfvRrCPos2+qwjLvv2W/lINF5gvXvJUg4NXHLRxK0UmrcPdZJHe7uRwITgPMSxNl2W84FGt19Y3vb0k5ch7j7LOBzQJGZje+gjkaC9//bBIn5MFrf+462pWXbpxEkvc7iyiFoMYwm+OLmmdmHO6jj/jC254A/ERxTe8/7SPC5vRf4JkF3VlttyxQQvPbfbPu+t7P+fnXEbXvL9n0z2TLuPoNg+48haEV3FFcewV7zki7EVe3uRxF0vVxM6/crYRmC70ouQRfR+wl3rpLc/gKCHcd7EsTWdluMYGfyUODgsPw5HdRRHsb2GEEL7dW2dZjZPIIE8e8J4u2yPpUg3P11dz/Z3WcSfLnWhcub3P3S8IO6gODDsYagn74WOCoscxVBEthXhqCveAPBXt2aljoIEkVN+Dwd1TEDqHX3D4d1/JIwccXVcTTBj9MOgj0TCJqmn3L32QSJqzpu+Zgw8dxL0GVxZ7i8PvzxAjicoLnaURkIDpINgX0/fBVxZcaF6/+B4Au0CdhqZhOBh4GfEuwZd1qHmY1093fD96SmkzKzCX5I3wF+RvCli8WVSRhX3Dp7w9fr/R3UsTFcb6+7NwFP0bpD0dnr9SWCVsqwJF6vOoJu0GqCnZZd4fZ1VMcG4KPufhJBS7KlhdciJ3xN/uDufw2fqynuvX8fQRcXAGEdnwZeDteH8D0Jb+fFbUd7dbRs+4Ph9i1Otkz43u8iOM6yb0emnbjqgU+Z2XrgboLPQcvr0l4dW8I6Kghaa20TZtsyGwlaTRvCx58i/N4nsf2fA14OH9+3/e1sSzWwxd0rCT4r2wl6EDqq4x3gE+4+h+DzuDuujiOBm4HT3H17uHgTrT0lECTiTSSpTyUIMysN/+cA/0FwEA4z629mA8LVPkSwR7sy3GN+DDg+LHM1QRdS2zIDgCZ3X9lSB/BZggPJHdZB8GZNNbPhYR3fI2h+xpe5heBH852wDMATwHlmdhBwKcGPCMADwBfCMjuB9WGz99Hw8YvCMh8nGDXRUZmWx+aHt88jaOa2LP9fgu6K54DdYZmHCJq3vyf40Mev/546zGw0QbdJy7Z8JC7WhGXc/fMEI8OuI9jDXx6u01FcjxIkbYCvEnzhV3aw7S8RfIG/FpY5n6All8zr9WWCHZBkXq/ngTPCrsIvErzPqzp4vXIJPpPnhT8Ix4V1AWBmLXulW9395+HilwhaHd8M93C/GD5/y/q3ELRcn6bVA7S25gbHbUfCOsJlTUB+uOy8JMoMD9+X88ysH0G37zOdxHUr8Ct3H0dwDOpNdz+ngzoGEHwezwtvn0VwHK/duNx9C8GP/WXhapcQfFY6eo1bXrNLafPed7AtTwEnht1F5xEkiFWd1NHyvR9L8BtzV7j+WOCvBMcs18St/xIw0cwODd/7s8M4k5PswYqe9he+SZsJvhgbCZpd3yDYa18D/A+tB5PHERxg2k2whxJf5kcErYh6gq6csXFl9oTrxsK64utoIDigmEwdfw6X1RMcjxgaV2YDQTOyiiA5vUrQarmX4MeknuAHckhYxsIPihPsKa2OK/N/aB3m+lqSZRaH68cI9mLPDMt8iNZhoy1lPkYwyiZG63DS5QStpPbq+EEYS/ww12TiGkpwoHILwV7VkE7imk/r8MBq4OtJ1HFGXFzvEvQxd1ZmRvg6vUHwZe4sruEEOwktwyl/00kdp4W3W4ZFPx9XxwiCvVYnSIANBAd+P0ZwQLzlvV8bV+bUuPVbyvxb+PpuIfjRj4UxXthBHd+m9XPa8t5/upO4vhb+jx/mmkxcT4av7xJah7m2V8cFBINIWuqI3/aOXq+5BN/v+nCdQ5MoM4bge72uzXvf0ba8Gffe/5mgZdlRHYtp/d4viavjZoIdiVfDv/K438KPEfwmrQN+0JXfUU21ISIiCfWpLiYREUmeEoSIiCSkBCEiIgkpQYiISEJKECIikpAShIiIJKQEIZJFwhPhRLKCEoRIN5nZf5vZN+PuX2lm3zCz75jZS2a2zMyuiHv8fjNbYmavmdmCuOXVZnaNmS0lmGZDJCsoQYh0360E02G0TN9yNsHZxxMJJp6bAcw0sznh+hd4MN/WLODrZjY0XD6AYP7+6e7+bCY3QKQjeVEHINJTuft6M9tuZu8nmNr5FYKJFU8Ob0MwDftEgjl4vm5mp4fLx4TLtxNMp3AvIllGCULkwNxMMJHfCIIWxYnA1e5+Y/xKZnY8wUSEs9291swWElxeEqDO3dvOFioSOXUxiRyY+wgmAjyaYGbSR4ELwusiYGajwhl+BxFc9a/WzCYRXOhHJKupBSFyANy9wcyeAnaFrYDHzOx9wOJgxmaqCa5H/AjBVOurCGZjfT6qmEWSpdlcRQ5AeHD6ZeAsd38j6nhEUkldTCLdZGaTCa4v8KSSg/RGakGIiEhCakGIiEhCShAiIpKQEoSIiCSkBCEiIgkpQYiISEL/HxsB8ygRpdvyAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.lineplot(x='year',y='PCT_CHANGE', data=df_UMBC)" ] }, { "cell_type": "markdown", "metadata": { "id": "V1TiCardmjU2" }, "source": [ "## Step 18 - Define Growth Rate Function\n", "\n", "Since we want to do the same calculation for JHU. Instead of doing it piecemeal as we did for UMBC. Let's create a function which can be reused for any college. This function takes a list of tuitions and return a list of percentage changes year over year. This function can be used later." ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "id": "eCqEe_646zMc" }, "outputs": [], "source": [ "#(Write code here)\n", "def percent_changed(tuition_Growth):\n", " percent_Growth = [0]\n", " for i in range(1,len(tuition_Growth)):\n", " difference = (tuition_Growth[i]-tuition_Growth[i-1])/tuition_Growth[i-1]\n", " percent_Growth.append(difference*100)\n", " rounded_list = []\n", " for i in percent_Growth:\n", " rounded_list.append(round(i,2))\n", " return rounded_list\n", " " ] }, { "cell_type": "markdown", "metadata": { "id": "U2jqd5GYmy3Z" }, "source": [ "## Step 19 - Get JHU Data\n", "\n", "The dataframe contains many years of data of all U.S. colleges. Let's just look at JHU. Filter/query the dataframe to retrieve only rows that belong to JHU. Save the JHU data to a new data frame using a new variable so that the old big data frame is still available for later use." ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "f2mhYOjV6zMj", "outputId": "5a1767dc-3a2e-4bc4-891a-3129e8fda95c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 25 entries, 2139 to 171396\n", "Data columns (total 4 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 UNITID 25 non-null int64 \n", " 1 INSTNM 25 non-null object \n", " 2 TUITIONFEE_IN 21 non-null float64\n", " 3 year 25 non-null object \n", "dtypes: float64(1), int64(1), object(2)\n", "memory usage: 1000.0+ bytes\n" ] } ], "source": [ "#(Write code here)\n", "df_JHU = df_all[df_all['INSTNM']=='Johns Hopkins University']\n", "df_JHU.info()" ] }, { "cell_type": "markdown", "metadata": { "id": "VkyEUa6mnKgD" }, "source": [ "## Step 20 - Plot JHU's in-state tuition overtime\n", "\n", "Let's plot bar and then line chart." ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "id": "wGXcNTJ46zMo", "outputId": "73594b62-a9e0-4975-a72f-854120d78a93" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# <14> \n", "# Plot JHU's in-state tuition overtime from 1996 to 2019. \n", "# Let's plot bar and line chart together\n", "\n", "#(Write code here)\n", "\n", "sns.barplot(x='year',y='TUITIONFEE_IN',data=df_JHU)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "id": "n66sT0m3ANrM", "outputId": "568ea5ef-abe8-463e-fa20-ee4bef90071f" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.lineplot(x='year',y='TUITIONFEE_IN',data=df_JHU)" ] }, { "cell_type": "markdown", "metadata": { "id": "wNn0AXexnsxq" }, "source": [ "## Step 21 - Calculate Tuition Growth Rate using the Function defined earlier\n" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "id": "q5xcRlLMn4Ap" }, "outputs": [], "source": [ "# Write code here\n", "tuition_Growth = df_JHU['TUITIONFEE_IN'].tolist()\n", "percent_Growth = percent_changed(tuition_Growth)" ] }, { "cell_type": "markdown", "metadata": { "id": "HSPcfvsGn8MG" }, "source": [ "## Step 22 - Add the list of percentage changes to the dataframe as a new column (\"PCT_CHANGE\")" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7KM74FQH6zM4", "outputId": "3cc64967-5abb-45ce-f3bc-b93cac92c647" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " \n" ] } ], "source": [ "#(Write code here)\n", "df_JHU['PCT_CHANGE'] = percent_Growth" ] }, { "cell_type": "markdown", "metadata": { "id": "LPei7NtpoFFb" }, "source": [ "## Step 23 - Plot the JHU percentage changes tuitions over time\n", "\n", "Bar plot first, then line plot." ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "id": "vGi8YI_A6zM7", "outputId": "46fc6e99-b3de-4e7e-90ce-774985bd9015" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#(Write code here)\n", "sns.barplot(x='year',y='PCT_CHANGE',data=df_JHU)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "id": "YiVkeHd1A9u8", "outputId": "ea49a33b-7641-47f0-85af-2793977d0553" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.lineplot(x='year',y='PCT_CHANGE',data=df_JHU)" ] }, { "cell_type": "markdown", "metadata": { "id": "CBHqIJBcoXv-" }, "source": [ "## Step 24 - Compare UMBC and JHU \n", "\n", "In order to plot both UMBC and JHU tuition change over time in the same plot, we need to combine the two datasets using the common key of YEAR. \n", "\n", "First make a umbc2 dataframe with only two columns needed. We don't need other columns. Also change the column name from \"PCT_CHANGE\" to \"UMBC_PCT\" in preparation for the merge. This is because both umbc and jhu dataframe have the same column name \"PCT_CHANGE\", we rename them to there is no collision during the merge. BTW, Pandas handles collision gracefully, Feel free to try it without changing the column names." ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 937 }, "id": "bGYYULq16zNA", "outputId": "bca4b2dd-b8a9-438e-dd59-ba048c2ed346" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yearUMBC_PCT
215119960.00
90561997NaN
159231998NaN
225981999NaN
291782000NaN
3582120017.65
4252420027.65
49156200316.13
5578620048.55
6252220056.23
6940120061.20
7633220071.00
8329120080.83
9032420091.05
9750620103.37
10496020113.23
11267220123.14
12050320133.11
12835320143.14
13606420155.99
14368520162.34
15089120172.25
15794020182.26
16472320192.12
1714012020-21.68
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " year UMBC_PCT\n", "2151 1996 0.00\n", "9056 1997 NaN\n", "15923 1998 NaN\n", "22598 1999 NaN\n", "29178 2000 NaN\n", "35821 2001 7.65\n", "42524 2002 7.65\n", "49156 2003 16.13\n", "55786 2004 8.55\n", "62522 2005 6.23\n", "69401 2006 1.20\n", "76332 2007 1.00\n", "83291 2008 0.83\n", "90324 2009 1.05\n", "97506 2010 3.37\n", "104960 2011 3.23\n", "112672 2012 3.14\n", "120503 2013 3.11\n", "128353 2014 3.14\n", "136064 2015 5.99\n", "143685 2016 2.34\n", "150891 2017 2.25\n", "157940 2018 2.26\n", "164723 2019 2.12\n", "171401 2020 -21.68" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#(Write code here)\n", "umbc2 = df_UMBC[['year']]\n", "umbc2['UMBC_PCT'] = df_UMBC['PCT_CHANGE']\n", "umbc2" ] }, { "cell_type": "markdown", "metadata": { "id": "W23PSBYmozzi" }, "source": [ "## Step 25 \n", "\n", "Then make a jhu2 dataframe with only two columns needed. WE don't need other columns. Also change the column name from \"PCT_CHANGE\" to \"JHU_PCT\" in preparation for the merge" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 937 }, "id": "DX95nFyG6zNF", "outputId": "f78aee30-e1d6-4300-8201-dad581692289" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yearJHU_PCT
213919960.00
90461997NaN
159141998NaN
225891999NaN
291692000NaN
3581220015.13
4251520024.50
4914820034.89
5577920044.91
6251520054.91
6939420067.21
7632520075.90
8328420085.01
9031720093.85
9749920103.91
10495320113.93
11266520122.63
12049620134.79
12834620143.50
13605720153.51
14367820163.49
15088620173.49
15793520183.01
16471820193.00
1713962020-2.15
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " year JHU_PCT\n", "2139 1996 0.00\n", "9046 1997 NaN\n", "15914 1998 NaN\n", "22589 1999 NaN\n", "29169 2000 NaN\n", "35812 2001 5.13\n", "42515 2002 4.50\n", "49148 2003 4.89\n", "55779 2004 4.91\n", "62515 2005 4.91\n", "69394 2006 7.21\n", "76325 2007 5.90\n", "83284 2008 5.01\n", "90317 2009 3.85\n", "97499 2010 3.91\n", "104953 2011 3.93\n", "112665 2012 2.63\n", "120496 2013 4.79\n", "128346 2014 3.50\n", "136057 2015 3.51\n", "143678 2016 3.49\n", "150886 2017 3.49\n", "157935 2018 3.01\n", "164718 2019 3.00\n", "171396 2020 -2.15" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#(Write code here)\n", "jhu2 = df_JHU[['year']]\n", "jhu2['JHU_PCT'] = df_JHU['PCT_CHANGE']\n", "jhu2" ] }, { "cell_type": "markdown", "metadata": { "id": "Q19tgEntpBsD" }, "source": [ "## Step 26 - Merger the two data frames and display the new dataframe" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "LJ_6wKKU6zNL", "outputId": "21dd0a94-3f66-4d05-9203-1121365b6447" }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yearUMBC_PCTJHU_PCT
019960.00.0
11997NaNNaN
21998NaNNaN
31999NaNNaN
42000NaNNaN
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " year UMBC_PCT JHU_PCT\n", "0 1996 0.0 0.0\n", "1 1997 NaN NaN\n", "2 1998 NaN NaN\n", "3 1999 NaN NaN\n", "4 2000 NaN NaN" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#(Write code here)\n", "merged_dfs = pd.merge(umbc2,jhu2)\n", "merged_dfs.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "dWdOJl19pXud" }, "source": [ "## Step 27 - Plot UMBC and JHU separately on the same line plot " ] }, { "cell_type": "markdown", "metadata": { "id": "hyClyzNepVrH" }, "source": [] }, { "cell_type": "code", "execution_count": 85, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 312 }, "id": "Ng3bbP076zNW", "outputId": "e5acdbad-97b0-449b-bc84-c64a547bc60d" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#(Write code here)\n", "merged_dfs.plot.bar(x='year',y=[\"UMBC_PCT\",\"JHU_PCT\"])" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "id": "6YNmMkgW6zNv", "outputId": "25110c37-4785-47fe-b6cd-204e21120cde" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "merged_dfs.plot.line(x='year',y=[\"UMBC_PCT\",\"JHU_PCT\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "Pw6GMGUwpjz6" }, "source": [ "## Step 28 - Document your observation of the comparison plots using the following Markdown cell" ] }, { "cell_type": "markdown", "metadata": { "id": "tzeP_v8E6zNz" }, "source": [ "#### Based on my observation of the plots\n", "#### blah, blah \n", "#### blah, blah\n", "#### ...\n", "#### blah, blah" ] }, { "cell_type": "markdown", "metadata": { "id": "rk4SRLcSBlta" }, "source": [ "#According to the plots, it is clear that the tuition costs for UMBC students have changed significantly over time. Sometimes there has been a sharp spike in the fees, followed immediately by a sharp decrease.\n", "#On the other side, JHU's tuition costs haven't undergone a particularly dramatic adjustment, though there have been some noticeable increases and decreases throughout time." ] }, { "cell_type": "markdown", "metadata": { "id": "8UBlFqP3BulA" }, "source": [] } ], "metadata": { "colab": { "name": "assignment_07.ipynb.txt", "provenance": [] }, "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 1 }