{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# STOR 120: Take Home Midterm 1\n", "\n", "**Due:** Friday, September 17, 9:05 am on Gradescope\n", " \n", "**Directions:** The...

1 answer below »
Midterm Assignment for Introductory Coding Class


{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# STOR 120: Take Home Midterm 1\n", "\n", "**Due:** Friday, September 17, 9:05 am on Gradescope\n", " \n", "**Directions:** The exam is open book, notes, course materials, internet, and all things that are not direct communication with others. Just as with all course assignments, you will submit exams to Gradescope as Jupyter Notebooks with the ipynb file extension. To receive full credit, you should show all of your code used to answer each question." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Data:** The dataset used on this exam contains information on the number of students that majored in different topics of study at universities in the United States in 2019 and is broken down by age group, sex, and state. The original source of the data is the US Census Bureau, but this dataset was found on [Kaggle.com](https://www.kaggle.com/tjkyner/bachelor-degree-majors-by-age-sex-and-state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Run the cell below to import the dataset.**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "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", "






































































StateSexAge GroupBachelor's Degree HoldersScience and EngineeringScience and Engineering Related FieldsBusinessEducationArts, Humanities and Others
AlabamaTotal25 and older885,357
263,555
98,445
210,147
141,071
172,139
AlabamaTotal25 to 39
268,924
90,736
32,378
58,515
29,342
57,953
AlabamaTotal40 to 64
418,480
115,762
46,724
112,271
63,875
79,848
AlabamaTotal65 and older197,953
57,057
19,343
39,361
47,854
34,338
AlabamaMale
25 and older405,618
159,366
26,004
113,909
29,490
76,849
" ], "text/plain": [ "State | Sex | Age Group | Bachelor's Degree Holders | Science and Engineering | Science and Engineering Related Fields | Business | Education | Arts, Humanities and Others\n", "Alabama | Total | 25 and older | 885,357 | 263,555 | 98,445 | 210,147 | 141,071 | 172,139\n", "Alabama | Total | 25 to 39 | 268,924 | 90,736 | 32,378 | 58,515 | 29,342 | 57,953\n", "Alabama | Total | 40 to 64 | 418,480 | 115,762 | 46,724 | 112,271 | 63,875 | 79,848\n", "Alabama | Total | 65 and older | 197,953 | 57,057 | 19,343 | 39,361 | 47,854 | 34,338\n", "Alabama | Male | 25 and older | 405,618 | 159,366 | 26,004 | 113,909 | 29,490 | 76,849" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from datascience import *\n", "import numpy as np\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plots\n", "plots.style.use('fivethirtyeight')\n", "\n", "import warnings\n", "warnings.simplefilter('ignore'
stor-120-003-midterm-1-0dv1nydq.ipynb
Answered Same DaySep 17, 2021

Answer To: { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# STOR 120: Take Home Midterm...

Pritam Kumar answered on Sep 17 2021
134 Votes
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# STOR 120: Take Home Midterm 1\n",
"\n",
"**Due:** Friday, September 17, 9:05 am on Gradescope\n",
" \n",
"**Directions:** The exam is open book, notes, course materials, internet, and all things that are not direct communication with others. Just as with all course assignments, you will submit exams to Gradescope as Jupyter Notebooks with the ipynb file extension. To receive full credit, you should show all of your code used to answer each question."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Data:** The dataset used on this exam contains information on the number of students that majored in different topics of study at universities in the United States in 2019 and is broken down by age group, sex, and state. The original source of the data is the US Census Bureau, but this dataset was found on [Kaggle.com](https://www.kaggle.com/tjkyner/bachelor-degree-majors-by-age-sex-and-state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Run the cell below to import the dataset.**"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
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"
StateSexAge GroupBachelor's Degree HoldersScience and EngineeringScience and Engineering Related FieldsBusinessEducationArts, Humanities and Others
0AlabamaTotal25 and older885,357263,55598,445210,147141,071172,139
1AlabamaTotal25 to 39268,92490,73632,37858,51529,34257,953
2AlabamaTotal40 to 64418,480115,76246,724112,27163,87579,848
3AlabamaTotal65 and older197,95357,05719,34339,36147,85434,338
4AlabamaMale25 and older405,618159,36626,004113,90929,49076,849
\n",
"
"
],
"text/plain": [
" State Sex Age Group Bachelor's Degree Holders \\\n",
"0 Alabama Total 25 and older 885,357 \n",
"1 Alabama Total 25 to 39 268,924 \n",
"2 Alabama Total 40 to 64 418,480 \n",
"3 Alabama Total 65 and older 197,953 \n",
"4 Alabama Male 25 and older 405,618 \n",
"\n",
" Science and Engineering Science and Engineering Related Fields Business \\\n",
"0 263,555 98,445 210,147 \n",
"1 90,736 32,378 58,515 \n",
"2 115,762 46,724 112,271 \n",
"3 57,057 19,343 39,361 \n",
"4 159,366 26,004 113,909 \n",
"\n",
" Education Arts, Humanities and Others \n",
"0 141,071 172,139 \n",
"1 29,342 57,953 \n",
"2 63,875 79,848 \n",
"3 47,854 34,338 \n",
"4 29,490 76,849 "
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plots\n",
"plots.style.use('fivethirtyeight')\n",
"\n",
"import warnings\n",
"warnings.simplefilter('ignore', FutureWarning)\n",
"\n",
"edu = pd.read_csv('D:\\\\New\\\\Bachelor_Degree_Majors.csv')\n",
"edu.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 1 (6 Points)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before analayzing the data, we will first need to clean the data. Start by creating a new table named **edu_clean** with the following changes:\n",
"- Rename the variables\n",
" - \"Bachelor's Degree Holders\" should become \"Total\"\n",
" - \"Science and Engineering\" should become \"ScEn\"\n",
" - \"Science and Engineering Related Fields\" should become \"ScEn Rel\"\n",
" - \"Arts, Humanities and Others\" should become \"Other\"\n",
"- Remove all observations where *Sex* is equal to *Total*"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"edu_clean = pd.read_csv('D:\\\\New\\\\Bachelor_Degree_Majors.csv')"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['State', 'Sex', 'Age Group', 'Bachelor's Degree Holders',\n",
" 'Science and Engineering', 'Science and Engineering Related Fields',\n",
" 'Business', 'Education', 'Arts, Humanities and Others'],\n",
" dtype='object')"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edu_clean.columns"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"
StateSexAge GroupTotalScEnScEn RelBusinessEducationOther
0AlabamaTotal25 and older885,357263,55598,445210,147141,071172,139
1AlabamaTotal25 to 39268,92490,73632,37858,51529,34257,953
2AlabamaTotal40 to 64418,480115,76246,724112,27163,87579,848
3AlabamaTotal65 and older197,95357,05719,34339,36147,85434,338
4AlabamaMale25 and older405,618159,36626,004113,90929,49076,849
..............................
607WyomingMale65 and older16,4829,3751,1452,0112,3781,573
608WyomingFemale25 and older59,07415,5708,4706,85616,63811,540
609WyomingFemale25 to 3918,1806,7082,2681,9363,3133,955
610WyomingFemale40 to 6426,5375,1104,1943,8278,0075,399
611WyomingFemale65 and older14,3573,7522,0081,0935,3182,186
\n",
"

612 rows × 9 columns

\n",
"
"
],
"text/plain": [
" State Sex Age Group Total ScEn ScEn Rel Business \\\n",
"0 Alabama Total 25 and older 885,357 263,555 98,445 210,147 \n",
"1 Alabama Total 25 to 39 268,924 90,736 32,378 58,515 \n",
"2 Alabama Total 40 to 64 418,480 115,762 46,724 112,271 \n",
"3 Alabama Total 65 and older 197,953 57,057 19,343 39,361 \n",
"4 Alabama Male 25 and older 405,618 159,366 26,004 113,909 \n",
".. ... ... ... ... ... ... ... \n",
"607 Wyoming Male 65 and older 16,482 9,375 1,145 2,011 \n",
"608 Wyoming Female 25 and older 59,074 15,570 8,470 6,856 \n",
"609 Wyoming Female 25 to 39 18,180 6,708 2,268 1,936 \n",
"610 Wyoming Female 40 to 64 26,537 5,110 4,194 3,827 \n",
"611 Wyoming Female 65 and older 14,357 3,752 2,008 1,093 \n",
"\n",
" Education Other \n",
"0 141,071 172,139 \n",
"1 29,342 57,953 \n",
"2 63,875 79,848 \n",
"3 47,854 34,338 \n",
"4 29,490 76,849 \n",
".. ... ... \n",
"607 2,378 1,573 \n",
"608 16,638 11,540 \n",
"609 3,313 3,955 \n",
"610 8,007 5,399 \n",
"611 5,318 2,186 \n",
"\n",
"[612 rows x 9 columns]"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edu_clean = edu_clean.rename(columns={\"Bachelor's Degree Holders\" : \"Total\", \"Science and Engineering\" : \"ScEn\",\n",
" \"Science and Engineering Related Fields\" : \"ScEn Rel\",\n",
" \"Arts, Humanities and Others\" : \"Other\"})\n",
"\n",
"edu_clean #Do Not Change this Line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 2 (10 Points)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Currently, there is a problem. We have several variables in our dataset that are numeric, but the presence of commas will prevent us from answering key questions about the data. Notice the following python code which can convert the string \"1,030,452\" to an integer."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1030452"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"int(str.replace(\"1,030,452\",\",\",\"\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the next two code blocks, create a function named **str_to_int** and then use the method **apply** on the last 6 variables of the data in **edu_clean**. Create a new table named **edu_clean_int** that is similar to **edu_clean**, except that the last six variables are *int* arrays and not *str* arrays. You will not get full credit if you do not create a function or do not use the **apply** method."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"def str_to_int(series):\n",
" \n",
" for i in series:\n",
" i = str.replace(i,\",\",\"\")\n",
" i = int(i)\n",
" return series"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"edu_clean_change1 = edu_clean[[\"State\",\"Sex\",\"Age Group\"]]\n",
"edu_clean_change2 = edu_clean[[\"Total\", \"ScEn\", \"ScEn Rel\", \"Business\", \"Education\", \"Other\"]]"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
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"
TotalScEnScEn RelBusinessEducationOtherStateSexAge Group
0885,357263,55598,445210,147141,071172,139AlabamaTotal25 and older
1268,92490,73632,37858,51529,34257,953AlabamaTotal25 to 39
2418,480115,76246,724112,27163,87579,848AlabamaTotal40 to 64
3197,95357,05719,34339,36147,85434,338AlabamaTotal65 and older
4405,618159,36626,004113,90929,49076,849AlabamaMale25 and older
..............................
60716,4829,3751,1452,0112,3781,573WyomingMale65 and older
60859,07415,5708,4706,85616,63811,540WyomingFemale25 and older
60918,1806,7082,2681,9363,3133,955WyomingFemale25 to 39
61026,5375,1104,1943,8278,0075,399WyomingFemale40 to 64
61114,3573,7522,0081,0935,3182,186WyomingFemale65 and older
\n",
"

612 rows × 9 columns

\n",
"
"
],
"text/plain": [
" Total ScEn ScEn Rel Business Education Other State Sex \\\n",
"0 885,357 263,555 98,445 210,147 141,071 172,139 Alabama Total \n",
"1 268,924 90,736 32,378 58,515 29,342 57,953 Alabama Total \n",
"2 418,480 115,762 46,724 112,271 63,875 79,848 Alabama Total \n",
"3 197,953 57,057 19,343 39,361 47,854 34,338 Alabama Total \n",
"4 405,618 159,366 26,004 113,909 29,490 76,849 Alabama Male \n",
".. ... ... ... ... ... ... ... ... \n",
"607 16,482 9,375 1,145 2,011 2,378 1,573 Wyoming Male \n",
"608 59,074 15,570 8,470 6,856 16,638 11,540 Wyoming Female \n",
"609 18,180 6,708 2,268 1,936 3,313 3,955 Wyoming Female \n",
"610 26,537 5,110 4,194 3,827 8,007 5,399 Wyoming Female \n",
"611 14,357 3,752 2,008 1,093 5,318 2,186 Wyoming Female \n",
"\n",
" Age Group \n",
"0 25 and older \n",
"1 25 to 39 \n",
"2 40 to 64 \n",
"3 65 and older \n",
"4 25 and older \n",
".. ... \n",
"607 65 and older \n",
"608 25 and older \n",
"609 25 to 39 \n",
"610 40 to 64 \n",
"611 65 and older \n",
"\n",
"[612 rows x 9 columns]"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edu_clean_int = edu_clean_change2.apply(str_to_int, axis = 1)\n",
"\n",
"edu_clean_int[\"State\"] = edu_clean_change1[\"State\"]\n",
"edu_clean_int[\"Sex\"] = edu_clean_change1[\"Sex\"]\n",
"edu_clean_int[\"Age Group\"] = edu_clean_change1[\"Age Group\"]\n",
"\n",
"edu_clean_int #Do Not Change this Line"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question 3 (14 Points Total)\n",
"\n",
"#### 3.1 (6 Points)\n",
"Create a table called **Bachelors_by_Sex** based off **edu_clean_int** which has only two variables labeled \"Male\" and \"Female\". Each row in this table should contain the total number of bachelor's degrees based on people 25 and older for males and females for each state. In other words, every row is for a different state, but the name of the state should not be in **Bachelors_by_Sex**. Sort this table by \"Female\" in descending order."
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"new_df = edu_clean_int[[\"State\", \"Sex\", \"Age Group\", \"Total\"]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
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"
StateSexAge GroupTotal
4AlabamaMale25 and older405,618
8AlabamaFemale25 and older479,739
16AlaskaMale25 and older65,820
20AlaskaFemale25 and older80,337
28ArizonaMale25 and older724,089
...............
584West VirginiaFemale25 and older146,635
592WisconsinMale25 and older579,921
596WisconsinFemale25 and older678,458
604WyomingMale25 and older54,483
608WyomingFemale25 and older59,074
\n",
"

102 rows × 4 columns

\n",
"
"
],
"text/plain": [
" State Sex Age Group Total\n",
"4 Alabama Male 25 and older 405,618\n",
"8 Alabama Female 25 and older 479,739\n",
"16 Alaska Male 25 and older 65,820\n",
"20 Alaska Female 25 and older 80,337\n",
"28 Arizona Male 25 and older 724,089\n",
".. ... ... ... ...\n",
"584 West Virginia Female 25 and older 146,635\n",
"592 Wisconsin Male 25 and older 579,921\n",
"596 Wisconsin Female 25 and older 678,458\n",
"604 Wyoming Male 25 and older 54,483\n",
"608 Wyoming Female 25 and older 59,074\n",
"\n",
"[102 rows x 4 columns]"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_df = new_df[new_df.Sex != \"Total\"]\n",
"new_df = new_df[new_df[\"Age Group\"] != \"40 to 64\"]\n",
"new_df = new_df[new_df[\"Age Group\"] != \"25 to 39\"]\n",
"new_df = new_df[new_df[\"Age Group\"] != \"65 and older\"]\n",
"new_df"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
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SexFemaleMale
State
Alabama479,739405,618
Alaska80,33765,820
Arizona768,069724,089
Arkansas260,083215,284
California4,868,5204,559,964
Colorado866,670828,932
Connecticut527,498467,050
Delaware121,955106,244
District of Columbia157,755143,674
Florida2,485,8022,267,835
Georgia1,260,6951,040,873
Hawaii183,260151,949
Idaho171,981164,674
Illinois1,644,0071,464,965
Indiana642,726570,100
Iowa333,945