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General Assembly Project One - SAT and ACT Scores by State
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projectone.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jupyter Notebook can also be found at http://salcorpenterprise.com/dsi-project-one/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Load the data and perform basic operations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 1. Load the data in using pandas."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np \n",
"import seaborn as sns\n",
"from matplotlib import pyplot as plt\n",
"import scipy.stats as stats\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Loading each data with sat representing data from SAT scores nationally for 2017 and act doing\n",
"# the same for ACT scores nationally\n",
"\n",
"sat = pd.read_csv('../data/sat.csv')\n",
"act = pd.read_csv('../data/act.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 2. Print the first ten rows of each dataframe."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>State</th>\n",
" <th>Participation</th>\n",
" <th>Evidence-Based Reading and Writing</th>\n",
" <th>Math</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>Alabama</td>\n",
" <td>5%</td>\n",
" <td>593</td>\n",
" <td>572</td>\n",
" <td>1165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Alaska</td>\n",
" <td>38%</td>\n",
" <td>547</td>\n",
" <td>533</td>\n",
" <td>1080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>Arizona</td>\n",
" <td>30%</td>\n",
" <td>563</td>\n",
" <td>553</td>\n",
" <td>1116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>Arkansas</td>\n",
" <td>3%</td>\n",
" <td>614</td>\n",
" <td>594</td>\n",
" <td>1208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>California</td>\n",
" <td>53%</td>\n",
" <td>531</td>\n",
" <td>524</td>\n",
" <td>1055</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>Colorado</td>\n",
" <td>11%</td>\n",
" <td>606</td>\n",
" <td>595</td>\n",
" <td>1201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>Connecticut</td>\n",
" <td>100%</td>\n",
" <td>530</td>\n",
" <td>512</td>\n",
" <td>1041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>Delaware</td>\n",
" <td>100%</td>\n",
" <td>503</td>\n",
" <td>492</td>\n",
" <td>996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>District of Columbia</td>\n",
" <td>100%</td>\n",
" <td>482</td>\n",
" <td>468</td>\n",
" <td>950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>Florida</td>\n",
" <td>83%</td>\n",
" <td>520</td>\n",
" <td>497</td>\n",
" <td>1017</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 State Participation \\\n",
"0 0 Alabama 5% \n",
"1 1 Alaska 38% \n",
"2 2 Arizona 30% \n",
"3 3 Arkansas 3% \n",
"4 4 California 53% \n",
"5 5 Colorado 11% \n",
"6 6 Connecticut 100% \n",
"7 7 Delaware 100% \n",
"8 8 District of Columbia 100% \n",
"9 9 Florida 83% \n",
"\n",
" Evidence-Based Reading and Writing Math Total \n",
"0 593 572 1165 \n",
"1 547 533 1080 \n",
"2 563 553 1116 \n",
"3 614 594 1208 \n",
"4 531 524 1055 \n",
"5 606 595 1201 \n",
"6 530 512 1041 \n",
"7 503 492 996 \n",
"8 482 468 950 \n",
"9 520 497 1017 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The following code will show the first 10 rows of SAT then the next field does the same for ACT.\n",
"\n",
"sat.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>State</th>\n",
" <th>Participation</th>\n",
" <th>English</th>\n",
" <th>Math</th>\n",
" <th>Reading</th>\n",
" <th>Science</th>\n",
" <th>Composite</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>National</td>\n",
" <td>60%</td>\n",
" <td>20.3</td>\n",
" <td>20.7</td>\n",
" <td>21.4</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Alabama</td>\n",
" <td>100%</td>\n",
" <td>18.9</td>\n",
" <td>18.4</td>\n",
" <td>19.7</td>\n",
" <td>19.4</td>\n",
" <td>19.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>Alaska</td>\n",
" <td>65%</td>\n",
" <td>18.7</td>\n",
" <td>19.8</td>\n",
" <td>20.4</td>\n",
" <td>19.9</td>\n",
" <td>19.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>Arizona</td>\n",
" <td>62%</td>\n",
" <td>18.6</td>\n",
" <td>19.8</td>\n",
" <td>20.1</td>\n",
" <td>19.8</td>\n",
" <td>19.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>Arkansas</td>\n",
" <td>100%</td>\n",
" <td>18.9</td>\n",
" <td>19.0</td>\n",
" <td>19.7</td>\n",
" <td>19.5</td>\n",
" <td>19.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>California</td>\n",
" <td>31%</td>\n",
" <td>22.5</td>\n",
" <td>22.7</td>\n",
" <td>23.1</td>\n",
" <td>22.2</td>\n",
" <td>22.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>Colorado</td>\n",
" <td>100%</td>\n",
" <td>20.1</td>\n",
" <td>20.3</td>\n",
" <td>21.2</td>\n",
" <td>20.9</td>\n",
" <td>20.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>Connecticut</td>\n",
" <td>31%</td>\n",
" <td>25.5</td>\n",
" <td>24.6</td>\n",
" <td>25.6</td>\n",
" <td>24.6</td>\n",
" <td>25.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>Delaware</td>\n",
" <td>18%</td>\n",
" <td>24.1</td>\n",
" <td>23.4</td>\n",
" <td>24.8</td>\n",
" <td>23.6</td>\n",
" <td>24.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>District of Columbia</td>\n",
" <td>32%</td>\n",
" <td>24.4</td>\n",
" <td>23.5</td>\n",
" <td>24.9</td>\n",
" <td>23.5</td>\n",
" <td>24.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 State Participation English Math Reading \\\n",
"0 0 National 60% 20.3 20.7 21.4 \n",
"1 1 Alabama 100% 18.9 18.4 19.7 \n",
"2 2 Alaska 65% 18.7 19.8 20.4 \n",
"3 3 Arizona 62% 18.6 19.8 20.1 \n",
"4 4 Arkansas 100% 18.9 19.0 19.7 \n",
"5 5 California 31% 22.5 22.7 23.1 \n",
"6 6 Colorado 100% 20.1 20.3 21.2 \n",
"7 7 Connecticut 31% 25.5 24.6 25.6 \n",
"8 8 Delaware 18% 24.1 23.4 24.8 \n",
"9 9 District of Columbia 32% 24.4 23.5 24.9 \n",
"\n",
" Science Composite \n",
"0 21.0 21.0 \n",
"1 19.4 19.2 \n",
"2 19.9 19.8 \n",
"3 19.8 19.7 \n",
"4 19.5 19.4 \n",
"5 22.2 22.8 \n",
"6 20.9 20.8 \n",
"7 24.6 25.2 \n",
"8 23.6 24.1 \n",
"9 23.5 24.2 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"act.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 3. Describe in words what each variable (column) is."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# For the SAT table, columns are as follows:\n",
" # State is individual states around the U.S.\n",
" # Participation is Participation rate for the SAT\n",
" # Evidence Based Reading and Writing is score for Reading and Writing\n",
" # Math is the score for Math\n",
" # Total is the R&W score plus the Math Score"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# For the ACT table, columns are as follows:\n",
" # State is individual states around the U.S.\n",
" # Participation is Participation rate for the ACT\n",
" # English is the score for English\n",
" # Math is the score for Math\n",
" # Reading is the score for Reading\n",
" # Composite is the average of Math, Reading, and Science"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 4. Does the data look complete? Are there any obvious issues with the observations?"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 51 entries, 0 to 50\n",
"Data columns (total 6 columns):\n",
"Unnamed: 0 51 non-null int64\n",
"State 51 non-null object\n",
"Participation 51 non-null object\n",
"Evidence-Based Reading and Writing 51 non-null int64\n",
"Math 51 non-null int64\n",
"Total 51 non-null int64\n",
"dtypes: int64(4), object(2)\n",
"memory usage: 2.5+ KB\n"
]
}
],
"source": [
"# Data does look complete with one glaring missing item. ACT table has a row with National Data, while SAT does not\n",
"sat.info()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 52 entries, 0 to 51\n",
"Data columns (total 8 columns):\n",
"Unnamed: 0 52 non-null int64\n",
"State 52 non-null object\n",
"Participation 52 non-null object\n",
"English 52 non-null float64\n",
"Math 52 non-null float64\n",
"Reading 52 non-null float64\n",
"Science 52 non-null float64\n",
"Composite 52 non-null float64\n",
"dtypes: float64(5), int64(1), object(2)\n",
"memory usage: 3.3+ KB\n"
]
}
],
"source": [
"act.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 5. Print the types of each column."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0 int64\n",
"State object\n",
"Participation object\n",
"Evidence-Based Reading and Writing int64\n",
"Math int64\n",
"Total int64\n",
"dtype: object"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# dtypes helps show what type of data is embedded in each column of the data frame: integer, float, object, etc.\n",
"\n",
"sat.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0 int64\n",
"State object\n",
"Participation object\n",
"English float64\n",
"Math float64\n",
"Reading float64\n",
"Science float64\n",
"Composite float64\n",
"dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"act.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 6. Do any types need to be reassigned? If so, go ahead and do it."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>State</th>\n",
" <th>Participation</th>\n",
" <th>Evidence-Based Reading and Writing</th>\n",
" <th>Math</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>Alabama</td>\n",
" <td>0.05</td>\n",
" <td>593</td>\n",
" <td>572</td>\n",
" <td>1165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Alaska</td>\n",
" <td>0.38</td>\n",
" <td>547</td>\n",
" <td>533</td>\n",
" <td>1080</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 State Participation Evidence-Based Reading and Writing \\\n",
"0 0 Alabama 0.05 593 \n",
"1 1 Alaska 0.38 547 \n",
"\n",
" Math Total \n",
"0 572 1165 \n",
"1 533 1080 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Participation shows as an object and needs to be reassigned to a float as seen below.\n",
"\n",
"sat['Participation'] = sat['Participation'].str.rstrip('%').astype('float') / 100.0 #.rstrip takes the percentage symbol out\n",
"act['Participation'] = act['Participation'].str.rstrip('%').astype('float') / 100.0\n",
"\n",
"# Checking to make sure above worked.\n",
"\n",
"sat.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 7. Create a dictionary for each column mapping the State to its respective value for that column. (For example, you should have three SAT dictionaries.)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#SAT Dictionaries using the zip command and list comprehension\n",
"\n",
"sat_readandwrite = {k:v for (k,v) in zip(sat['State'],sat['Evidence-Based Reading and Writing'])}\n",
"sat_math = {k:v for (k,v) in zip(sat['State'], sat['Math'])}\n",
"sat_total = {k:v for (k,v) in zip(sat['State'], sat['Total'])} "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#ACT Dictionaries\n",
"\n",
"act_english = {k:v for (k,v) in zip(act['State'], act['English'])}\n",
"act_math = {k:v for (k,v) in zip(act['State'], act['Math'])}\n",
"act_reading = {k:v for (k,v) in zip(act['State'], act['Reading'])}\n",
"act_science = {k:v for (k,v) in zip(act['State'], act['Science'])}\n",
"act_composite = {k:v for (k,v) in zip(act['State'], act['Composite'])}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 8. Create one dictionary where each key is the column name, and each value is an iterable (a list or a Pandas Series) of all the values in that column."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Used to_dict to put each DataFrame into a Dictionary as described above.\n",
"\n",
"sat_dict = sat.to_dict(orient='list')\n",
"act_dict = act.to_dict(orient='list')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 9. Merge the dataframes on the state column."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0_x int64\n",
"State object\n",
"Participation_x float64\n",
"Evidence-Based Reading and Writing int64\n",
"Math_x int64\n",
"Total int64\n",
"Unnamed: 0_y int64\n",
"Participation_y float64\n",
"English float64\n",
"Math_y float64\n",
"Reading float64\n",
"Science float64\n",
"Composite float64\n",
"dtype: object"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# For this we use pd.merge to merge both the SAT and ACT DataFrames using an inner join on the State column.\n",
"\n",
"merge_data = pd.merge(sat, act, how='inner', on='State')\n",
"merge_data.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 10. Change the names of the columns so you can distinguish between the SAT columns and the ACT columns."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>SAT Participation</th>\n",
" <th>SAT Read and Write</th>\n",
" <th>SAT Math</th>\n",
" <th>Total</th>\n",
" <th>ACT Participation</th>\n",
" <th>ACT English</th>\n",
" <th>ACT Math</th>\n",
" <th>ACT Reading</th>\n",
" <th>ACT Science</th>\n",
" <th>ACT Composite</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alabama</td>\n",
" <td>0.05</td>\n",
" <td>593</td>\n",
" <td>572</td>\n",
" <td>1165</td>\n",
" <td>1.00</td>\n",
" <td>18.9</td>\n",
" <td>18.4</td>\n",
" <td>19.7</td>\n",
" <td>19.4</td>\n",
" <td>19.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alaska</td>\n",
" <td>0.38</td>\n",
" <td>547</td>\n",
" <td>533</td>\n",
" <td>1080</td>\n",
" <td>0.65</td>\n",
" <td>18.7</td>\n",
" <td>19.8</td>\n",
" <td>20.4</td>\n",
" <td>19.9</td>\n",
" <td>19.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State SAT Participation SAT Read and Write SAT Math Total \\\n",
"0 Alabama 0.05 593 572 1165 \n",
"1 Alaska 0.38 547 533 1080 \n",
"\n",
" ACT Participation ACT English ACT Math ACT Reading ACT Science \\\n",
"0 1.00 18.9 18.4 19.7 19.4 \n",
"1 0.65 18.7 19.8 20.4 19.9 \n",
"\n",
" ACT Composite \n",
"0 19.2 \n",
"1 19.8 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# For this we use .rename to change like columns to distinguish between SAT and ACT scores as well as make the columns\n",
"# more readable\n",
"\n",
"new_columns = merge_data.rename(columns={'Participation_x':'SAT Participation', 'Math_x':'SAT Math', \n",
" 'Evidence-Based Reading and Writing':'SAT Read and Write',\n",
" 'Participation_y':'ACT Participation','English':'ACT English',\n",
" 'Math_y':'ACT Math', 'Reading':'ACT Reading', 'Science':'ACT Science',\n",
" 'Composite':'ACT Composite'\n",
" })\n",
"\n",
"# Merging also created the two \"Unnamed\" columns which we remove since they are unnecessary at this point.\n",
"\n",
"clean_table = new_columns.drop(['Unnamed: 0_x', 'Unnamed: 0_y'], axis=1)\n",
"clean_table.head(2) # to verify and view the new DataFrame, just the first two rows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 11. Print the minimum and maximum of each numeric column in the data frame."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum Values are \n",
" SAT Participation 0.02\n",
"SAT Read and Write 482.00\n",
"SAT Math 52.00\n",
"Total 950.00\n",
"ACT Participation 0.08\n",
"ACT English 16.30\n",
"ACT Math 18.00\n",
"ACT Reading 18.10\n",
"ACT Science 2.30\n",
"ACT Composite 17.80\n",
"dtype: float64\n",
"Maximum Values are \n",
" SAT Participation 1.0\n",
"SAT Read and Write 644.0\n",
"SAT Math 651.0\n",
"Total 1295.0\n",
"ACT Participation 1.0\n",
"ACT English 25.5\n",
"ACT Math 25.3\n",
"ACT Reading 26.0\n",
"ACT Science 24.9\n",
"ACT Composite 25.5\n",
"dtype: float64\n"
]
}
],
"source": [
"# here we use .min and .max to get the minimum and maximum values for all of the columns in the new cleaned up\n",
"# and merged DataFrame.\n",
"\n",
"print ('Minimum Values are \\n', clean_table.min(axis=0, numeric_only=True))\n",
"print ('Maximum Values are \\n', clean_table.max(axis=0, numeric_only=True))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 12. Write a function using only list comprehensions, no loops, to compute standard deviation. Using this function, calculate the standard deviation of each numeric column in both data sets. Add these to a list called `sd`.\n",
"\n",
"$$\\sigma = \\sqrt{\\frac{1}{n}\\sum_{i=1}^n(x_i - \\mu)^2}$$"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def stan_dev(df):\n",
" return [ df[col].std() for col in \n",
" df.select_dtypes(include = ['float', 'int64']).columns.tolist()]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.35276632270013036,\n",
" 45.66690138768932,\n",
" 84.90911865855486,\n",
" 92.49481172519046,\n",
" 0.32140842015886834,\n",
" 2.35367713980303,\n",
" 1.9819894936505533,\n",
" 2.0672706264873146,\n",
" 3.182462975155452,\n",
" 2.020694891154341]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sd = stan_dev(clean_table)\n",
"sd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Manipulate the dataframe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 13. Turn the list `sd` into a new observation in your dataset."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'State': 52, 'SAT Participation': 'TotalSTDs', 'SAT Read and Write': 0.35276632270013036, 'SAT Math': 45.66690138768932, 'Total': 84.90911865855486, 'ACT Participation': 92.49481172519046, 'ACT English': 0.32140842015886834, 'ACT Math': 2.35367713980303, 'ACT Reading': 1.9819894936505533, 'ACT Science': 2.0672706264873146, 'ACT Composite': 3.182462975155452}\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>SAT Participation</th>\n",
" <th>SAT Read and Write</th>\n",
" <th>SAT Math</th>\n",
" <th>Total</th>\n",
" <th>ACT Participation</th>\n",
" <th>ACT English</th>\n",
" <th>ACT Math</th>\n",
" <th>ACT Reading</th>\n",
" <th>ACT Science</th>\n",
" <th>ACT Composite</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>Wisconsin</td>\n",
" <td>0.03</td>\n",
" <td>642.000000</td>\n",
" <td>649.000000</td>\n",
" <td>1291.000000</td>\n",
" <td>1.000000</td>\n",
" <td>19.700000</td>\n",
" <td>20.400000</td>\n",
" <td>20.600000</td>\n",
" <td>20.900000</td>\n",
" <td>20.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Wyoming</td>\n",
" <td>0.03</td>\n",
" <td>626.000000</td>\n",
" <td>604.000000</td>\n",
" <td>1230.000000</td>\n",
" <td>1.000000</td>\n",
" <td>19.400000</td>\n",
" <td>19.800000</td>\n",
" <td>20.800000</td>\n",
" <td>20.600000</td>\n",
" <td>20.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>52</td>\n",
" <td>TotalSTDs</td>\n",
" <td>0.352766</td>\n",
" <td>45.666901</td>\n",
" <td>84.909119</td>\n",
" <td>92.494812</td>\n",
" <td>0.321408</td>\n",
" <td>2.353677</td>\n",
" <td>1.981989</td>\n",
" <td>2.067271</td>\n",
" <td>3.182463</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State SAT Participation SAT Read and Write SAT Math Total \\\n",
"49 Wisconsin 0.03 642.000000 649.000000 1291.000000 \n",
"50 Wyoming 0.03 626.000000 604.000000 1230.000000 \n",
"51 52 TotalSTDs 0.352766 45.666901 84.909119 \n",
"\n",
" ACT Participation ACT English ACT Math ACT Reading ACT Science \\\n",
"49 1.000000 19.700000 20.400000 20.600000 20.900000 \n",
"50 1.000000 19.400000 19.800000 20.800000 20.600000 \n",
"51 92.494812 0.321408 2.353677 1.981989 2.067271 \n",
"\n",
" ACT Composite \n",
"49 20.500000 \n",
"50 20.200000 \n",
"51 3.182463 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### Make a dictionary that ‘lines up’ with the original df columns and has the STD values\n",
"### Pad the first two rows (index and state) with dummy values\n",
"# 52 is the column index (not the df index) for the inserted row\n",
"# totalSTDs is the newly inserted row ‘state’ label\n",
"df_data = {}\n",
"for i, j in zip(clean_table.columns, [52, 'TotalSTDs']+stan_dev(clean_table)):\n",
" df_data[i] = j\n",
"print(df_data)\n",
"\n",
"# show the to-be-appended totals row we’re gonna stick on the bottom of our original df\n",
"pd.DataFrame(data=df_data, index=[clean_table.index[-1] + 1])\n",
"\n",
"# append the STD total row to the original df and assign to new df, ‘df_std’\n",
"df_std = clean_table.append(pd.DataFrame(data=df_data, index=[clean_table.index[-1] + 1]))\n",
"\n",
"# get the columns of the std’d frame in the correct (orginal) order again\n",
"df_std = df_std[clean_table.columns.tolist()]\n",
"\n",
"# finished product snippet\n",
"df_std.tail(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 14. Sort the dataframe by the values in a numeric column (e.g. observations descending by SAT participation rate)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>SAT Participation</th>\n",
" <th>SAT Read and Write</th>\n",
" <th>SAT Math</th>\n",
" <th>Total</th>\n",
" <th>ACT Participation</th>\n",
" <th>ACT English</th>\n",
" <th>ACT Math</th>\n",
" <th>ACT Reading</th>\n",
" <th>ACT Science</th>\n",
" <th>ACT Composite</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>District of Columbia</td>\n",
" <td>1.00</td>\n",
" <td>482</td>\n",
" <td>468</td>\n",
" <td>950</td>\n",
" <td>0.32</td>\n",
" <td>24.4</td>\n",
" <td>23.5</td>\n",
" <td>24.9</td>\n",
" <td>23.5</td>\n",
" <td>24.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Michigan</td>\n",
" <td>1.00</td>\n",
" <td>509</td>\n",
" <td>495</td>\n",
" <td>1005</td>\n",
" <td>0.29</td>\n",
" <td>24.1</td>\n",
" <td>23.7</td>\n",
" <td>24.5</td>\n",
" <td>23.8</td>\n",
" <td>24.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Connecticut</td>\n",
" <td>1.00</td>\n",
" <td>530</td>\n",
" <td>512</td>\n",
" <td>1041</td>\n",
" <td>0.31</td>\n",
" <td>25.5</td>\n",
" <td>24.6</td>\n",
" <td>25.6</td>\n",
" <td>24.6</td>\n",
" <td>25.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Delaware</td>\n",
" <td>1.00</td>\n",
" <td>503</td>\n",
" <td>492</td>\n",
" <td>996</td>\n",
" <td>0.18</td>\n",
" <td>24.1</td>\n",
" <td>23.4</td>\n",
" <td>24.8</td>\n",
" <td>23.6</td>\n",
" <td>24.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>New Hampshire</td>\n",
" <td>0.96</td>\n",
" <td>532</td>\n",
" <td>520</td>\n",
" <td>1052</td>\n",
" <td>0.18</td>\n",
" <td>25.4</td>\n",
" <td>25.1</td>\n",
" <td>26.0</td>\n",
" <td>24.9</td>\n",
" <td>25.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Maine</td>\n",
" <td>0.95</td>\n",
" <td>513</td>\n",
" <td>499</td>\n",
" <td>1012</td>\n",
" <td>0.08</td>\n",
" <td>24.2</td>\n",
" <td>24.0</td>\n",
" <td>24.8</td>\n",
" <td>23.7</td>\n",
" <td>24.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Idaho</td>\n",
" <td>0.93</td>\n",
" <td>513</td>\n",
" <td>493</td>\n",
" <td>1005</td>\n",
" <td>0.38</td>\n",
" <td>21.9</td>\n",
" <td>21.8</td>\n",
" <td>23.0</td>\n",
" <td>22.1</td>\n",
" <td>22.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Florida</td>\n",
" <td>0.83</td>\n",
" <td>520</td>\n",
" <td>497</td>\n",
" <td>1017</td>\n",
" <td>0.73</td>\n",
" <td>19.0</td>\n",
" <td>19.4</td>\n",
" <td>21.0</td>\n",
" <td>19.4</td>\n",
" <td>19.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Massachusetts</td>\n",
" <td>0.76</td>\n",
" <td>555</td>\n",
" <td>551</td>\n",
" <td>1107</td>\n",
" <td>0.29</td>\n",
" <td>25.4</td>\n",
" <td>25.3</td>\n",
" <td>25.9</td>\n",
" <td>24.7</td>\n",
" <td>25.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Rhode Island</td>\n",
" <td>0.71</td>\n",
" <td>539</td>\n",
" <td>524</td>\n",
" <td>1062</td>\n",
" <td>0.21</td>\n",
" <td>24.0</td>\n",
" <td>23.3</td>\n",
" <td>24.7</td>\n",
" <td>23.4</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>New Jersey</td>\n",
" <td>0.70</td>\n",
" <td>530</td>\n",
" <td>526</td>\n",
" <td>1056</td>\n",
" <td>0.34</td>\n",
" <td>23.8</td>\n",
" <td>23.8</td>\n",
" <td>24.1</td>\n",
" <td>23.2</td>\n",
" <td>23.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Maryland</td>\n",
" <td>0.69</td>\n",
" <td>536</td>\n",
" <td>52</td>\n",
" <td>1060</td>\n",
" <td>0.28</td>\n",
" <td>23.3</td>\n",
" <td>23.1</td>\n",
" <td>24.2</td>\n",
" <td>2.3</td>\n",
" <td>23.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>New York</td>\n",
" <td>0.67</td>\n",
" <td>528</td>\n",
" <td>523</td>\n",
" <td>1052</td>\n",
" <td>0.31</td>\n",
" <td>23.8</td>\n",
" <td>24.0</td>\n",
" <td>24.6</td>\n",
" <td>23.9</td>\n",
" <td>24.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Virginia</td>\n",
" <td>0.65</td>\n",
" <td>561</td>\n",
" <td>541</td>\n",
" <td>1102</td>\n",
" <td>0.29</td>\n",
" <td>23.5</td>\n",
" <td>23.3</td>\n",
" <td>24.6</td>\n",
" <td>23.5</td>\n",
" <td>23.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Pennsylvania</td>\n",
" <td>0.65</td>\n",
" <td>540</td>\n",
" <td>531</td>\n",
" <td>1071</td>\n",
" <td>0.23</td>\n",
" <td>23.4</td>\n",
" <td>23.4</td>\n",
" <td>24.2</td>\n",
" <td>23.3</td>\n",
" <td>23.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Washington</td>\n",
" <td>0.64</td>\n",
" <td>541</td>\n",
" <td>534</td>\n",
" <td>1075</td>\n",
" <td>0.29</td>\n",
" <td>20.9</td>\n",
" <td>21.9</td>\n",
" <td>22.1</td>\n",
" <td>22.0</td>\n",
" <td>21.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Indiana</td>\n",
" <td>0.63</td>\n",
" <td>542</td>\n",
" <td>532</td>\n",
" <td>1074</td>\n",
" <td>0.35</td>\n",
" <td>22.0</td>\n",
" <td>22.4</td>\n",
" <td>23.2</td>\n",
" <td>22.3</td>\n",
" <td>22.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Texas</td>\n",
" <td>0.62</td>\n",
" <td>513</td>\n",
" <td>507</td>\n",
" <td>1020</td>\n",
" <td>0.45</td>\n",
" <td>19.5</td>\n",
" <td>20.7</td>\n",
" <td>21.1</td>\n",
" <td>20.9</td>\n",
" <td>20.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Georgia</td>\n",
" <td>0.61</td>\n",
" <td>535</td>\n",
" <td>515</td>\n",
" <td>1050</td>\n",
" <td>0.55</td>\n",
" <td>21.0</td>\n",
" <td>20.9</td>\n",
" <td>22.0</td>\n",
" <td>21.3</td>\n",
" <td>21.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Vermont</td>\n",
" <td>0.60</td>\n",
" <td>562</td>\n",
" <td>551</td>\n",
" <td>1114</td>\n",
" <td>0.29</td>\n",
" <td>23.3</td>\n",
" <td>23.1</td>\n",
" <td>24.4</td>\n",
" <td>23.2</td>\n",
" <td>23.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Hawaii</td>\n",
" <td>0.55</td>\n",
" <td>544</td>\n",
" <td>541</td>\n",
" <td>1085</td>\n",
" <td>0.90</td>\n",
" <td>17.8</td>\n",
" <td>19.2</td>\n",
" <td>19.2</td>\n",
" <td>19.3</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>California</td>\n",
" <td>0.53</td>\n",
" <td>531</td>\n",
" <td>524</td>\n",
" <td>1055</td>\n",
" <td>0.31</td>\n",
" <td>22.5</td>\n",
" <td>22.7</td>\n",
" <td>23.1</td>\n",
" <td>22.2</td>\n",
" <td>22.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>South Carolina</td>\n",
" <td>0.50</td>\n",
" <td>543</td>\n",
" <td>521</td>\n",
" <td>1064</td>\n",
" <td>1.00</td>\n",
" <td>17.5</td>\n",
" <td>18.6</td>\n",
" <td>19.1</td>\n",
" <td>18.9</td>\n",
" <td>18.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>North Carolina</td>\n",
" <td>0.49</td>\n",
" <td>546</td>\n",
" <td>535</td>\n",
" <td>1081</td>\n",
" <td>1.00</td>\n",
" <td>17.8</td>\n",
" <td>19.3</td>\n",
" <td>19.6</td>\n",
" <td>19.3</td>\n",
" <td>19.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Oregon</td>\n",
" <td>0.43</td>\n",
" <td>560</td>\n",
" <td>548</td>\n",
" <td>1108</td>\n",
" <td>0.40</td>\n",
" <td>21.2</td>\n",
" <td>21.5</td>\n",
" <td>22.4</td>\n",
" <td>21.7</td>\n",
" <td>21.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alaska</td>\n",
" <td>0.38</td>\n",
" <td>547</td>\n",
" <td>533</td>\n",
" <td>1080</td>\n",
" <td>0.65</td>\n",
" <td>18.7</td>\n",
" <td>19.8</td>\n",
" <td>20.4</td>\n",
" <td>19.9</td>\n",
" <td>19.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Arizona</td>\n",
" <td>0.30</td>\n",
" <td>563</td>\n",
" <td>553</td>\n",
" <td>1116</td>\n",
" <td>0.62</td>\n",
" <td>18.6</td>\n",
" <td>19.8</td>\n",
" <td>20.1</td>\n",
" <td>19.8</td>\n",
" <td>19.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Nevada</td>\n",
" <td>0.26</td>\n",
" <td>563</td>\n",
" <td>553</td>\n",
" <td>1116</td>\n",
" <td>1.00</td>\n",
" <td>16.3</td>\n",
" <td>18.0</td>\n",
" <td>18.1</td>\n",
" <td>18.2</td>\n",
" <td>17.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>West Virginia</td>\n",
" <td>0.14</td>\n",
" <td>558</td>\n",
" <td>528</td>\n",
" <td>1086</td>\n",
" <td>0.69</td>\n",
" <td>20.0</td>\n",
" <td>19.4</td>\n",
" <td>21.2</td>\n",
" <td>20.5</td>\n",
" <td>20.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>Ohio</td>\n",
" <td>0.12</td>\n",
" <td>578</td>\n",
" <td>570</td>\n",
" <td>1149</td>\n",
" <td>0.75</td>\n",
" <td>21.2</td>\n",
" <td>21.6</td>\n",
" <td>22.5</td>\n",
" <td>22.0</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Colorado</td>\n",
" <td>0.11</td>\n",
" <td>606</td>\n",
" <td>595</td>\n",
" <td>1201</td>\n",
" <td>1.00</td>\n",
" <td>20.1</td>\n",
" <td>20.3</td>\n",
" <td>21.2</td>\n",
" <td>20.9</td>\n",
" <td>20.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>New Mexico</td>\n",
" <td>0.11</td>\n",
" <td>577</td>\n",
" <td>561</td>\n",
" <td>1138</td>\n",
" <td>0.66</td>\n",
" <td>18.6</td>\n",
" <td>19.4</td>\n",
" <td>20.4</td>\n",
" <td>20.0</td>\n",
" <td>19.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Montana</td>\n",
" <td>0.10</td>\n",
" <td>605</td>\n",
" <td>591</td>\n",
" <td>1196</td>\n",
" <td>1.00</td>\n",
" <td>19.0</td>\n",
" <td>20.2</td>\n",
" <td>21.0</td>\n",
" <td>20.5</td>\n",
" <td>20.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Illinois</td>\n",
" <td>0.09</td>\n",
" <td>559</td>\n",
" <td>556</td>\n",
" <td>1115</td>\n",
" <td>0.93</td>\n",
" <td>21.0</td>\n",
" <td>21.2</td>\n",
" <td>21.6</td>\n",
" <td>21.3</td>\n",
" <td>21.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Oklahoma</td>\n",
" <td>0.07</td>\n",
" <td>530</td>\n",
" <td>517</td>\n",
" <td>1047</td>\n",
" <td>1.00</td>\n",
" <td>18.5</td>\n",
" <td>18.8</td>\n",
" <td>20.1</td>\n",
" <td>19.6</td>\n",
" <td>19.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alabama</td>\n",
" <td>0.05</td>\n",
" <td>593</td>\n",
" <td>572</td>\n",
" <td>1165</td>\n",
" <td>1.00</td>\n",
" <td>18.9</td>\n",
" <td>18.4</td>\n",
" <td>19.7</td>\n",
" <td>19.4</td>\n",
" <td>19.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Tennessee</td>\n",
" <td>0.05</td>\n",
" <td>623</td>\n",
" <td>604</td>\n",
" <td>1228</td>\n",
" <td>1.00</td>\n",
" <td>19.5</td>\n",
" <td>19.2</td>\n",
" <td>20.1</td>\n",
" <td>19.9</td>\n",
" <td>19.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Kentucky</td>\n",
" <td>0.04</td>\n",
" <td>631</td>\n",
" <td>616</td>\n",
" <td>1247</td>\n",
" <td>1.00</td>\n",
" <td>19.6</td>\n",
" <td>19.4</td>\n",
" <td>20.5</td>\n",
" <td>20.1</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Kansas</td>\n",
" <td>0.04</td>\n",
" <td>632</td>\n",
" <td>628</td>\n",
" <td>1260</td>\n",
" <td>0.73</td>\n",
" <td>21.1</td>\n",
" <td>21.3</td>\n",
" <td>22.3</td>\n",
" <td>21.7</td>\n",
" <td>21.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Louisiana</td>\n",
" <td>0.04</td>\n",
" <td>611</td>\n",
" <td>586</td>\n",
" <td>1198</td>\n",
" <td>1.00</td>\n",
" <td>19.4</td>\n",
" <td>18.8</td>\n",
" <td>19.8</td>\n",
" <td>19.6</td>\n",
" <td>19.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Utah</td>\n",
" <td>0.03</td>\n",
" <td>624</td>\n",
" <td>614</td>\n",
" <td>1238</td>\n",
" <td>1.00</td>\n",
" <td>19.5</td>\n",
" <td>19.9</td>\n",
" <td>20.8</td>\n",
" <td>20.6</td>\n",
" <td>20.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>Wisconsin</td>\n",
" <td>0.03</td>\n",
" <td>642</td>\n",
" <td>649</td>\n",
" <td>1291</td>\n",
" <td>1.00</td>\n",
" <td>19.7</td>\n",
" <td>20.4</td>\n",
" <td>20.6</td>\n",
" <td>20.9</td>\n",
" <td>20.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Missouri</td>\n",
" <td>0.03</td>\n",
" <td>640</td>\n",
" <td>631</td>\n",
" <td>1271</td>\n",
" <td>1.00</td>\n",
" <td>19.8</td>\n",
" <td>19.9</td>\n",
" <td>20.8</td>\n",
" <td>20.5</td>\n",
" <td>20.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>South Dakota</td>\n",
" <td>0.03</td>\n",
" <td>612</td>\n",
" <td>603</td>\n",
" <td>1216</td>\n",
" <td>0.80</td>\n",
" <td>20.7</td>\n",
" <td>21.5</td>\n",
" <td>22.3</td>\n",
" <td>22.0</td>\n",
" <td>21.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Nebraska</td>\n",
" <td>0.03</td>\n",
" <td>629</td>\n",
" <td>625</td>\n",
" <td>1253</td>\n",
" <td>0.84</td>\n",
" <td>20.9</td>\n",
" <td>20.9</td>\n",
" <td>21.9</td>\n",
" <td>21.5</td>\n",
" <td>21.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Minnesota</td>\n",
" <td>0.03</td>\n",
" <td>644</td>\n",
" <td>651</td>\n",
" <td>1295</td>\n",
" <td>1.00</td>\n",
" <td>20.4</td>\n",
" <td>21.5</td>\n",
" <td>21.8</td>\n",
" <td>21.6</td>\n",
" <td>21.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Arkansas</td>\n",
" <td>0.03</td>\n",
" <td>614</td>\n",
" <td>594</td>\n",
" <td>1208</td>\n",
" <td>1.00</td>\n",
" <td>18.9</td>\n",
" <td>19.0</td>\n",
" <td>19.7</td>\n",
" <td>19.5</td>\n",
" <td>19.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Wyoming</td>\n",
" <td>0.03</td>\n",
" <td>626</td>\n",
" <td>604</td>\n",
" <td>1230</td>\n",
" <td>1.00</td>\n",
" <td>19.4</td>\n",
" <td>19.8</td>\n",
" <td>20.8</td>\n",
" <td>20.6</td>\n",
" <td>20.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>North Dakota</td>\n",
" <td>0.02</td>\n",
" <td>635</td>\n",
" <td>621</td>\n",
" <td>1256</td>\n",
" <td>0.98</td>\n",
" <td>19.0</td>\n",
" <td>20.4</td>\n",
" <td>20.5</td>\n",
" <td>20.6</td>\n",
" <td>20.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Mississippi</td>\n",
" <td>0.02</td>\n",
" <td>634</td>\n",
" <td>607</td>\n",
" <td>1242</td>\n",
" <td>1.00</td>\n",
" <td>18.2</td>\n",
" <td>18.1</td>\n",
" <td>18.8</td>\n",
" <td>18.8</td>\n",
" <td>18.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Iowa</td>\n",
" <td>0.02</td>\n",
" <td>641</td>\n",
" <td>635</td>\n",
" <td>1275</td>\n",
" <td>0.67</td>\n",
" <td>21.2</td>\n",
" <td>21.3</td>\n",
" <td>22.6</td>\n",
" <td>22.1</td>\n",
" <td>21.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State SAT Participation SAT Read and Write SAT Math \\\n",
"8 District of Columbia 1.00 482 468 \n",
"22 Michigan 1.00 509 495 \n",
"6 Connecticut 1.00 530 512 \n",
"7 Delaware 1.00 503 492 \n",
"29 New Hampshire 0.96 532 520 \n",
"19 Maine 0.95 513 499 \n",
"12 Idaho 0.93 513 493 \n",
"9 Florida 0.83 520 497 \n",
"21 Massachusetts 0.76 555 551 \n",
"39 Rhode Island 0.71 539 524 \n",
"30 New Jersey 0.70 530 526 \n",
"20 Maryland 0.69 536 52 \n",
"32 New York 0.67 528 523 \n",
"46 Virginia 0.65 561 541 \n",
"38 Pennsylvania 0.65 540 531 \n",
"47 Washington 0.64 541 534 \n",
"14 Indiana 0.63 542 532 \n",
"43 Texas 0.62 513 507 \n",
"10 Georgia 0.61 535 515 \n",
"45 Vermont 0.60 562 551 \n",
"11 Hawaii 0.55 544 541 \n",
"4 California 0.53 531 524 \n",
"40 South Carolina 0.50 543 521 \n",
"33 North Carolina 0.49 546 535 \n",
"37 Oregon 0.43 560 548 \n",
"1 Alaska 0.38 547 533 \n",
"2 Arizona 0.30 563 553 \n",
"28 Nevada 0.26 563 553 \n",
"48 West Virginia 0.14 558 528 \n",
"35 Ohio 0.12 578 570 \n",
"5 Colorado 0.11 606 595 \n",
"31 New Mexico 0.11 577 561 \n",
"26 Montana 0.10 605 591 \n",
"13 Illinois 0.09 559 556 \n",
"36 Oklahoma 0.07 530 517 \n",
"0 Alabama 0.05 593 572 \n",
"42 Tennessee 0.05 623 604 \n",
"17 Kentucky 0.04 631 616 \n",
"16 Kansas 0.04 632 628 \n",
"18 Louisiana 0.04 611 586 \n",
"44 Utah 0.03 624 614 \n",
"49 Wisconsin 0.03 642 649 \n",
"25 Missouri 0.03 640 631 \n",
"41 South Dakota 0.03 612 603 \n",
"27 Nebraska 0.03 629 625 \n",
"23 Minnesota 0.03 644 651 \n",
"3 Arkansas 0.03 614 594 \n",
"50 Wyoming 0.03 626 604 \n",
"34 North Dakota 0.02 635 621 \n",
"24 Mississippi 0.02 634 607 \n",
"15 Iowa 0.02 641 635 \n",
"\n",
" Total ACT Participation ACT English ACT Math ACT Reading ACT Science \\\n",
"8 950 0.32 24.4 23.5 24.9 23.5 \n",
"22 1005 0.29 24.1 23.7 24.5 23.8 \n",
"6 1041 0.31 25.5 24.6 25.6 24.6 \n",
"7 996 0.18 24.1 23.4 24.8 23.6 \n",
"29 1052 0.18 25.4 25.1 26.0 24.9 \n",
"19 1012 0.08 24.2 24.0 24.8 23.7 \n",
"12 1005 0.38 21.9 21.8 23.0 22.1 \n",
"9 1017 0.73 19.0 19.4 21.0 19.4 \n",
"21 1107 0.29 25.4 25.3 25.9 24.7 \n",
"39 1062 0.21 24.0 23.3 24.7 23.4 \n",
"30 1056 0.34 23.8 23.8 24.1 23.2 \n",
"20 1060 0.28 23.3 23.1 24.2 2.3 \n",
"32 1052 0.31 23.8 24.0 24.6 23.9 \n",
"46 1102 0.29 23.5 23.3 24.6 23.5 \n",
"38 1071 0.23 23.4 23.4 24.2 23.3 \n",
"47 1075 0.29 20.9 21.9 22.1 22.0 \n",
"14 1074 0.35 22.0 22.4 23.2 22.3 \n",
"43 1020 0.45 19.5 20.7 21.1 20.9 \n",
"10 1050 0.55 21.0 20.9 22.0 21.3 \n",
"45 1114 0.29 23.3 23.1 24.4 23.2 \n",
"11 1085 0.90 17.8 19.2 19.2 19.3 \n",
"4 1055 0.31 22.5 22.7 23.1 22.2 \n",
"40 1064 1.00 17.5 18.6 19.1 18.9 \n",
"33 1081 1.00 17.8 19.3 19.6 19.3 \n",
"37 1108 0.40 21.2 21.5 22.4 21.7 \n",
"1 1080 0.65 18.7 19.8 20.4 19.9 \n",
"2 1116 0.62 18.6 19.8 20.1 19.8 \n",
"28 1116 1.00 16.3 18.0 18.1 18.2 \n",
"48 1086 0.69 20.0 19.4 21.2 20.5 \n",
"35 1149 0.75 21.2 21.6 22.5 22.0 \n",
"5 1201 1.00 20.1 20.3 21.2 20.9 \n",
"31 1138 0.66 18.6 19.4 20.4 20.0 \n",
"26 1196 1.00 19.0 20.2 21.0 20.5 \n",
"13 1115 0.93 21.0 21.2 21.6 21.3 \n",
"36 1047 1.00 18.5 18.8 20.1 19.6 \n",
"0 1165 1.00 18.9 18.4 19.7 19.4 \n",
"42 1228 1.00 19.5 19.2 20.1 19.9 \n",
"17 1247 1.00 19.6 19.4 20.5 20.1 \n",
"16 1260 0.73 21.1 21.3 22.3 21.7 \n",
"18 1198 1.00 19.4 18.8 19.8 19.6 \n",
"44 1238 1.00 19.5 19.9 20.8 20.6 \n",
"49 1291 1.00 19.7 20.4 20.6 20.9 \n",
"25 1271 1.00 19.8 19.9 20.8 20.5 \n",
"41 1216 0.80 20.7 21.5 22.3 22.0 \n",
"27 1253 0.84 20.9 20.9 21.9 21.5 \n",
"23 1295 1.00 20.4 21.5 21.8 21.6 \n",
"3 1208 1.00 18.9 19.0 19.7 19.5 \n",
"50 1230 1.00 19.4 19.8 20.8 20.6 \n",
"34 1256 0.98 19.0 20.4 20.5 20.6 \n",
"24 1242 1.00 18.2 18.1 18.8 18.8 \n",
"15 1275 0.67 21.2 21.3 22.6 22.1 \n",
"\n",
" ACT Composite \n",
"8 24.2 \n",
"22 24.1 \n",
"6 25.2 \n",
"7 24.1 \n",
"29 25.5 \n",
"19 24.3 \n",
"12 22.3 \n",
"9 19.8 \n",
"21 25.4 \n",
"39 24.0 \n",
"30 23.9 \n",
"20 23.6 \n",
"32 24.2 \n",
"46 23.8 \n",
"38 23.7 \n",
"47 21.9 \n",
"14 22.6 \n",
"43 20.7 \n",
"10 21.4 \n",
"45 23.6 \n",
"11 19.0 \n",
"4 22.8 \n",
"40 18.7 \n",
"33 19.1 \n",
"37 21.8 \n",
"1 19.8 \n",
"2 19.7 \n",
"28 17.8 \n",
"48 20.4 \n",
"35 22.0 \n",
"5 20.8 \n",
"31 19.7 \n",
"26 20.3 \n",
"13 21.4 \n",
"36 19.4 \n",
"0 19.2 \n",
"42 19.8 \n",
"17 20.0 \n",
"16 21.7 \n",
"18 19.5 \n",
"44 20.3 \n",
"49 20.5 \n",
"25 20.4 \n",
"41 21.8 \n",
"27 21.4 \n",
"23 21.5 \n",
"3 19.4 \n",
"50 20.2 \n",
"34 20.3 \n",
"24 18.6 \n",
"15 21.9 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Here we use .sort_values to specifically sort based on Column input and whether we want it in ascending or descending order.\n",
"\n",
"clean_table.sort_values('SAT Participation', ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 15. Use a boolean filter to display only observations with a score above a certain threshold (e.g. only states with a participation rate above 50%)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>SAT Participation</th>\n",
" <th>SAT Read and Write</th>\n",
" <th>SAT Math</th>\n",
" <th>Total</th>\n",
" <th>ACT Participation</th>\n",
" <th>ACT English</th>\n",
" <th>ACT Math</th>\n",
" <th>ACT Reading</th>\n",
" <th>ACT Science</th>\n",
" <th>ACT Composite</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>California</td>\n",
" <td>0.53</td>\n",
" <td>531</td>\n",
" <td>524</td>\n",
" <td>1055</td>\n",
" <td>0.31</td>\n",
" <td>22.5</td>\n",
" <td>22.7</td>\n",
" <td>23.1</td>\n",
" <td>22.2</td>\n",
" <td>22.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Connecticut</td>\n",
" <td>1.00</td>\n",
" <td>530</td>\n",
" <td>512</td>\n",
" <td>1041</td>\n",
" <td>0.31</td>\n",
" <td>25.5</td>\n",
" <td>24.6</td>\n",
" <td>25.6</td>\n",
" <td>24.6</td>\n",
" <td>25.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Delaware</td>\n",
" <td>1.00</td>\n",
" <td>503</td>\n",
" <td>492</td>\n",
" <td>996</td>\n",
" <td>0.18</td>\n",
" <td>24.1</td>\n",
" <td>23.4</td>\n",
" <td>24.8</td>\n",
" <td>23.6</td>\n",
" <td>24.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>District of Columbia</td>\n",
" <td>1.00</td>\n",
" <td>482</td>\n",
" <td>468</td>\n",
" <td>950</td>\n",
" <td>0.32</td>\n",
" <td>24.4</td>\n",
" <td>23.5</td>\n",
" <td>24.9</td>\n",
" <td>23.5</td>\n",
" <td>24.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Florida</td>\n",
" <td>0.83</td>\n",
" <td>520</td>\n",
" <td>497</td>\n",
" <td>1017</td>\n",
" <td>0.73</td>\n",
" <td>19.0</td>\n",
" <td>19.4</td>\n",
" <td>21.0</td>\n",
" <td>19.4</td>\n",
" <td>19.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Georgia</td>\n",
" <td>0.61</td>\n",
" <td>535</td>\n",
" <td>515</td>\n",
" <td>1050</td>\n",
" <td>0.55</td>\n",
" <td>21.0</td>\n",
" <td>20.9</td>\n",
" <td>22.0</td>\n",
" <td>21.3</td>\n",
" <td>21.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Hawaii</td>\n",
" <td>0.55</td>\n",
" <td>544</td>\n",
" <td>541</td>\n",
" <td>1085</td>\n",
" <td>0.90</td>\n",
" <td>17.8</td>\n",
" <td>19.2</td>\n",
" <td>19.2</td>\n",
" <td>19.3</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Idaho</td>\n",
" <td>0.93</td>\n",
" <td>513</td>\n",
" <td>493</td>\n",
" <td>1005</td>\n",
" <td>0.38</td>\n",
" <td>21.9</td>\n",
" <td>21.8</td>\n",
" <td>23.0</td>\n",
" <td>22.1</td>\n",
" <td>22.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Indiana</td>\n",
" <td>0.63</td>\n",
" <td>542</td>\n",
" <td>532</td>\n",
" <td>1074</td>\n",
" <td>0.35</td>\n",
" <td>22.0</td>\n",
" <td>22.4</td>\n",
" <td>23.2</td>\n",
" <td>22.3</td>\n",
" <td>22.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Maine</td>\n",
" <td>0.95</td>\n",
" <td>513</td>\n",
" <td>499</td>\n",
" <td>1012</td>\n",
" <td>0.08</td>\n",
" <td>24.2</td>\n",
" <td>24.0</td>\n",
" <td>24.8</td>\n",
" <td>23.7</td>\n",
" <td>24.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Maryland</td>\n",
" <td>0.69</td>\n",
" <td>536</td>\n",
" <td>52</td>\n",
" <td>1060</td>\n",
" <td>0.28</td>\n",
" <td>23.3</td>\n",
" <td>23.1</td>\n",
" <td>24.2</td>\n",
" <td>2.3</td>\n",
" <td>23.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Massachusetts</td>\n",
" <td>0.76</td>\n",
" <td>555</td>\n",
" <td>551</td>\n",
" <td>1107</td>\n",
" <td>0.29</td>\n",
" <td>25.4</td>\n",
" <td>25.3</td>\n",
" <td>25.9</td>\n",
" <td>24.7</td>\n",
" <td>25.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Michigan</td>\n",
" <td>1.00</td>\n",
" <td>509</td>\n",
" <td>495</td>\n",
" <td>1005</td>\n",
" <td>0.29</td>\n",
" <td>24.1</td>\n",
" <td>23.7</td>\n",
" <td>24.5</td>\n",
" <td>23.8</td>\n",
" <td>24.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>New Hampshire</td>\n",
" <td>0.96</td>\n",
" <td>532</td>\n",
" <td>520</td>\n",
" <td>1052</td>\n",
" <td>0.18</td>\n",
" <td>25.4</td>\n",
" <td>25.1</td>\n",
" <td>26.0</td>\n",
" <td>24.9</td>\n",
" <td>25.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>New Jersey</td>\n",
" <td>0.70</td>\n",
" <td>530</td>\n",
" <td>526</td>\n",
" <td>1056</td>\n",
" <td>0.34</td>\n",
" <td>23.8</td>\n",
" <td>23.8</td>\n",
" <td>24.1</td>\n",
" <td>23.2</td>\n",
" <td>23.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>New York</td>\n",
" <td>0.67</td>\n",
" <td>528</td>\n",
" <td>523</td>\n",
" <td>1052</td>\n",
" <td>0.31</td>\n",
" <td>23.8</td>\n",
" <td>24.0</td>\n",
" <td>24.6</td>\n",
" <td>23.9</td>\n",
" <td>24.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Pennsylvania</td>\n",
" <td>0.65</td>\n",
" <td>540</td>\n",
" <td>531</td>\n",
" <td>1071</td>\n",
" <td>0.23</td>\n",
" <td>23.4</td>\n",
" <td>23.4</td>\n",
" <td>24.2</td>\n",
" <td>23.3</td>\n",
" <td>23.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Rhode Island</td>\n",
" <td>0.71</td>\n",
" <td>539</td>\n",
" <td>524</td>\n",
" <td>1062</td>\n",
" <td>0.21</td>\n",
" <td>24.0</td>\n",
" <td>23.3</td>\n",
" <td>24.7</td>\n",
" <td>23.4</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>South Carolina</td>\n",
" <td>0.50</td>\n",
" <td>543</td>\n",
" <td>521</td>\n",
" <td>1064</td>\n",
" <td>1.00</td>\n",
" <td>17.5</td>\n",
" <td>18.6</td>\n",
" <td>19.1</td>\n",
" <td>18.9</td>\n",
" <td>18.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Texas</td>\n",
" <td>0.62</td>\n",
" <td>513</td>\n",
" <td>507</td>\n",
" <td>1020</td>\n",
" <td>0.45</td>\n",
" <td>19.5</td>\n",
" <td>20.7</td>\n",
" <td>21.1</td>\n",
" <td>20.9</td>\n",
" <td>20.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Vermont</td>\n",
" <td>0.60</td>\n",
" <td>562</td>\n",
" <td>551</td>\n",
" <td>1114</td>\n",
" <td>0.29</td>\n",
" <td>23.3</td>\n",
" <td>23.1</td>\n",
" <td>24.4</td>\n",
" <td>23.2</td>\n",
" <td>23.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Virginia</td>\n",
" <td>0.65</td>\n",
" <td>561</td>\n",
" <td>541</td>\n",
" <td>1102</td>\n",
" <td>0.29</td>\n",
" <td>23.5</td>\n",
" <td>23.3</td>\n",
" <td>24.6</td>\n",
" <td>23.5</td>\n",
" <td>23.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Washington</td>\n",
" <td>0.64</td>\n",
" <td>541</td>\n",
" <td>534</td>\n",
" <td>1075</td>\n",
" <td>0.29</td>\n",
" <td>20.9</td>\n",
" <td>21.9</td>\n",
" <td>22.1</td>\n",
" <td>22.0</td>\n",
" <td>21.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State SAT Participation SAT Read and Write SAT Math \\\n",
"4 California 0.53 531 524 \n",
"6 Connecticut 1.00 530 512 \n",
"7 Delaware 1.00 503 492 \n",
"8 District of Columbia 1.00 482 468 \n",
"9 Florida 0.83 520 497 \n",
"10 Georgia 0.61 535 515 \n",
"11 Hawaii 0.55 544 541 \n",
"12 Idaho 0.93 513 493 \n",
"14 Indiana 0.63 542 532 \n",
"19 Maine 0.95 513 499 \n",
"20 Maryland 0.69 536 52 \n",
"21 Massachusetts 0.76 555 551 \n",
"22 Michigan 1.00 509 495 \n",
"29 New Hampshire 0.96 532 520 \n",
"30 New Jersey 0.70 530 526 \n",
"32 New York 0.67 528 523 \n",
"38 Pennsylvania 0.65 540 531 \n",
"39 Rhode Island 0.71 539 524 \n",
"40 South Carolina 0.50 543 521 \n",
"43 Texas 0.62 513 507 \n",
"45 Vermont 0.60 562 551 \n",
"46 Virginia 0.65 561 541 \n",
"47 Washington 0.64 541 534 \n",
"\n",
" Total ACT Participation ACT English ACT Math ACT Reading ACT Science \\\n",
"4 1055 0.31 22.5 22.7 23.1 22.2 \n",
"6 1041 0.31 25.5 24.6 25.6 24.6 \n",
"7 996 0.18 24.1 23.4 24.8 23.6 \n",
"8 950 0.32 24.4 23.5 24.9 23.5 \n",
"9 1017 0.73 19.0 19.4 21.0 19.4 \n",
"10 1050 0.55 21.0 20.9 22.0 21.3 \n",
"11 1085 0.90 17.8 19.2 19.2 19.3 \n",
"12 1005 0.38 21.9 21.8 23.0 22.1 \n",
"14 1074 0.35 22.0 22.4 23.2 22.3 \n",
"19 1012 0.08 24.2 24.0 24.8 23.7 \n",
"20 1060 0.28 23.3 23.1 24.2 2.3 \n",
"21 1107 0.29 25.4 25.3 25.9 24.7 \n",
"22 1005 0.29 24.1 23.7 24.5 23.8 \n",
"29 1052 0.18 25.4 25.1 26.0 24.9 \n",
"30 1056 0.34 23.8 23.8 24.1 23.2 \n",
"32 1052 0.31 23.8 24.0 24.6 23.9 \n",
"38 1071 0.23 23.4 23.4 24.2 23.3 \n",
"39 1062 0.21 24.0 23.3 24.7 23.4 \n",
"40 1064 1.00 17.5 18.6 19.1 18.9 \n",
"43 1020 0.45 19.5 20.7 21.1 20.9 \n",
"45 1114 0.29 23.3 23.1 24.4 23.2 \n",
"46 1102 0.29 23.5 23.3 24.6 23.5 \n",
"47 1075 0.29 20.9 21.9 22.1 22.0 \n",
"\n",
" ACT Composite \n",
"4 22.8 \n",
"6 25.2 \n",
"7 24.1 \n",
"8 24.2 \n",
"9 19.8 \n",
"10 21.4 \n",
"11 19.0 \n",
"12 22.3 \n",
"14 22.6 \n",
"19 24.3 \n",
"20 23.6 \n",
"21 25.4 \n",
"22 24.1 \n",
"29 25.5 \n",
"30 23.9 \n",
"32 24.2 \n",
"38 23.7 \n",
"39 24.0 \n",
"40 18.7 \n",
"43 20.7 \n",
"45 23.6 \n",
"46 23.8 \n",
"47 21.9 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Using a boolean to specifically show an inputted column (here SAT Participation) that has a value greater of 0.5 or 50%\n",
"\n",
"clean_table[(clean_table)['SAT Participation'] >= 0.50]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3: Visualize the data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"16. Using MatPlotLib and PyPlot, plot the distribution of the Rate columns for both SAT and ACT using histograms. (You should have two histograms. You might find this link helpful in organizing one plot above the other.)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7401ac3fd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7401ac39b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Drawing histograms of the SAT and ACT participation variables\n",
"\n",
"plt.figure(1)\n",
"n, bins, patches = plt.hist(clean_table['SAT Participation'], 10, facecolor='blue')\n",
"plt.xlabel('Participation Rate')\n",
"#plt.ylabel('Probability')\n",
"plt.title('SAT')\n",
"#plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
"# plt.axis([40, 160, 0, 0.03])\n",
"# plt.grid(True)\n",
"plt.show()\n",
"\n",
"plt.figure(2)\n",
"n, bins, patches = plt.hist(clean_table['ACT Participation'], 10, facecolor='blue')\n",
"plt.xlabel('Participation Rate')\n",
"#plt.ylabel('Probability')\n",
"plt.title('ACT')\n",
"# plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
"# plt.axis([40, 160, 0, 0.03])\n",
"# plt.grid(True)\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 17. Plot the Math(s) distributions from both data sets."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Cleaning up outliers from the SAT Math and ACT Science columns. Both seem to have at least one point that is\n",
"# significantly lower than the other scores.\n",
"\n",
"clean_sat_math = clean_table[(clean_table)['SAT Math'] >= 300]\n",
"clean_act_science = clean_table[(clean_table)['ACT Science'] >= 15]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7401d76c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7401da3048>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7401d02240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(1)\n",
"n, bins, patches = plt.hist(clean_sat_math['SAT Math'], 10, facecolor='blue')\n",
"plt.xlabel('Math Score')\n",
"plt.ylabel('Probability')\n",
"plt.title('SAT')\n",
"# plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
"# plt.axis([40, 160, 0, 0.03])\n",
"# plt.grid(True)\n",
"plt.show()\n",
"\n",
"plt.figure(2)\n",
"n, bins, patches = plt.hist(clean_table['ACT Math'], 10, facecolor='blue')\n",
"plt.xlabel('Math Score')\n",
"plt.ylabel('Probability')\n",
"plt.title('ACT')\n",
"# plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
"# plt.axis([40, 160, 0, 0.03])\n",
"# plt.grid(True)\n",
"plt.show()\n",
"\n",
"plt.figure(3)\n",
"n, bins, patches = plt.hist(clean_act_science['ACT Science'], 10, facecolor='blue')\n",
"plt.xlabel('Science Score')\n",
"plt.ylabel('Probability')\n",
"plt.title('ACT Science')\n",
"# plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
"# plt.axis([40, 160, 0, 0.03])\n",
"# plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 18. Plot the Verbal distributions from both data sets."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7405e1f208>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7405e9bac8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7405ef07b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# f, (ax1, ax3) = plt.subplots(1,2, figsize = (14,6))\n",
"\n",
"\n",
"plt.figure(1)\n",
"n, bins, patches = plt.hist(clean_table['SAT Read and Write'], 10, facecolor='blue')\n",
"plt.xlabel('Reading and Writing Score')\n",
"plt.ylabel('Probability')\n",
"plt.title('SAT Reading and Writing')\n",
"# plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
"# plt.axis([40, 160, 0, 0.03])\n",
"# plt.grid(True)\n",
"plt.show()\n",
"\n",
"plt.figure(2)\n",
"n, bins, patches = plt.hist(clean_table['ACT English'], 10, facecolor='blue')\n",
"plt.xlabel('English Score')\n",
"plt.ylabel('Probability')\n",
"plt.title('ACT English')\n",
"# plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
"# plt.axis([40, 160, 0, 0.03])\n",
"# plt.grid(True)\n",
"plt.show()\n",
"\n",
"plt.figure(3)\n",
"n, bins, patches = plt.hist(clean_table['ACT Reading'], 10, facecolor='blue')\n",
"plt.xlabel('Reading Score')\n",
"plt.ylabel('Probability')\n",
"plt.title('ACT Reading')\n",
"# plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
"# plt.axis([40, 160, 0, 0.03])\n",
"# plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 19. When we make assumptions about how data are distributed, what is the most common assumption?"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The most common assumption of how data is usually distributed is that it matches a normal distribution where\n",
"# mean = mode = median"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 20. Does this assumption hold true for any of our columns? Which?"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The ACT Science score seems to be the closest variable to achieving a normal distribution. All of the others are\n",
"# either positively or negatively skewed."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 21. Plot some scatterplots examining relationships between all variables."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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cePfT+Mr3X8XtH1mA2bOCrDEHK9SP1/Tvw+xZQdz+kbRpsR7Lnb6CfZcqVShD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iPrkfsY+pz6get0TCaImEbR1vMB+NCeRBQU2TxRuazpgax903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\n2HZYUkr5ihCiq+LhTwL4cP7fWwC8DGBt/vGtUkoJ4CdCiPOEEBfmn/uClHI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phjcjtmmLyAOSYvYI7J7Zhh8gLmmNyO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\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Aeogev9Yp2YBfqll64KlXnDr1PID2ZdLJBacd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\nmmG+gzn1Pff4obRnPo4fSjuxD8dlsdsylUpWM7mC5yd4Zzx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85cn9TiKiZqj0uJ+mCYQD\nmROCQR+iiRTrL5EN8sexm548WvZ4PTlL8Rh52ZVzsGFP4Xh4w8Awa7HFGn2l4L8A+AWA/0NKOQIA\nQogvN/hv1CUc9GFuR7jsTSz5iXEib2tGDTC6gW4l9YjILkb94Afrlhhmti3kZ5apbo2qvdXcsLyW\n1xOpJpvhrtntzLJNKqkjpq8J+fCpnYe430lE1AA87kfkLBzHulvx+Nd8Pftx+QP7WIst0uh7Cq4E\n8G8AnhdC7BRCfASAUmsvGk/h9fFo2ZtYTiVTiMSSeOJzS/DM+usxa0aIn7Il8pBm1ACjG+hWUo+I\n7GLUD86OGWc2Eksyy1QXXZeIxJPoaAuif8UVuP2qOTVf5VTtDct5g3NyumyGR0YnTGs0NVcldcTs\nNWfHory6k4ioQcod98vOSgEBRGJJzJoRYv0lslE0xnGsmxWPf83W88joBGuxhRp6UlBK+Y9Syk8C\n+AMALwD4MoCLhRDfFELc2si/VatwwIeZ4QC2ruouuqFwT+4mlrouEYklcf9Tx7HowX3oHzyBTbcu\nwsUXhfjpBCIPaFYNMLqZ+cxwADv6eniDc1KOWT/42alR08wW55tZpkplP6l91+7DBXlbcfUlNX06\ntNo8Mr/kdNkM73/lHLasLNzP2b66B63MctO1+jVsX90zfdn7L+xyG9eaHjxy4FTB7+Kn4omIalfq\nuF+rP31v13W7hnD5A/tw/1PHc2NOgPWXyGq6LpHSdWxd1W04jt2yspt90uGKx8j7Xzk3bcy8ZWU3\nHn1+JPd/WIubr9HThwIApJQRAD8A8AMhRAeAVQDuA/Bcqf8nhJgLYDeA3wOgA3hcSrk98zt+CGA+\ngDMA/kJK+W4tbdM0gRktAQT8Gnau6UU45EM0lp6zNntJanqu2/TctgBw8PQYNu89hq9/4ipE4ym0\nh5qy2MgFrMgwNV+zaoDZzcwBKHWDc+aYgNL9oC3kN8ysUb7tyDIz7Dz59xkALuStf8UVePt8rOra\nW20eVcpvFnNM1chm+DMf/iBaAz58885rMKMlgJHRCex58Sw+8+EPYoav0ZPElOa1DE8mdex58Sz6\nV1yBrtntuWX/2esXoD2z7I1qjSaAt96LFfyu7NUs3O+0n9dyTO7jxQyXOu5Xasw5ePRN1l9FeTHH\nXhFNpPCFJ17GrBkh3H1TFy55Xwu+eec1uKg1gNfemsDTR95Ij6VC1o5jG83LGTYaIx/+zTgeX3Mt\n2kJ+RGJJfPeXv8bg0Tdz/4e1uPmavmSllOMAvpV5lJME8NdSypeFEDMAHBZC/ATAXwL4n1LKbwgh\n7kP6BOPmWtukaQLh4IW33t5SuBjM7vUwrzMM8D6XVJolGabmamYN0DSR26jlb9yMnrMRc0xl+0H2\nZEl+Zs3ybQNm2GHM8tY1u73mK/aqzaNC+c1ijqkqmibQFkrfiyOpXxiw+DWBL31koR1N8lSGw0Ef\ndvx0BNsOvJZ7zmjZF9caXZfY0bd42j2teKWyMjyVY3IlT2bY7LhfqTEnZ4pQmidz7AXZPpnUZe6k\nkF8TOPnQcvQPnnBTn/Rshs3GyKe+uhyaEGgL+tG35DIcPD3OsbCFlDrNLqU8J6V8OfP1eQCvAvgA\ngI8D2JV52S4AdzSzHab3g4ileINLKkmVDFN9vF4DmGMCnN0PmGHnMb8XV9KzNxhnjqkWKt0f02sZ\nrnXZ5189eOqry7Fzba9n656KvJZjch9muJBZrZ6Mp1h/FcYcu5dX+qSXM1xujMyxsD2U+Bi0ESHE\nfACLARwCcLGU8hyQ7kRCiNn1/v5USkc0kcpdphoO+ODLTOuSvdfDtE9rci5bqkKzM0zNU08N0HWJ\naCJV8fRz1b7easyxd5n1g9aAholYMp3ZWAqaBrQE1MwvwAw7hVne2oL+splyW901whxTpcIBHx67\n8xq8G01gbkcYr49HMTMcsP2Ttl7IsOn4MeCbVnda/Romk3pBHVLsSmUy4IUck7t5OcP5xwC/9elr\n8b1//jV2/HSkYF9f9fEgpXk5x26UHj/1YODQWSy7cg66ZrcjEkuiNaDljtO7jdcybLZ/oon0vrmm\nCRVn7XE9JZeyEKIdwF4A90gp3xOi/IZZCHEXgLsAYN68eSVfm0rpOB9L4rfRBMJBP8Ym4kiFA5gR\n8sPn0yy5r0ylB6SceOCKastw5v9VnGOncsLB21prgK5LjEXiWD9wBBdfFMI9t1yOeZ1hTEwlDXcy\n8l+ff/BIlU/ENLsWkz0qOTCZHZTl94OpRAq6LiE0gXfOx/DIgVN4670Ytq7qxsP7T+Kt92JK5Rdg\nLS5WSz21qgbXUnd1XaZfF/IVZLJUDlWvu0aYY+tVWidVJKWcNtO5zDwP2NNmt2Y4vwZlPyTTEQ5M\nq2NSSkTi6QPRr701gf2vnMPqD83DnhfPFhyQVrkOkXtzXMr8+55p6u8/842PNe13V9v2ZrZFFW7N\ncCXbbCllwTHA8Ugca/54Pu6+uQuTCV3p7ToVcmuOvSh7oj4c9KE14MOaP56P30YTAIDxSBxSBjCj\nJeC6vunGDOfX4UjmA+T5tVVKCSEE5naEMTKaHguvvHYu9v6vM+hbchnHwDZR7pS7ECKAdOf4gZTy\nqczTbwkh5mR+PgfAaPH/k1I+LqXslVL2zpo1q+TfiCV1TMSSuP+p41j04D7c/9RxTMSSiCX13Guy\nZ6g1kfm3wScExyJxrNs1hMsf2Id1u4YwFolD12VNryO11JphoLocO1G1mbazD9RSA7I3LZ81I4SN\nH12E+586nm73buN259/kPKlLHDw9hvUDRxBNWD/FVzErajFZr7hPfecXp0v2sdyntSQQiaWwbvdh\nXP5Aeru58aOLMGtGCPc+eQxfvLFLqfwCrMXFaqmnVtfgaupurm27h6ZlslQOVa67Rphj65nl/ju/\nOO2I8Xgl+zlWcmuG0zmJ5WrQut1DGI/E01fSB3y5OgYAY9E4Pv/9w1j04D70D57AHYsvxZ4X05+G\nd0IdIvfmmLzDrRmudJttum1M6A0/3kfN49Yce1EqpWMsEsdduw9jZDSCSDw1rY+ejyUxlXTX2MiN\nGS6uw3ftPox/fXcqd6wpldINx8J7D7+OZVfO4RjYRkqdFBTp0+N/D+BVKeW2vB8NAlib+XotgP9R\nz9/RJXDvk8cKDgjd++QxWLVvX+kBKacduCLrMuxU1WbaaX0ge4Pku2/qwua9x8q22+wm53ZPVcwc\nu1dxn1p25Rxs2DNc0/Zo895juPumLrx0Zhxds9sBqJFfgBk2Uks9VbkGl8ukWQ5VrbtGmGN7GGVr\nw55hx5zAsXs/J5+bM5zOyfC05fxuNFGQjWgihQ1Fr9u891hueqwsVesQuTvH5A1uznCl22yVto1U\nGzfn2IuiiVTuOETX7Ha0Bf3GfdSez7Q1hVszbLZfnn/Cr9RYmGNg+yh1UhDAnwD4NICbhRDDmcft\nAL4B4KNCiNcAfDTzfc3CIZMDQiFrQljpASknHbiiHEsy7FTVZtppfSB789zshi2fUbvL3WzXRsyx\nSxX3qUqzatYXu2a347r5HRgZnQCgTH4BZniaWuqpyjW4XCbNcqhw3TXCHNugVLbyv1ehHxixez+n\niGszbJaTuR3hgmyUylN22wkoXYfIxTkmz3BthiveZqu1baTauDbHXtQW8uf65MjohFf6qCszXKoO\nv3RmvGBdF/98ZHSCY2AbKXVPQSnlL2F+s4uPNOrvRGNJXDe/AwdPj+Weu25+B6KxJNpbAo36M+Z/\nP3NAatrfj6cKbqZZ6etIHVZl2KmqzbTT+kD6BsmL8fp4tKJ2Z19ffG+rcMDegQ9z7F7FfSo7CKt1\ne/T6eDR3T8GlCzqVyC/ADBuppZ6qXINLZbJUDlWtu0aYY3uYZcvoBI7d/cCI3fs5+dyc4VI16P0z\nQrlsmL0uEkti/yvn4NeE0nWI3J1j8gY3Z7jSbXZEoW0j1cbNOfai/D756PMjeOiOK036aArtLeqN\nt2vh1gyXqsOl6u/EVHoszDGwfVS7UtAS4aAfO/p6sHRBJ/yayBzI7EE4aE2hyR6QKvz70ztBpa8j\ncopqM+20PqBpAp1tQcy+KGRQY6a3O/v6nWt7ceqry7FzbS9vsEtNVdyn9r9yDttXl8+qcV/swewZ\nIXS0BbHtkz3Mr+Jqqacq12DTTF4UKplD1l0qxyhb21f35E7gqNQPjNi9n+MV6ZwULuetq7oxMxwo\nyIZhnvp6EA748NnrF7AOERHVoZptNreNROoIB3y54xDPHj+Hfx55G9sN+6ia4226wKgOb1nZXXDC\nz2gs3BZKj4U5BraPkNJ9k2j39vbKoaGhkq/RdYloIoVw0IdoPJW+IbyFIaz079vdTpdyxAKsJMdO\nVG2mndoHLGi38gvBrRl2uuJstvo1TCZ1O7ZHymcYcFeOa1mHKtdgRdqmxsIow005tkKtdVIVVfYN\ndd9IHhUzrOsyvXxDPkRjKWga0OKfvqwVqVVu54gF2owcz7/vmYpfe+YbH2va765Fte2pRrVtb2Zb\nKuTZDNer0m02a7ElHLFAVcyxF6VSOqKJFNpCfkRiSbT6fZhKKTHeVj7HqmU4v75GYkmEgz5MJnTW\nX/tUtHA9eaUgkP6keDiQCWPQh2giBb3oLsO6LjERS0KX6X9TKb3g++LXV/v320N+aCLzb4lPtFfy\nOqcpXrb1LEtqvkaur3KZLv5bACrqA6plKv99hgOZGqNI28j9zPqD0WDM59PS05xlYylgmNNspsu9\njhqjGTWtlrpUqmbbXXeN2mZ3m8gdirPl82nT9huM9guYPw8SF/5t8ac/zV6cgXLbT+aGiKh2xdts\nIS6MVaWUkJh+XCF/HByNJzExxRpMZJXsuEdoAgFNXDiJlORJIzfRRHpqUV1mjkEFfDw+qhjPXiuv\n6xJjkfi0e8pkL1st/vn6m7uw+kPzsGHPsOHrqXLllj2pxcr1VevfUjlTKreN3Mkscx3hAMajCcMs\nAqgop8yzNZq9nBvx+1XMgoptIneobL+gB0Gfhi888bKt+WM/sIbxcjbPAGC8nS21beb6IiKqjlFt\n3rKyG08feQN9Sy6bVo8vviiETcsW4d4nj7EGE1kgv4/eduXFWH7lHOx58SzuWHwpNu9lP3Qio7r7\n8KqrEQoI/Kd/GOa4V2GevVIwmkhh/cARHDw9hqQucfD0GNYPHEE0kTL8+bIr52DDnmHT16tE9U+b\nllv2pJZGr69S+az1b6mcKZXbRu5UKnOGz8dTFeeUebZGM5ezrktE4sm6f7+KWVCxTeQOle0XDOPd\naML2/EUTKQwc+g36V1yBkw8tR/+KKzBw6DfsBxWqdD/KuN6YZ6DqbTPXFxFRTj21efPeY1h25RzD\nevzFG7tw75PHWIOJLDKVTCESS+KJzy3Bny++FHtePItlV87B5r3sh6oqV3+N6u6mJ49iYirFca/i\nPHulYDjow0tnxguee+nMeO4mpsU/75rdXvL1qnDCp4PLLXtSSyPXV7l81vq3VM6Uym0jdzLLXFvI\nb5zFkA9Syopyyjxbo1nLOVuDO9qCdf9+FbOgYpvIHSrdL5jbEZ72nNX5aw1o0z5tvWVlN1oDnv0s\naMWq2Y8yqzelMlDVtpl1i4gIQGNqc3a7XVyPnXKcj8gNdF0iEkvi/qeOF4xRPzCzlf1QUZXU32rG\nxBz3qsWze4fReArXze/Aiqsvwf57bsCvvnY7Dmz8U0xlrxTM/DxrZHSi4HsAuG5+B6Jxtc5mlzvr\nrsJVhMXLFlBzWVJao9ZXJVen1Pq3VMmUUf9SpW3kHWaZi8SShs+fHYtWlNPsIP7kQ8uxryqTJAAA\nIABJREFU/54bsOLqSwxfR/VrVt3IjhEaMaaxu7ax3pKVKt0veH08Ou25evNX7dg9Gk/h6SNvFFwp\n+PSRN9gPKlDNp5fN6o1ZBqYSKRzY+Kf41dduz21DS22bub6IyIsMx3dVHOOKxJJYf3NXwe+8bn5H\nbrsdjacK6rdTjvMRuUG6L1+YaWPWjBB0KTHJfThlRePlx8b5NTV7juXkQ8txfiqRO2YElD4mxXVt\nD8+eFAwHfHjszmvwldsWoX/wBBY9uA/3P3UckczAIxzwYUffYixd0Am/JrD/lXPYvron9/3SBZ3Y\n0bcY4YBaZ7NLfUo+e4Z/3a4hXP7APqzbNYSxSNzyE4PFy1bVZUlpjVhf2eyFg6U/FVLr31IhU2b9\nq9Wv2d428pZS/WFHX+F2bMvKbjxy4BTagv6S27hsvu/afRiLHtyH/sET2HTrImy8ZSHz3ATNqmnZ\nMcKjz49gy8ruot/fU9Xvt7Pust6S1SrbL+jBzHCgofmrZeweDvpwx+JLc/s3/YMncMfiS/kJ3ApU\nc7WxcQ00zkCrX8t9Mj67Tr5y2yI8duc1SoxhiYhUYLbNq+YY1127D2P1h+Zh4y0LC/Z39r9yLldb\n8+vuN18YwdZVxWNi1mCiZsjvyyuuvgSbbl2E+586jgefPs5+qCBdlwiHyo+NszV14y0LsenWC+dY\nvvjEy/jKbYtwR88lRcekOO5VhZBSrfvNNUJvb68cGhoq+7qJqSTW7R7CwdNjueeWLujEzrW9aA/5\nc59KCgd9iMZTaPVrmEzque/DAZ8yU3JmTcSSWLfL+D0BMP1Ze8jamWSLl63Fy1KtlWai0hxbod71\nlc1l/4or0D94omQGa/1bNmeqZN8LB3zNaJvyOVYpw15j1h+i8SRG34thbkcYI6MTePT5Ebx9Pob+\nFVdg/yvn8JkPfxBtIf+0nJrl+/E116It6K81z8pnGLAvx82oafnrccXVl+Dum7rQNbsd56cSCPo1\nhIPVjQXsqrs21NtSmGOPqGS/AEBD81cq62Zj9/NTCdy1+7BhvZ7REjD6L8xwRrXLzqgGAtMzEE2k\njNfjml60t0zf51RxH9MBHLHAmpHj+fc909DfZ6Uz3/hY0353tculmW2pkGcznFVqf8OoNpc6xvX4\nmmvRFvIjEksiHPRhMqEX1Nb8ujuVSEHXgXCINbgBHLHgOC62R/44a/89NxQcG1xx9SXY+NHLMa8z\nrEI/VD7HVmR4IpbEO+djuP+p42X3RbIzwxnW6jW9gEBunXLca4mKFqhnrxSs5Iy3pgm0h/zQRPpf\nn08r+F7F0JY6667SvXaKl62Ky5IuqHd9lb46pfBTIbX+LSszZTStSan+xbyT1cwy1+L3oS3kx53f\nPoSP7fgF3j4fw5aV3Xj0+RHs+OkI2gz+T6l8tzHPTdOMupF/teizx8+hf/AE/vXdSfQPnkBLDZ/O\na3ZtM5s2kfWW7FDJfkGj81fL2N3sXh1tFn8A0InCQd+0ceqWld2myzt/fWdP/uV2wSVyGTBdjyHj\nfU7WLSLyolLbvOJjXNv7etDq10rvowiBGS0B+DRtWm0tqN9BP9pbWIOJmi1/nFV8P8/Bo2/ilm0/\nAwD2Q0WEgz48cuCU4QxDrX6tYD8dMN8HCYd8BeuU4151eHLvMDvFQHYu2/yz2Nm5bK2+cq5RNE2g\nsy2Y/rR80Vn3CRe+X3KG7BzTg0ffBAD0r7gCXbPbEY0n67nKyBZmN9ptDfjYv0h52W3E42uuRTjo\nx8joBB5+7iQGj76JpQs6p+XVzdtLL9I0gbaQH1//xFW5q0Uffu4k3j4fU25dlrqpeTSRYh7JE7Lj\np2qyHo2Z/J9YCu0t7B+lTCb03P0Yu2a3Y2R0Ak8feQOfvX4B2kPmn6UtVa/SV+mzZhERlWNWKycT\nOjrCAXzr0+mr/0ZGJ7Dn0Fn0LbmM++BEDpI/zprk2Eh50XgKb70Xw8PPncyNjV8fj6It6Md4NMFj\noi7gySsFszcq3vYTozPezp/L1uysO+fuJbvkZy97dcp4JO64E4IATG90rmlg/yJH0DSRHshF4ugf\nPIFnj58zzavbt5deZHS1qIrr0qzWRhMpjmfIM2rJuqZh2n1Ztq7qhubJvb7qhAM+9C25rOB+jH1L\nLitbW0rVq+zvZc0iIiqtVK2cTOr4/PcP4/f/5lkse+Tn2HbgNe6DEzlMOODD6iXz0D94gvcRdIBs\nTX77fAwf2/EL3PntQ+mZRwR4TNQlPHmqNjvFQDIzDVX2jPdkPJWbesqNSl1FSNRMbsqe2RQlLQEf\nWvw+V7xHcr9K+6RXt5du5pR6XHKKUOGM90BUr1r6a0vAh4f3nyy42u3h/Sex7ZM9FrbcmWqtj+Wm\neXVK3SUislOpWsl9cCLny/bx7Bj13343ib/7D9245H2tPL6gILOaDAHWY5fw5EnB7LQEs2aEcPdN\nXblLYH/vopDrb3aZvYoQAC/fpYao9Caxbshe9ua5Jx9ajpHRCTz6/AgGj75ZcEm8098juZNZPy2V\nV6O8L3vk57kbS7tt++hUtdyoW+V6nP9+Dmz8U2z7yanc1NP5tVbl90DUSNVmPRpPYdkVF+Pii0IQ\nArj4ohCWXXExp+6pUCXLu7juagIlp0uqpU4TEXmRWQ3On1p0xdWX5I7jRWJJXoVC5CCTCR39gyew\n7IqLccfiD+Ci1gDem0ygxa9xbKQgo5o8EUti/c1d+PNrLsUHZrYiGkshEk9iKpFK36OV++iO4ck1\nFA748Nid1+B8LIl7nzyGl86MY/3NXVj9oXnYsGfY8F4QRDRduXuouInRe92yshtds9oqmlqKyC61\n9FPm3RncVoON3s/WVd3QBPDWe2pOc0qkmhafhmsv68AXn3g514+2r+5Bi4/zhzaCcd3twWN3XoMv\n5C3zbL1yW52m5pt/3zN2N0FJXC7elp3GbuDQb3DH4kuxeS+P4xE5UTjgw8411yIaT00bqwZ8Gnwc\nryqv1a9h9ZJ52DAwXLDPnkjq0P2StddBPNnbNE3Ap2m498ljuTlwl105Bxv2DJveC4KIpit3DxU3\nMXqvm/cew2c+/EHudJDSaumnzLszuK0GG72fe588hofuuAo71/Yye0QVmEympu3TbNgzjMmkM+uC\naozr7jB8moada3tx6qvLC+qV2+o0EZEdstPYfebDH8TmvTyOR+RUmiYgAcOxKvutM0wmdWwYGJ62\nz/5uNMF16DCevFIQAMKhwjnJu2a3l7wXhNNwmhqyQrl7qGS5IY9m77Ut5IcmnPVeyFuy2c2famdk\ndAKtAfPPBTHvzlCqBjux7pq+n5CPuSOqUFvIj4svCmH/PTfk6v03XxhBG6fwMVVNvaykTuVPl1Tp\nWJmIyMsqqcOaJtAW8rv6OB6RW+X3cQGBiy8KFfw8e6yB7FPpeNhsbDu3IwzusjuLJ68UBC7MSZ41\nMjpR8D1w4V4QTpOdpmbdriFc/sA+rNs1hLFIHLou7W4auUxxPwKm9xu35LGS90qkomg8hfU3d2HT\nrYvQP3gCix7ch/7BEyX7IfPuDGbraSqRcmTdZe6I6jcVT2HTssJ6v2nZIkyxHxmqdpxabZ1iXSMi\nKq2aOuzm43hEbjWtj+8ewqZli7Di6ktyr7lufgcisaSNrfS2euowkF5/r49HWXsdxrMnBbNzki9d\n0Am/JrD/lXPYvron9/3SBZ2OvXcNp6khqxT3I6N+45Y8VvJeiVQUDvjwl39SONXOwdNj2DBgPkUH\n8+4MZutJ1+HIusvcEdVPl7LgFgnZKX10qfaHAuxS7Ti12jrFukZEVFo1ddjNx/GI3MrsFhEbP3p5\nrt9uX93Dfmuj6utwYd3duqobM8MBrkOH8ey1udk5yXeu7c1dGtvq1wq+d8JUW0Y4TQ1ZxagfFfcb\nt+SxkvdKpCJNE2hv8VfVD5l3ZzBbTxBwZN1l7ojqFw6Z1HtOyWSo2nFqtXWKdY2IqLRq6rC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b0+A2rWaIN25WOdhntPChpdRSFreI1TVPJe/l8A86WU3QAO4MJZezdy07qt1Z9IKa8BsBzA3UKI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xJRWp/Ix3GBGI7SNsymcpWnGuUqKYYTeVKwGDO7093nNboc9aL1lVxR20ZRKw9E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\n/qLs3nNmdrCZ/cmCq7v+b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"text/plain": [
"<matplotlib.figure.Figure at 0x7401da37b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# The following code uses Seaborn to make scatterplots of all the data showing the relationships to each other.\n",
" \n",
"g = sns.pairplot(clean_table)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 22. Are there any interesting relationships to note?"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# SAT Reading and Writing and SAT Math have a positive linear relationship which makes some sense since you can generally infer\n",
"# that students who have high Reading and Writing scores could have a high Math score as well\n",
"# All of the ACT scores reflect this as well. All of the individual sections have a postive linear relationship with\n",
"# all of the other scores."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 23. Create box plots for each variable."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Ed Salinas\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
" return getattr(obj, method)(*args, **kwds)\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5,1,'Participation Rates')"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7406188860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7409451400>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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lSYsxSrivBm7pW97ZrdubFwL/tD9FSZL2z5EjtMk86+a9lHmSnwGmgVP2sn0zsBng+OOP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\nS1KDDHdJapDhLkkNMtwlqUGGuyQ16H8B2w9OZcQyyZoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x740ae38160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Due to the scale on the y-axis for each of the different Test type variables (ACT vs SAT scores), need\n",
"# to separate out each other. Again we use the cleaner data set that filters out outliers in SAT Math\n",
"# and ACT Science.\n",
"\n",
"sat_clean_boxplots = [clean_sat_math['SAT Math'], clean_table['SAT Read and Write']]\n",
"sat_labels = ('Math', 'Reading and Writing')\n",
"plt.figure(1)\n",
"plt.boxplot(sat_clean_boxplots, labels = sat_labels)\n",
"plt.title('SAT Scores')\n",
"\n",
"act_clean_boxplots = [clean_table['ACT English'], clean_table['ACT Reading'], clean_table['ACT Math'], \n",
" clean_act_science['ACT Science']]\n",
"act_labels = ('English', 'Reading', 'Math', 'Science')\n",
"plt.figure(2)\n",
"plt.boxplot(act_clean_boxplots, labels = act_labels)\n",
"plt.title('ACT Scores')\n",
"\n",
"participation_boxplots = [clean_table['SAT Participation'], clean_table['ACT Participation']]\n",
"part_labels = ('SAT', 'ACT')\n",
"plt.figure(3)\n",
"plt.boxplot(participation_boxplots, labels = part_labels)\n",
"plt.title('Participation Rates')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### BONUS: Using Tableau, create a heat map for each variable using a map of the US. "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Done on Tableau Public"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4: Descriptive and Inferential Statistics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 24. Summarize each distribution. As data scientists, be sure to back up these summaries with statistics. (Hint: What are the three things we care about when describing distributions?)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Mean, Standard Deviation, Shape\n",
"# For these observations, we note the following:\n",
"# Symmetric distributions are when mean = mode = median\n",
"# Negative Skew is when mean < median < mode\n",
"# Positive Skew is mode < median < mean\n",
"\n",
"# SAT Participation Rate is positively skewed meaning that participation is low.\n",
"# Mode = 0.03, Median = 0.38, Mean = .398 so Mode < Median < Mean\n",
"\n",
"# ACT Participation Rate is negatively skewed meaning that participation in this test is high.\n",
"# Mean = 0.653, Median = 0.69, Mode = 1 so Mean < Median < Mode\n",
"\n",
"# Both SAT and ACT Math scores are positively skewed.\n",
"# ACT Math: Mode = 19.4, Median = 20.9, Mean = 21.2 so Mode < Median < Mean\n",
"# SAT Math: Mode = 541, Median = 549.5, Mean = 557.54 so Mode < Median < Mean\n",
"\n",
"# ACT Science is very close to be normally distributed (a.k.a. symmetric distribution).\n",
"# Mode = 20.9, Median = 21.3, Mean = 21.4 so Mode = Median = Mean\n",
"\n",
"# SAT Reading and Writing, ACT English, and ACT Reading are all positively skewed.\n",
"# SAT Reading and Writing: Mode = 530, Median = 559, Mean = 569 so Mode < Median < Mean\n",
"# ACT English: Mode = 19.5, Median = 20.7, Mean = 20.9 so Mode < Median < Mean\n",
"# ACT Reading: Mode = 20.8, Median = 21.8, Mean = 22.0 so Mode < Median < Mean"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 25. Summarize each relationship. Be sure to back up these summaries with statistics."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Most of the relationships between variables are inherently straightforward. For example, SAT Math and SAT Reading\n",
"# and Writing have a linear relationship. This stands to reason since students who have a high Math score will\n",
"# tend to also have a high Reading and Writing score. The scatterplot for this relationship makes that pretty clear.\n",
"# This also holds true for the ACT scores. Each of the ACT score variables have a positive linear relationship\n",
"# with each other so that students with high Math scores tend to have high English and Science scores.\n",
"\n",
"# No other conclusions can be made from the data that has been presented to us."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 26. Execute a hypothesis test comparing the SAT and ACT participation rates. Use $\\alpha = 0.05$. Be sure to interpret your results."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Ttest_indResult(statistic=-3.8085778908170544, pvalue=0.00024134203698662353)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Two sample t-test is the best hypothesis test to do\n",
"\n",
"# H0 (null hypothesis): μ_SAT = μ_ACT \n",
"# HA:(alternative hypothesis): μ_SAT ≠ μ_ACT\n",
"\n",
"stats.ttest_ind(clean_table['SAT Participation'], clean_table['ACT Participation'])\n",
"\n",
"# p is 0.0002 while alpha is 0.05\n",
"# Because p is less than alpha, we reject the null hypothesis and conclude that the alternative hypothesis is true."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 27. Generate and interpret 95% confidence intervals for SAT and ACT participation rates."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-0.31051279537380627, 1.1065912267463553)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# stats.t.interval helps us to find the confidence intervals for both the SAT and ACT participation rates.\n",
"\n",
"stats.t.interval(0.95, len(clean_table['SAT Participation'])-1, loc=np.mean(clean_table['SAT Participation']),\n",
" scale=np.std(clean_table['SAT Participation'], ddof = 1))\n",
"\n",
"# The output shows that we are 95% confident that the true mean of the SAT partipation rate falls \n",
"# between 31.2% and 110%."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0069812093585757129, 1.2981168298571104)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.t.interval(0.95, len(clean_table['ACT Participation'])-1, loc=np.mean(clean_table['ACT Participation']),\n",
" scale=np.std(clean_table['ACT Participation'], ddof = 1))\n",
"\n",
"# The output shows that we are 95% confident that the true mean of the ACT participation rate falls\n",
"# between 0.70% and 130%."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 28. Given your answer to 26, was your answer to 27 surprising? Why?"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The answer is surprising, but not really surprising since the formulas used assume a normal distribution\n",
"# and neither of these data sets are normally distributed as evidenced earlier."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 29. Is it appropriate to generate correlation between SAT and ACT math scores? Why?"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# It would not be appropriate to generate correlation between ACT and SAT Math scores since the score is significantly\n",
"# different between the two tests. SAT Math scores range from 200 to 800 while ACT Math scores range from\n",
"# 1-36. There would not be a good way to correlate to two based on how the results are reported.\n",
"\n",
"# The scatterplot for ACT Math vs SAT Math also shows insignficant correlation between the two."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 30. Suppose we only seek to understand the relationship between SAT and ACT data in 2017. Does it make sense to conduct statistical inference given the data we have? Why?"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# State level data is very much generalized. We would need a more granular level analysis on the local school district or\n",
"# even county level per state. This would indicate a much, much larger data set, but we should be able to \n",
"# get a more detailed analysis and statistical inference from a more granular source."
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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