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I cleaned up my data but adding a colunm and removing one

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I learned how to change data set by using google sheets
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tsmith37
2025-11-04 20:22:03 -05:00
parent 6871602f17
commit ca517d7d43
3 changed files with 122 additions and 68 deletions
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69
.ipynb_checkpoints/argument-checkpoint.ipynb
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@@ -5,7 +5,7 @@
"id": "worldwide-blood",
"metadata": {},
"source": [
"# Introduction"
"# A Data Science Investigation About Fatal Car Crashes in America "
]
},
{
@@ -47,7 +47,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 7,
"id": "technical-evans",
"metadata": {},
"outputs": [],
@@ -59,14 +59,14 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 9,
"id": "overhead-sigma",
"metadata": {},
"outputs": [],
"source": [
"### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
"\n",
"file_name = \"bad-drivers.csv\"\n",
"file_name = \"Bad_Driver - bad-drivers.csv\"\n",
"dataset_path = \"data/\" + file_name\n",
"\n",
"df = pd.read_csv(dataset_path)"
@@ -74,7 +74,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 10,
"id": "heated-blade",
"metadata": {},
"outputs": [
@@ -106,7 +106,7 @@
" <th>Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted</th>\n",
" <th>Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents</th>\n",
" <th>Car Insurance Premiums ($)</th>\n",
" <th>Losses incurred by insurance companies for collisions per insured driver ($)</th>\n",
" <th>Region</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
@@ -119,7 +119,7 @@
" <td>96</td>\n",
" <td>80</td>\n",
" <td>784.55</td>\n",
" <td>145.08</td>\n",
" <td>Southeast</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
@@ -130,7 +130,7 @@
" <td>90</td>\n",
" <td>94</td>\n",
" <td>1053.48</td>\n",
" <td>133.93</td>\n",
" <td>West</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
@@ -141,7 +141,7 @@
" <td>84</td>\n",
" <td>96</td>\n",
" <td>899.47</td>\n",
" <td>110.35</td>\n",
" <td>Southeast</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
@@ -152,7 +152,7 @@
" <td>94</td>\n",
" <td>95</td>\n",
" <td>827.34</td>\n",
" <td>142.39</td>\n",
" <td>Southeast</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
@@ -163,7 +163,7 @@
" <td>91</td>\n",
" <td>89</td>\n",
" <td>878.41</td>\n",
" <td>165.63</td>\n",
" <td>West</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -212,22 +212,15 @@
"3 95 \n",
"4 89 \n",
"\n",
" Car Insurance Premiums ($) \\\n",
"0 784.55 \n",
"1 1053.48 \n",
"2 899.47 \n",
"3 827.34 \n",
"4 878.41 \n",
"\n",
" Losses incurred by insurance companies for collisions per insured driver ($) \n",
"0 145.08 \n",
"1 133.93 \n",
"2 110.35 \n",
"3 142.39 \n",
"4 165.63 "
" Car Insurance Premiums ($) Region \n",
"0 784.55 Southeast \n",
"1 1053.48 West \n",
"2 899.47 Southeast \n",
"3 827.34 Southeast \n",
"4 878.41 West "
]
},
"execution_count": 8,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -245,23 +238,31 @@
"\n",
"*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*\n",
"\n",
"### When is this data set from?\n",
"\n",
"I got the data set from FiveThirtyEight. It was used for an article called\n",
"\"Dear Mona, Which state has the worst drivers?\" in October 2014. The person who wrote the article is Mona Chalabi, they are a data editor at the Guardian US, \n",
"a columnist at New York Margazine, and a lead news writer for FiveThirtyEight.\n",
"\n",
"The date is about fatal collisions in each state. There are 7 rows, some of the rows\n",
"are about \"Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\" and \"Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\"\n",
"\n",
"### How did this data set get clean?\n",
"\n",
"I did not need to do much cleaning of the data myself, but I did add a column called \"Region\" to separate the state into 5 different regions: Northwest, Midwest, Southeast, West, and Northeast. I also excluded data on Losses incurred by insurance companies for collisions per insured driver because insurance companies are well known for finding ways to get out of paying customers for collisions, thus it is not an accurate representation of fatal car crashes. \n",
"\n",
"## What specific research questions will you investigate?\n",
"\n",
"1. Is drinking and driving the biggest cause of fatal collisions?\n",
"\n",
"2. What state are you most likely to get into a fatal collision for their first accident?\n",
"\n",
"3. What state is the most unlucky state for fatal collisions?\n",
"\n",
"4. Is there a connection between the speed and the roads that are causing fatal collisions, that would make the Car Insurance Premiums more expensive?\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "6ba44c9c-60d1-46a4-8257-b4e8eeea348d",
"metadata": {},
"source": [
"I will recategorise the data so that all of the states data will be separated into the five regions of the United States"
]
},
{
"cell_type": "code",
"execution_count": 40,
@@ -375,7 +376,7 @@
"id": "recognized-positive",
"metadata": {},
"source": [
"## First Research Question: Is drinking and driving the biggest cause of fatal collisions?\\"
"## First Research Question: Is drinking and driving the biggest cause of fatal collisions?"
]
},
{

69
argument.ipynb
View File

@@ -5,7 +5,7 @@
"id": "worldwide-blood",
"metadata": {},
"source": [
"# Introduction"
"# A Data Science Investigation About Fatal Car Crashes in America "
]
},
{
@@ -47,7 +47,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 7,
"id": "technical-evans",
"metadata": {},
"outputs": [],
@@ -59,14 +59,14 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 9,
"id": "overhead-sigma",
"metadata": {},
"outputs": [],
"source": [
"### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
"\n",
"file_name = \"bad-drivers.csv\"\n",
"file_name = \"Bad_Driver - bad-drivers.csv\"\n",
"dataset_path = \"data/\" + file_name\n",
"\n",
"df = pd.read_csv(dataset_path)"
@@ -74,7 +74,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 10,
"id": "heated-blade",
"metadata": {},
"outputs": [
@@ -106,7 +106,7 @@
" <th>Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted</th>\n",
" <th>Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents</th>\n",
" <th>Car Insurance Premiums ($)</th>\n",
" <th>Losses incurred by insurance companies for collisions per insured driver ($)</th>\n",
" <th>Region</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
@@ -119,7 +119,7 @@
" <td>96</td>\n",
" <td>80</td>\n",
" <td>784.55</td>\n",
" <td>145.08</td>\n",
" <td>Southeast</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
@@ -130,7 +130,7 @@
" <td>90</td>\n",
" <td>94</td>\n",
" <td>1053.48</td>\n",
" <td>133.93</td>\n",
" <td>West</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
@@ -141,7 +141,7 @@
" <td>84</td>\n",
" <td>96</td>\n",
" <td>899.47</td>\n",
" <td>110.35</td>\n",
" <td>Southeast</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
@@ -152,7 +152,7 @@
" <td>94</td>\n",
" <td>95</td>\n",
" <td>827.34</td>\n",
" <td>142.39</td>\n",
" <td>Southeast</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
@@ -163,7 +163,7 @@
" <td>91</td>\n",
" <td>89</td>\n",
" <td>878.41</td>\n",
" <td>165.63</td>\n",
" <td>West</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -212,22 +212,15 @@
"3 95 \n",
"4 89 \n",
"\n",
" Car Insurance Premiums ($) \\\n",
"0 784.55 \n",
"1 1053.48 \n",
"2 899.47 \n",
"3 827.34 \n",
"4 878.41 \n",
"\n",
" Losses incurred by insurance companies for collisions per insured driver ($) \n",
"0 145.08 \n",
"1 133.93 \n",
"2 110.35 \n",
"3 142.39 \n",
"4 165.63 "
" Car Insurance Premiums ($) Region \n",
"0 784.55 Southeast \n",
"1 1053.48 West \n",
"2 899.47 Southeast \n",
"3 827.34 Southeast \n",
"4 878.41 West "
]
},
"execution_count": 8,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -245,23 +238,31 @@
"\n",
"*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*\n",
"\n",
"### When is this data set from?\n",
"\n",
"I got the data set from FiveThirtyEight. It was used for an article called\n",
"\"Dear Mona, Which state has the worst drivers?\" in October 2014. The person who wrote the article is Mona Chalabi, they are a data editor at the Guardian US, \n",
"a columnist at New York Margazine, and a lead news writer for FiveThirtyEight.\n",
"\n",
"The date is about fatal collisions in each state. There are 7 rows, some of the rows\n",
"are about \"Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\" and \"Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\"\n",
"\n",
"### How did this data set get clean?\n",
"\n",
"I did not need to do much cleaning of the data myself, but I did add a column called \"Region\" to separate the state into 5 different regions: Northwest, Midwest, Southeast, West, and Northeast. I also excluded data on Losses incurred by insurance companies for collisions per insured driver because insurance companies are well known for finding ways to get out of paying customers for collisions, thus it is not an accurate representation of fatal car crashes. \n",
"\n",
"## What specific research questions will you investigate?\n",
"\n",
"1. Is drinking and driving the biggest cause of fatal collisions?\n",
"\n",
"2. What state are you most likely to get into a fatal collision for their first accident?\n",
"\n",
"3. What state is the most unlucky state for fatal collisions?\n",
"\n",
"4. Is there a connection between the speed and the roads that are causing fatal collisions, that would make the Car Insurance Premiums more expensive?\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "6ba44c9c-60d1-46a4-8257-b4e8eeea348d",
"metadata": {},
"source": [
"I will recategorise the data so that all of the states data will be separated into the five regions of the United States"
]
},
{
"cell_type": "code",
"execution_count": 40,
@@ -375,7 +376,7 @@
"id": "recognized-positive",
"metadata": {},
"source": [
"## First Research Question: Is drinking and driving the biggest cause of fatal collisions?\\"
"## First Research Question: Is drinking and driving the biggest cause of fatal collisions?"
]
},
{

52
data/Bad_Driver - bad-drivers.csv Normal file
View File

@@ -0,0 +1,52 @@
State,Number of drivers involved in fatal collisions per billion miles,Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding,Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired,Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted,Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents,Car Insurance Premiums ($),Region
Alabama,18.8,39,30,96,80,784.55,Southeast
Alaska,18.1,41,25,90,94,1053.48,West
Arizona,18.6,35,28,84,96,899.47,Southeast
Arkansas,22.4,18,26,94,95,827.34,Southeast
California,12,35,28,91,89,878.41,West
Colorado,13.6,37,28,79,95,835.5,West
Connecticut,10.8,46,36,87,82,1068.73,Northwest
Delaware,16.2,38,30,87,99,1137.87,Northwest
District of Columbia,5.9,34,27,100,100,1273.89,Northwest
Florida,17.9,21,29,92,94,1160.13,Southeast
Georgia,15.6,19,25,95,93,913.15,Southeast
Hawaii,17.5,54,41,82,87,861.18,West
Idaho,15.3,36,29,85,98,641.96,West
Illinois,12.8,36,34,94,96,803.11,Midwest
Indiana,14.5,25,29,95,95,710.46,Midwest
Iowa,15.7,17,25,97,87,649.06,Midwest
Kansas,17.8,27,24,77,85,780.45,Midwest
Kentucky,21.4,19,23,78,76,872.51,Southeast
Louisiana,20.5,35,33,73,98,1281.55,Southeast
Maine,15.1,38,30,87,84,661.88,Northeast
Maryland,12.5,34,32,71,99,1048.78,Northeast
Massachusetts,8.2,23,35,87,80,1011.14,Northeast
Michigan,14.1,24,28,95,77,1110.61,Midwest
Minnesota,9.6,23,29,88,88,777.18,Midwest
Mississippi,17.6,15,31,10,100,896.07,Southeast
Missouri,16.1,43,34,92,84,790.32,Midwest
Montana,21.4,39,44,84,85,816.21,West
Nebraska,14.9,13,35,93,90,732.28,Midwest
Nevada,14.7,37,32,95,99,1029.87,West
New Hampshire,11.6,35,30,87,83,746.54,Northeast
New Jersey,11.2,16,28,86,78,1301.52,Northeast
New Mexico,18.4,19,27,67,98,869.85,Southeast
New York,12.3,32,29,88,80,1234.31,Northeast
North Carolina,16.8,39,31,94,81,708.24,Southeast
North Dakota,23.9,23,42,99,86,688.75,Midwest
Ohio,14.1,28,34,99,82,697.73,Midwest
Oklahoma,19.9,32,29,92,94,881.51,Southeast
Oregon,12.8,33,26,67,90,804.71,West
Pennsylvania,18.2,50,31,96,88,905.99,Northeast
Rhode Island,11.1,34,38,92,79,1148.99,Northeast
South Carolina,23.9,38,41,96,81,858.97,Southeast
South Dakota,19.4,31,33,98,86,669.31,Midwest
Tennessee,19.5,21,29,82,81,767.91,Southeast
Texas,19.4,40,38,91,87,1004.75,Southeast
Utah,11.3,43,16,88,96,809.38,West
Vermont,13.6,30,30,96,95,716.2,Northeast
Virginia,12.7,19,27,87,88,768.95,Southeast
Washington,10.6,42,33,82,86,890.03,West
West Virginia,23.8,34,28,97,87,992.61,Southeast
Wisconsin,13.8,36,33,39,84,670.31,Midwest
Wyoming,17.4,42,32,81,90,791.14,West
1 State Number of drivers involved in fatal collisions per billion miles Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents Car Insurance Premiums ($) Region
2 Alabama 18.8 39 30 96 80 784.55 Southeast
3 Alaska 18.1 41 25 90 94 1053.48 West
4 Arizona 18.6 35 28 84 96 899.47 Southeast
5 Arkansas 22.4 18 26 94 95 827.34 Southeast
6 California 12 35 28 91 89 878.41 West
7 Colorado 13.6 37 28 79 95 835.5 West
8 Connecticut 10.8 46 36 87 82 1068.73 Northwest
9 Delaware 16.2 38 30 87 99 1137.87 Northwest
10 District of Columbia 5.9 34 27 100 100 1273.89 Northwest
11 Florida 17.9 21 29 92 94 1160.13 Southeast
12 Georgia 15.6 19 25 95 93 913.15 Southeast
13 Hawaii 17.5 54 41 82 87 861.18 West
14 Idaho 15.3 36 29 85 98 641.96 West
15 Illinois 12.8 36 34 94 96 803.11 Midwest
16 Indiana 14.5 25 29 95 95 710.46 Midwest
17 Iowa 15.7 17 25 97 87 649.06 Midwest
18 Kansas 17.8 27 24 77 85 780.45 Midwest
19 Kentucky 21.4 19 23 78 76 872.51 Southeast
20 Louisiana 20.5 35 33 73 98 1281.55 Southeast
21 Maine 15.1 38 30 87 84 661.88 Northeast
22 Maryland 12.5 34 32 71 99 1048.78 Northeast
23 Massachusetts 8.2 23 35 87 80 1011.14 Northeast
24 Michigan 14.1 24 28 95 77 1110.61 Midwest
25 Minnesota 9.6 23 29 88 88 777.18 Midwest
26 Mississippi 17.6 15 31 10 100 896.07 Southeast
27 Missouri 16.1 43 34 92 84 790.32 Midwest
28 Montana 21.4 39 44 84 85 816.21 West
29 Nebraska 14.9 13 35 93 90 732.28 Midwest
30 Nevada 14.7 37 32 95 99 1029.87 West
31 New Hampshire 11.6 35 30 87 83 746.54 Northeast
32 New Jersey 11.2 16 28 86 78 1301.52 Northeast
33 New Mexico 18.4 19 27 67 98 869.85 Southeast
34 New York 12.3 32 29 88 80 1234.31 Northeast
35 North Carolina 16.8 39 31 94 81 708.24 Southeast
36 North Dakota 23.9 23 42 99 86 688.75 Midwest
37 Ohio 14.1 28 34 99 82 697.73 Midwest
38 Oklahoma 19.9 32 29 92 94 881.51 Southeast
39 Oregon 12.8 33 26 67 90 804.71 West
40 Pennsylvania 18.2 50 31 96 88 905.99 Northeast
41 Rhode Island 11.1 34 38 92 79 1148.99 Northeast
42 South Carolina 23.9 38 41 96 81 858.97 Southeast
43 South Dakota 19.4 31 33 98 86 669.31 Midwest
44 Tennessee 19.5 21 29 82 81 767.91 Southeast
45 Texas 19.4 40 38 91 87 1004.75 Southeast
46 Utah 11.3 43 16 88 96 809.38 West
47 Vermont 13.6 30 30 96 95 716.2 Northeast
48 Virginia 12.7 19 27 87 88 768.95 Southeast
49 Washington 10.6 42 33 82 86 890.03 West
50 West Virginia 23.8 34 28 97 87 992.61 Southeast
51 Wisconsin 13.8 36 33 39 84 670.31 Midwest
52 Wyoming 17.4 42 32 81 90 791.14 West