This project aims to visualize earnings based on college majors for students studied in US universities. The strategy is to analyze job outcomes of students who graduated from college between 2010 and 2012. The data set was originally collected by American Community Survey but was later cleaned by FiveThirtyEight and release at their Github repo.The data can be found through the link:https://github.com/fivethirtyeight/data/blob/master/college-majors/all-ages.csv
import pandas as pd
import matplotlib.pyplot as plt
% matplotlib inline
recent_grads = pd.read_csv("recent-grads.csv")
recent_grads.iloc[0]
recent_grads.head() # acess data at top 5 rows
Rank | Major_code | Major | Total | Men | Women | Major_category | ShareWomen | Sample_size | Employed | ... | Part_time | Full_time_year_round | Unemployed | Unemployment_rate | Median | P25th | P75th | College_jobs | Non_college_jobs | Low_wage_jobs | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 2419 | PETROLEUM ENGINEERING | 2339.0 | 2057.0 | 282.0 | Engineering | 0.120564 | 36 | 1976 | ... | 270 | 1207 | 37 | 0.018381 | 110000 | 95000 | 125000 | 1534 | 364 | 193 |
1 | 2 | 2416 | MINING AND MINERAL ENGINEERING | 756.0 | 679.0 | 77.0 | Engineering | 0.101852 | 7 | 640 | ... | 170 | 388 | 85 | 0.117241 | 75000 | 55000 | 90000 | 350 | 257 | 50 |
2 | 3 | 2415 | METALLURGICAL ENGINEERING | 856.0 | 725.0 | 131.0 | Engineering | 0.153037 | 3 | 648 | ... | 133 | 340 | 16 | 0.024096 | 73000 | 50000 | 105000 | 456 | 176 | 0 |
3 | 4 | 2417 | NAVAL ARCHITECTURE AND MARINE ENGINEERING | 1258.0 | 1123.0 | 135.0 | Engineering | 0.107313 | 16 | 758 | ... | 150 | 692 | 40 | 0.050125 | 70000 | 43000 | 80000 | 529 | 102 | 0 |
4 | 5 | 2405 | CHEMICAL ENGINEERING | 32260.0 | 21239.0 | 11021.0 | Engineering | 0.341631 | 289 | 25694 | ... | 5180 | 16697 | 1672 | 0.061098 | 65000 | 50000 | 75000 | 18314 | 4440 | 972 |
5 rows × 21 columns
recent_grads.tail()# To acess data at bottom 5 rows
Rank | Major_code | Major | Total | Men | Women | Major_category | ShareWomen | Sample_size | Employed | ... | Part_time | Full_time_year_round | Unemployed | Unemployment_rate | Median | P25th | P75th | College_jobs | Non_college_jobs | Low_wage_jobs | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
168 | 169 | 3609 | ZOOLOGY | 8409.0 | 3050.0 | 5359.0 | Biology & Life Science | 0.637293 | 47 | 6259 | ... | 2190 | 3602 | 304 | 0.046320 | 26000 | 20000 | 39000 | 2771 | 2947 | 743 |
169 | 170 | 5201 | EDUCATIONAL PSYCHOLOGY | 2854.0 | 522.0 | 2332.0 | Psychology & Social Work | 0.817099 | 7 | 2125 | ... | 572 | 1211 | 148 | 0.065112 | 25000 | 24000 | 34000 | 1488 | 615 | 82 |
170 | 171 | 5202 | CLINICAL PSYCHOLOGY | 2838.0 | 568.0 | 2270.0 | Psychology & Social Work | 0.799859 | 13 | 2101 | ... | 648 | 1293 | 368 | 0.149048 | 25000 | 25000 | 40000 | 986 | 870 | 622 |
171 | 172 | 5203 | COUNSELING PSYCHOLOGY | 4626.0 | 931.0 | 3695.0 | Psychology & Social Work | 0.798746 | 21 | 3777 | ... | 965 | 2738 | 214 | 0.053621 | 23400 | 19200 | 26000 | 2403 | 1245 | 308 |
172 | 173 | 3501 | LIBRARY SCIENCE | 1098.0 | 134.0 | 964.0 | Education | 0.877960 | 2 | 742 | ... | 237 | 410 | 87 | 0.104946 | 22000 | 20000 | 22000 | 288 | 338 | 192 |
5 rows × 21 columns
recent_grads.describe() # To get summary statistics of data set
Rank | Major_code | Total | Men | Women | ShareWomen | Sample_size | Employed | Full_time | Part_time | Full_time_year_round | Unemployed | Unemployment_rate | Median | P25th | P75th | College_jobs | Non_college_jobs | Low_wage_jobs | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 173.000000 | 173.000000 | 172.000000 | 172.000000 | 172.000000 | 172.000000 | 173.000000 | 173.000000 | 173.000000 | 173.000000 | 173.000000 | 173.000000 | 173.000000 | 173.000000 | 173.000000 | 173.000000 | 173.000000 | 173.000000 | 173.000000 |
mean | 87.000000 | 3879.815029 | 39370.081395 | 16723.406977 | 22646.674419 | 0.522223 | 356.080925 | 31192.763006 | 26029.306358 | 8832.398844 | 19694.427746 | 2416.329480 | 0.068191 | 40151.445087 | 29501.445087 | 51494.219653 | 12322.635838 | 13284.497110 | 3859.017341 |
std | 50.084928 | 1687.753140 | 63483.491009 | 28122.433474 | 41057.330740 | 0.231205 | 618.361022 | 50675.002241 | 42869.655092 | 14648.179473 | 33160.941514 | 4112.803148 | 0.030331 | 11470.181802 | 9166.005235 | 14906.279740 | 21299.868863 | 23789.655363 | 6944.998579 |
min | 1.000000 | 1100.000000 | 124.000000 | 119.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 111.000000 | 0.000000 | 111.000000 | 0.000000 | 0.000000 | 22000.000000 | 18500.000000 | 22000.000000 | 0.000000 | 0.000000 | 0.000000 |
25% | 44.000000 | 2403.000000 | 4549.750000 | 2177.500000 | 1778.250000 | 0.336026 | 39.000000 | 3608.000000 | 3154.000000 | 1030.000000 | 2453.000000 | 304.000000 | 0.050306 | 33000.000000 | 24000.000000 | 42000.000000 | 1675.000000 | 1591.000000 | 340.000000 |
50% | 87.000000 | 3608.000000 | 15104.000000 | 5434.000000 | 8386.500000 | 0.534024 | 130.000000 | 11797.000000 | 10048.000000 | 3299.000000 | 7413.000000 | 893.000000 | 0.067961 | 36000.000000 | 27000.000000 | 47000.000000 | 4390.000000 | 4595.000000 | 1231.000000 |
75% | 130.000000 | 5503.000000 | 38909.750000 | 14631.000000 | 22553.750000 | 0.703299 | 338.000000 | 31433.000000 | 25147.000000 | 9948.000000 | 16891.000000 | 2393.000000 | 0.087557 | 45000.000000 | 33000.000000 | 60000.000000 | 14444.000000 | 11783.000000 | 3466.000000 |
max | 173.000000 | 6403.000000 | 393735.000000 | 173809.000000 | 307087.000000 | 0.968954 | 4212.000000 | 307933.000000 | 251540.000000 | 115172.000000 | 199897.000000 | 28169.000000 | 0.177226 | 110000.000000 | 95000.000000 | 125000.000000 | 151643.000000 | 148395.000000 | 48207.000000 |
To test if there is missing rows in our data set, I first use df.isnull() method ,then used df.dropna( axis =0) method to drop any raw with missing values. After applying this method, I noticed that, there is only one missing row in our data set. The raw data set has 173 rows while the clean data set has now 172 rows after dropping the missing row.
recent_grads.isnull()
Rank | Major_code | Major | Total | Men | Women | Major_category | ShareWomen | Sample_size | Employed | ... | Part_time | Full_time_year_round | Unemployed | Unemployment_rate | Median | P25th | P75th | College_jobs | Non_college_jobs | Low_wage_jobs | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
1 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
2 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
3 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
4 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
5 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
6 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
7 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
8 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
9 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
10 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
11 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
12 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
13 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
14 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
15 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
16 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
17 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
18 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
19 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
20 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
21 | False | False | False | True | True | True | False | True | False | False | ... | False | False | False | False | False | False | False | False | False | False |
22 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
23 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
24 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
25 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
26 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
27 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
28 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
29 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
143 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
144 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
145 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
146 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
147 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
148 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
149 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
150 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
151 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
152 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
153 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
154 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
155 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
156 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
157 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
158 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
159 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
160 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
161 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
162 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
163 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
164 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
165 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
166 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
167 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
168 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
169 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
170 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
171 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
172 | False | False | False | False | False | False | False | False | False | False | ... | False | False | False | False | False | False | False | False | False | False |
173 rows × 21 columns
recent_grads.isnull().sum()
Rank 0 Major_code 0 Major 0 Total 1 Men 1 Women 1 Major_category 0 ShareWomen 1 Sample_size 0 Employed 0 Full_time 0 Part_time 0 Full_time_year_round 0 Unemployed 0 Unemployment_rate 0 Median 0 P25th 0 P75th 0 College_jobs 0 Non_college_jobs 0 Low_wage_jobs 0 dtype: int64
recent_grads.iloc[:] # raw_data_count with 173 rows
Rank | Major_code | Major | Total | Men | Women | Major_category | ShareWomen | Sample_size | Employed | ... | Part_time | Full_time_year_round | Unemployed | Unemployment_rate | Median | P25th | P75th | College_jobs | Non_college_jobs | Low_wage_jobs | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 2419 | PETROLEUM ENGINEERING | 2339.0 | 2057.0 | 282.0 | Engineering | 0.120564 | 36 | 1976 | ... | 270 | 1207 | 37 | 0.018381 | 110000 | 95000 | 125000 | 1534 | 364 | 193 |
1 | 2 | 2416 | MINING AND MINERAL ENGINEERING | 756.0 | 679.0 | 77.0 | Engineering | 0.101852 | 7 | 640 | ... | 170 | 388 | 85 | 0.117241 | 75000 | 55000 | 90000 | 350 | 257 | 50 |
2 | 3 | 2415 | METALLURGICAL ENGINEERING | 856.0 | 725.0 | 131.0 | Engineering | 0.153037 | 3 | 648 | ... | 133 | 340 | 16 | 0.024096 | 73000 | 50000 | 105000 | 456 | 176 | 0 |
3 | 4 | 2417 | NAVAL ARCHITECTURE AND MARINE ENGINEERING | 1258.0 | 1123.0 | 135.0 | Engineering | 0.107313 | 16 | 758 | ... | 150 | 692 | 40 | 0.050125 | 70000 | 43000 | 80000 | 529 | 102 | 0 |
4 | 5 | 2405 | CHEMICAL ENGINEERING | 32260.0 | 21239.0 | 11021.0 | Engineering | 0.341631 | 289 | 25694 | ... | 5180 | 16697 | 1672 | 0.061098 | 65000 | 50000 | 75000 | 18314 | 4440 | 972 |
5 | 6 | 2418 | NUCLEAR ENGINEERING | 2573.0 | 2200.0 | 373.0 | Engineering | 0.144967 | 17 | 1857 | ... | 264 | 1449 | 400 | 0.177226 | 65000 | 50000 | 102000 | 1142 | 657 | 244 |
6 | 7 | 6202 | ACTUARIAL SCIENCE | 3777.0 | 2110.0 | 1667.0 | Business | 0.441356 | 51 | 2912 | ... | 296 | 2482 | 308 | 0.095652 | 62000 | 53000 | 72000 | 1768 | 314 | 259 |
7 | 8 | 5001 | ASTRONOMY AND ASTROPHYSICS | 1792.0 | 832.0 | 960.0 | Physical Sciences | 0.535714 | 10 | 1526 | ... | 553 | 827 | 33 | 0.021167 | 62000 | 31500 | 109000 | 972 | 500 | 220 |
8 | 9 | 2414 | MECHANICAL ENGINEERING | 91227.0 | 80320.0 | 10907.0 | Engineering | 0.119559 | 1029 | 76442 | ... | 13101 | 54639 | 4650 | 0.057342 | 60000 | 48000 | 70000 | 52844 | 16384 | 3253 |
9 | 10 | 2408 | ELECTRICAL ENGINEERING | 81527.0 | 65511.0 | 16016.0 | Engineering | 0.196450 | 631 | 61928 | ... | 12695 | 41413 | 3895 | 0.059174 | 60000 | 45000 | 72000 | 45829 | 10874 | 3170 |
10 | 11 | 2407 | COMPUTER ENGINEERING | 41542.0 | 33258.0 | 8284.0 | Engineering | 0.199413 | 399 | 32506 | ... | 5146 | 23621 | 2275 | 0.065409 | 60000 | 45000 | 75000 | 23694 | 5721 | 980 |
11 | 12 | 2401 | AEROSPACE ENGINEERING | 15058.0 | 12953.0 | 2105.0 | Engineering | 0.139793 | 147 | 11391 | ... | 2724 | 8790 | 794 | 0.065162 | 60000 | 42000 | 70000 | 8184 | 2425 | 372 |
12 | 13 | 2404 | BIOMEDICAL ENGINEERING | 14955.0 | 8407.0 | 6548.0 | Engineering | 0.437847 | 79 | 10047 | ... | 2694 | 5986 | 1019 | 0.092084 | 60000 | 36000 | 70000 | 6439 | 2471 | 789 |
13 | 14 | 5008 | MATERIALS SCIENCE | 4279.0 | 2949.0 | 1330.0 | Engineering | 0.310820 | 22 | 3307 | ... | 878 | 1967 | 78 | 0.023043 | 60000 | 39000 | 65000 | 2626 | 391 | 81 |
14 | 15 | 2409 | ENGINEERING MECHANICS PHYSICS AND SCIENCE | 4321.0 | 3526.0 | 795.0 | Engineering | 0.183985 | 30 | 3608 | ... | 811 | 2004 | 23 | 0.006334 | 58000 | 25000 | 74000 | 2439 | 947 | 263 |
15 | 16 | 2402 | BIOLOGICAL ENGINEERING | 8925.0 | 6062.0 | 2863.0 | Engineering | 0.320784 | 55 | 6170 | ... | 1983 | 3413 | 589 | 0.087143 | 57100 | 40000 | 76000 | 3603 | 1595 | 524 |
16 | 17 | 2412 | INDUSTRIAL AND MANUFACTURING ENGINEERING | 18968.0 | 12453.0 | 6515.0 | Engineering | 0.343473 | 183 | 15604 | ... | 2243 | 11326 | 699 | 0.042876 | 57000 | 37900 | 67000 | 8306 | 3235 | 640 |
17 | 18 | 2400 | GENERAL ENGINEERING | 61152.0 | 45683.0 | 15469.0 | Engineering | 0.252960 | 425 | 44931 | ... | 7199 | 33540 | 2859 | 0.059824 | 56000 | 36000 | 69000 | 26898 | 11734 | 3192 |
18 | 19 | 2403 | ARCHITECTURAL ENGINEERING | 2825.0 | 1835.0 | 990.0 | Engineering | 0.350442 | 26 | 2575 | ... | 343 | 1848 | 170 | 0.061931 | 54000 | 38000 | 65000 | 1665 | 649 | 137 |
19 | 20 | 3201 | COURT REPORTING | 1148.0 | 877.0 | 271.0 | Law & Public Policy | 0.236063 | 14 | 930 | ... | 223 | 808 | 11 | 0.011690 | 54000 | 50000 | 54000 | 402 | 528 | 144 |
20 | 21 | 2102 | COMPUTER SCIENCE | 128319.0 | 99743.0 | 28576.0 | Computers & Mathematics | 0.222695 | 1196 | 102087 | ... | 18726 | 70932 | 6884 | 0.063173 | 53000 | 39000 | 70000 | 68622 | 25667 | 5144 |
21 | 22 | 1104 | FOOD SCIENCE | NaN | NaN | NaN | Agriculture & Natural Resources | NaN | 36 | 3149 | ... | 1121 | 1735 | 338 | 0.096931 | 53000 | 32000 | 70000 | 1183 | 1274 | 485 |
22 | 23 | 2502 | ELECTRICAL ENGINEERING TECHNOLOGY | 11565.0 | 8181.0 | 3384.0 | Engineering | 0.292607 | 97 | 8587 | ... | 1873 | 5681 | 824 | 0.087557 | 52000 | 35000 | 60000 | 5126 | 2686 | 696 |
23 | 24 | 2413 | MATERIALS ENGINEERING AND MATERIALS SCIENCE | 2993.0 | 2020.0 | 973.0 | Engineering | 0.325092 | 22 | 2449 | ... | 1040 | 1151 | 70 | 0.027789 | 52000 | 35000 | 62000 | 1911 | 305 | 70 |
24 | 25 | 6212 | MANAGEMENT INFORMATION SYSTEMS AND STATISTICS | 18713.0 | 13496.0 | 5217.0 | Business | 0.278790 | 278 | 16413 | ... | 2420 | 13017 | 1015 | 0.058240 | 51000 | 38000 | 60000 | 6342 | 5741 | 708 |
25 | 26 | 2406 | CIVIL ENGINEERING | 53153.0 | 41081.0 | 12072.0 | Engineering | 0.227118 | 565 | 43041 | ... | 10080 | 29196 | 3270 | 0.070610 | 50000 | 40000 | 60000 | 28526 | 9356 | 2899 |
26 | 27 | 5601 | CONSTRUCTION SERVICES | 18498.0 | 16820.0 | 1678.0 | Industrial Arts & Consumer Services | 0.090713 | 295 | 16318 | ... | 1751 | 12313 | 1042 | 0.060023 | 50000 | 36000 | 60000 | 3275 | 5351 | 703 |
27 | 28 | 6204 | OPERATIONS LOGISTICS AND E-COMMERCE | 11732.0 | 7921.0 | 3811.0 | Business | 0.324838 | 156 | 10027 | ... | 1183 | 7724 | 504 | 0.047859 | 50000 | 40000 | 60000 | 1466 | 3629 | 285 |
28 | 29 | 2499 | MISCELLANEOUS ENGINEERING | 9133.0 | 7398.0 | 1735.0 | Engineering | 0.189970 | 118 | 7428 | ... | 1662 | 5476 | 597 | 0.074393 | 50000 | 39000 | 65000 | 3445 | 2426 | 365 |
29 | 30 | 5402 | PUBLIC POLICY | 5978.0 | 2639.0 | 3339.0 | Law & Public Policy | 0.558548 | 55 | 4547 | ... | 1306 | 2776 | 670 | 0.128426 | 50000 | 35000 | 70000 | 1550 | 1871 | 340 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
143 | 144 | 1105 | PLANT SCIENCE AND AGRONOMY | 7416.0 | 4897.0 | 2519.0 | Agriculture & Natural Resources | 0.339671 | 110 | 6594 | ... | 1246 | 4522 | 314 | 0.045455 | 32000 | 22900 | 40000 | 2089 | 3545 | 1231 |
144 | 145 | 2308 | SCIENCE AND COMPUTER TEACHER EDUCATION | 6483.0 | 2049.0 | 4434.0 | Education | 0.683943 | 59 | 5362 | ... | 1227 | 3247 | 266 | 0.047264 | 32000 | 28000 | 39000 | 4214 | 1106 | 591 |
145 | 146 | 5200 | PSYCHOLOGY | 393735.0 | 86648.0 | 307087.0 | Psychology & Social Work | 0.779933 | 2584 | 307933 | ... | 115172 | 174438 | 28169 | 0.083811 | 31500 | 24000 | 41000 | 125148 | 141860 | 48207 |
146 | 147 | 6002 | MUSIC | 60633.0 | 29909.0 | 30724.0 | Arts | 0.506721 | 419 | 47662 | ... | 24943 | 21425 | 3918 | 0.075960 | 31000 | 22300 | 42000 | 13752 | 28786 | 9286 |
147 | 148 | 2306 | PHYSICAL AND HEALTH EDUCATION TEACHING | 28213.0 | 15670.0 | 12543.0 | Education | 0.444582 | 259 | 23794 | ... | 7230 | 13651 | 1920 | 0.074667 | 31000 | 24000 | 40000 | 12777 | 9328 | 2042 |
148 | 149 | 6006 | ART HISTORY AND CRITICISM | 21030.0 | 3240.0 | 17790.0 | Humanities & Liberal Arts | 0.845934 | 204 | 17579 | ... | 6140 | 9965 | 1128 | 0.060298 | 31000 | 23000 | 40000 | 5139 | 9738 | 3426 |
149 | 150 | 6000 | FINE ARTS | 74440.0 | 24786.0 | 49654.0 | Arts | 0.667034 | 623 | 59679 | ... | 23656 | 31877 | 5486 | 0.084186 | 30500 | 21000 | 41000 | 20792 | 32725 | 11880 |
150 | 151 | 2901 | FAMILY AND CONSUMER SCIENCES | 58001.0 | 5166.0 | 52835.0 | Industrial Arts & Consumer Services | 0.910933 | 518 | 46624 | ... | 15872 | 26906 | 3355 | 0.067128 | 30000 | 22900 | 40000 | 20985 | 20133 | 5248 |
151 | 152 | 5404 | SOCIAL WORK | 53552.0 | 5137.0 | 48415.0 | Psychology & Social Work | 0.904075 | 374 | 45038 | ... | 13481 | 27588 | 3329 | 0.068828 | 30000 | 25000 | 35000 | 27449 | 14416 | 4344 |
152 | 153 | 1103 | ANIMAL SCIENCES | 21573.0 | 5347.0 | 16226.0 | Agriculture & Natural Resources | 0.752144 | 255 | 17112 | ... | 5353 | 10824 | 917 | 0.050862 | 30000 | 22000 | 40000 | 5443 | 9571 | 2125 |
153 | 154 | 6003 | VISUAL AND PERFORMING ARTS | 16250.0 | 4133.0 | 12117.0 | Arts | 0.745662 | 132 | 12870 | ... | 6253 | 6322 | 1465 | 0.102197 | 30000 | 22000 | 40000 | 3849 | 7635 | 2840 |
154 | 155 | 2312 | TEACHER EDUCATION: MULTIPLE LEVELS | 14443.0 | 2734.0 | 11709.0 | Education | 0.810704 | 142 | 13076 | ... | 2214 | 8457 | 496 | 0.036546 | 30000 | 24000 | 37000 | 10766 | 1949 | 722 |
155 | 156 | 5299 | MISCELLANEOUS PSYCHOLOGY | 9628.0 | 1936.0 | 7692.0 | Psychology & Social Work | 0.798920 | 60 | 7653 | ... | 3221 | 3838 | 419 | 0.051908 | 30000 | 20800 | 40000 | 2960 | 3948 | 1650 |
156 | 157 | 5403 | HUMAN SERVICES AND COMMUNITY ORGANIZATION | 9374.0 | 885.0 | 8489.0 | Psychology & Social Work | 0.905590 | 89 | 8294 | ... | 2405 | 5061 | 326 | 0.037819 | 30000 | 24000 | 35000 | 2878 | 4595 | 724 |
157 | 158 | 3402 | HUMANITIES | 6652.0 | 2013.0 | 4639.0 | Humanities & Liberal Arts | 0.697384 | 49 | 5052 | ... | 2225 | 2661 | 372 | 0.068584 | 30000 | 20000 | 49000 | 1168 | 3354 | 1141 |
158 | 159 | 4901 | THEOLOGY AND RELIGIOUS VOCATIONS | 30207.0 | 18616.0 | 11591.0 | Humanities & Liberal Arts | 0.383719 | 310 | 24202 | ... | 8767 | 13944 | 1617 | 0.062628 | 29000 | 22000 | 38000 | 9927 | 12037 | 3304 |
159 | 160 | 6007 | STUDIO ARTS | 16977.0 | 4754.0 | 12223.0 | Arts | 0.719974 | 182 | 13908 | ... | 5673 | 7413 | 1368 | 0.089552 | 29000 | 19200 | 38300 | 3948 | 8707 | 3586 |
160 | 161 | 2201 | COSMETOLOGY SERVICES AND CULINARY ARTS | 10510.0 | 4364.0 | 6146.0 | Industrial Arts & Consumer Services | 0.584776 | 117 | 8650 | ... | 2064 | 5949 | 510 | 0.055677 | 29000 | 20000 | 36000 | 563 | 7384 | 3163 |
161 | 162 | 1199 | MISCELLANEOUS AGRICULTURE | 1488.0 | 404.0 | 1084.0 | Agriculture & Natural Resources | 0.728495 | 24 | 1290 | ... | 335 | 936 | 82 | 0.059767 | 29000 | 23000 | 42100 | 483 | 626 | 31 |
162 | 163 | 5502 | ANTHROPOLOGY AND ARCHEOLOGY | 38844.0 | 11376.0 | 27468.0 | Humanities & Liberal Arts | 0.707136 | 247 | 29633 | ... | 14515 | 13232 | 3395 | 0.102792 | 28000 | 20000 | 38000 | 9805 | 16693 | 6866 |
163 | 164 | 6102 | COMMUNICATION DISORDERS SCIENCES AND SERVICES | 38279.0 | 1225.0 | 37054.0 | Health | 0.967998 | 95 | 29763 | ... | 13862 | 14460 | 1487 | 0.047584 | 28000 | 20000 | 40000 | 19957 | 9404 | 5125 |
164 | 165 | 2307 | EARLY CHILDHOOD EDUCATION | 37589.0 | 1167.0 | 36422.0 | Education | 0.968954 | 342 | 32551 | ... | 7001 | 20748 | 1360 | 0.040105 | 28000 | 21000 | 35000 | 23515 | 7705 | 2868 |
165 | 166 | 2603 | OTHER FOREIGN LANGUAGES | 11204.0 | 3472.0 | 7732.0 | Humanities & Liberal Arts | 0.690111 | 56 | 7052 | ... | 3685 | 3214 | 846 | 0.107116 | 27500 | 22900 | 38000 | 2326 | 3703 | 1115 |
166 | 167 | 6001 | DRAMA AND THEATER ARTS | 43249.0 | 14440.0 | 28809.0 | Arts | 0.666119 | 357 | 36165 | ... | 15994 | 16891 | 3040 | 0.077541 | 27000 | 19200 | 35000 | 6994 | 25313 | 11068 |
167 | 168 | 3302 | COMPOSITION AND RHETORIC | 18953.0 | 7022.0 | 11931.0 | Humanities & Liberal Arts | 0.629505 | 151 | 15053 | ... | 6612 | 7832 | 1340 | 0.081742 | 27000 | 20000 | 35000 | 4855 | 8100 | 3466 |
168 | 169 | 3609 | ZOOLOGY | 8409.0 | 3050.0 | 5359.0 | Biology & Life Science | 0.637293 | 47 | 6259 | ... | 2190 | 3602 | 304 | 0.046320 | 26000 | 20000 | 39000 | 2771 | 2947 | 743 |
169 | 170 | 5201 | EDUCATIONAL PSYCHOLOGY | 2854.0 | 522.0 | 2332.0 | Psychology & Social Work | 0.817099 | 7 | 2125 | ... | 572 | 1211 | 148 | 0.065112 | 25000 | 24000 | 34000 | 1488 | 615 | 82 |
170 | 171 | 5202 | CLINICAL PSYCHOLOGY | 2838.0 | 568.0 | 2270.0 | Psychology & Social Work | 0.799859 | 13 | 2101 | ... | 648 | 1293 | 368 | 0.149048 | 25000 | 25000 | 40000 | 986 | 870 | 622 |
171 | 172 | 5203 | COUNSELING PSYCHOLOGY | 4626.0 | 931.0 | 3695.0 | Psychology & Social Work | 0.798746 | 21 | 3777 | ... | 965 | 2738 | 214 | 0.053621 | 23400 | 19200 | 26000 | 2403 | 1245 | 308 |
172 | 173 | 3501 | LIBRARY SCIENCE | 1098.0 | 134.0 | 964.0 | Education | 0.877960 | 2 | 742 | ... | 237 | 410 | 87 | 0.104946 | 22000 | 20000 | 22000 | 288 | 338 | 192 |
173 rows × 21 columns
recent_grads.dropna(axis = 0)# cleaned_data_count after dropping missing row
Rank | Major_code | Major | Total | Men | Women | Major_category | ShareWomen | Sample_size | Employed | ... | Part_time | Full_time_year_round | Unemployed | Unemployment_rate | Median | P25th | P75th | College_jobs | Non_college_jobs | Low_wage_jobs | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 2419 | PETROLEUM ENGINEERING | 2339.0 | 2057.0 | 282.0 | Engineering | 0.120564 | 36 | 1976 | ... | 270 | 1207 | 37 | 0.018381 | 110000 | 95000 | 125000 | 1534 | 364 | 193 |
1 | 2 | 2416 | MINING AND MINERAL ENGINEERING | 756.0 | 679.0 | 77.0 | Engineering | 0.101852 | 7 | 640 | ... | 170 | 388 | 85 | 0.117241 | 75000 | 55000 | 90000 | 350 | 257 | 50 |
2 | 3 | 2415 | METALLURGICAL ENGINEERING | 856.0 | 725.0 | 131.0 | Engineering | 0.153037 | 3 | 648 | ... | 133 | 340 | 16 | 0.024096 | 73000 | 50000 | 105000 | 456 | 176 | 0 |
3 | 4 | 2417 | NAVAL ARCHITECTURE AND MARINE ENGINEERING | 1258.0 | 1123.0 | 135.0 | Engineering | 0.107313 | 16 | 758 | ... | 150 | 692 | 40 | 0.050125 | 70000 | 43000 | 80000 | 529 | 102 | 0 |
4 | 5 | 2405 | CHEMICAL ENGINEERING | 32260.0 | 21239.0 | 11021.0 | Engineering | 0.341631 | 289 | 25694 | ... | 5180 | 16697 | 1672 | 0.061098 | 65000 | 50000 | 75000 | 18314 | 4440 | 972 |
5 | 6 | 2418 | NUCLEAR ENGINEERING | 2573.0 | 2200.0 | 373.0 | Engineering | 0.144967 | 17 | 1857 | ... | 264 | 1449 | 400 | 0.177226 | 65000 | 50000 | 102000 | 1142 | 657 | 244 |
6 | 7 | 6202 | ACTUARIAL SCIENCE | 3777.0 | 2110.0 | 1667.0 | Business | 0.441356 | 51 | 2912 | ... | 296 | 2482 | 308 | 0.095652 | 62000 | 53000 | 72000 | 1768 | 314 | 259 |
7 | 8 | 5001 | ASTRONOMY AND ASTROPHYSICS | 1792.0 | 832.0 | 960.0 | Physical Sciences | 0.535714 | 10 | 1526 | ... | 553 | 827 | 33 | 0.021167 | 62000 | 31500 | 109000 | 972 | 500 | 220 |
8 | 9 | 2414 | MECHANICAL ENGINEERING | 91227.0 | 80320.0 | 10907.0 | Engineering | 0.119559 | 1029 | 76442 | ... | 13101 | 54639 | 4650 | 0.057342 | 60000 | 48000 | 70000 | 52844 | 16384 | 3253 |
9 | 10 | 2408 | ELECTRICAL ENGINEERING | 81527.0 | 65511.0 | 16016.0 | Engineering | 0.196450 | 631 | 61928 | ... | 12695 | 41413 | 3895 | 0.059174 | 60000 | 45000 | 72000 | 45829 | 10874 | 3170 |
10 | 11 | 2407 | COMPUTER ENGINEERING | 41542.0 | 33258.0 | 8284.0 | Engineering | 0.199413 | 399 | 32506 | ... | 5146 | 23621 | 2275 | 0.065409 | 60000 | 45000 | 75000 | 23694 | 5721 | 980 |
11 | 12 | 2401 | AEROSPACE ENGINEERING | 15058.0 | 12953.0 | 2105.0 | Engineering | 0.139793 | 147 | 11391 | ... | 2724 | 8790 | 794 | 0.065162 | 60000 | 42000 | 70000 | 8184 | 2425 | 372 |
12 | 13 | 2404 | BIOMEDICAL ENGINEERING | 14955.0 | 8407.0 | 6548.0 | Engineering | 0.437847 | 79 | 10047 | ... | 2694 | 5986 | 1019 | 0.092084 | 60000 | 36000 | 70000 | 6439 | 2471 | 789 |
13 | 14 | 5008 | MATERIALS SCIENCE | 4279.0 | 2949.0 | 1330.0 | Engineering | 0.310820 | 22 | 3307 | ... | 878 | 1967 | 78 | 0.023043 | 60000 | 39000 | 65000 | 2626 | 391 | 81 |
14 | 15 | 2409 | ENGINEERING MECHANICS PHYSICS AND SCIENCE | 4321.0 | 3526.0 | 795.0 | Engineering | 0.183985 | 30 | 3608 | ... | 811 | 2004 | 23 | 0.006334 | 58000 | 25000 | 74000 | 2439 | 947 | 263 |
15 | 16 | 2402 | BIOLOGICAL ENGINEERING | 8925.0 | 6062.0 | 2863.0 | Engineering | 0.320784 | 55 | 6170 | ... | 1983 | 3413 | 589 | 0.087143 | 57100 | 40000 | 76000 | 3603 | 1595 | 524 |
16 | 17 | 2412 | INDUSTRIAL AND MANUFACTURING ENGINEERING | 18968.0 | 12453.0 | 6515.0 | Engineering | 0.343473 | 183 | 15604 | ... | 2243 | 11326 | 699 | 0.042876 | 57000 | 37900 | 67000 | 8306 | 3235 | 640 |
17 | 18 | 2400 | GENERAL ENGINEERING | 61152.0 | 45683.0 | 15469.0 | Engineering | 0.252960 | 425 | 44931 | ... | 7199 | 33540 | 2859 | 0.059824 | 56000 | 36000 | 69000 | 26898 | 11734 | 3192 |
18 | 19 | 2403 | ARCHITECTURAL ENGINEERING | 2825.0 | 1835.0 | 990.0 | Engineering | 0.350442 | 26 | 2575 | ... | 343 | 1848 | 170 | 0.061931 | 54000 | 38000 | 65000 | 1665 | 649 | 137 |
19 | 20 | 3201 | COURT REPORTING | 1148.0 | 877.0 | 271.0 | Law & Public Policy | 0.236063 | 14 | 930 | ... | 223 | 808 | 11 | 0.011690 | 54000 | 50000 | 54000 | 402 | 528 | 144 |
20 | 21 | 2102 | COMPUTER SCIENCE | 128319.0 | 99743.0 | 28576.0 | Computers & Mathematics | 0.222695 | 1196 | 102087 | ... | 18726 | 70932 | 6884 | 0.063173 | 53000 | 39000 | 70000 | 68622 | 25667 | 5144 |
22 | 23 | 2502 | ELECTRICAL ENGINEERING TECHNOLOGY | 11565.0 | 8181.0 | 3384.0 | Engineering | 0.292607 | 97 | 8587 | ... | 1873 | 5681 | 824 | 0.087557 | 52000 | 35000 | 60000 | 5126 | 2686 | 696 |
23 | 24 | 2413 | MATERIALS ENGINEERING AND MATERIALS SCIENCE | 2993.0 | 2020.0 | 973.0 | Engineering | 0.325092 | 22 | 2449 | ... | 1040 | 1151 | 70 | 0.027789 | 52000 | 35000 | 62000 | 1911 | 305 | 70 |
24 | 25 | 6212 | MANAGEMENT INFORMATION SYSTEMS AND STATISTICS | 18713.0 | 13496.0 | 5217.0 | Business | 0.278790 | 278 | 16413 | ... | 2420 | 13017 | 1015 | 0.058240 | 51000 | 38000 | 60000 | 6342 | 5741 | 708 |
25 | 26 | 2406 | CIVIL ENGINEERING | 53153.0 | 41081.0 | 12072.0 | Engineering | 0.227118 | 565 | 43041 | ... | 10080 | 29196 | 3270 | 0.070610 | 50000 | 40000 | 60000 | 28526 | 9356 | 2899 |
26 | 27 | 5601 | CONSTRUCTION SERVICES | 18498.0 | 16820.0 | 1678.0 | Industrial Arts & Consumer Services | 0.090713 | 295 | 16318 | ... | 1751 | 12313 | 1042 | 0.060023 | 50000 | 36000 | 60000 | 3275 | 5351 | 703 |
27 | 28 | 6204 | OPERATIONS LOGISTICS AND E-COMMERCE | 11732.0 | 7921.0 | 3811.0 | Business | 0.324838 | 156 | 10027 | ... | 1183 | 7724 | 504 | 0.047859 | 50000 | 40000 | 60000 | 1466 | 3629 | 285 |
28 | 29 | 2499 | MISCELLANEOUS ENGINEERING | 9133.0 | 7398.0 | 1735.0 | Engineering | 0.189970 | 118 | 7428 | ... | 1662 | 5476 | 597 | 0.074393 | 50000 | 39000 | 65000 | 3445 | 2426 | 365 |
29 | 30 | 5402 | PUBLIC POLICY | 5978.0 | 2639.0 | 3339.0 | Law & Public Policy | 0.558548 | 55 | 4547 | ... | 1306 | 2776 | 670 | 0.128426 | 50000 | 35000 | 70000 | 1550 | 1871 | 340 |
30 | 31 | 2410 | ENVIRONMENTAL ENGINEERING | 4047.0 | 2662.0 | 1385.0 | Engineering | 0.342229 | 26 | 2983 | ... | 930 | 1951 | 308 | 0.093589 | 50000 | 42000 | 56000 | 2028 | 830 | 260 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
143 | 144 | 1105 | PLANT SCIENCE AND AGRONOMY | 7416.0 | 4897.0 | 2519.0 | Agriculture & Natural Resources | 0.339671 | 110 | 6594 | ... | 1246 | 4522 | 314 | 0.045455 | 32000 | 22900 | 40000 | 2089 | 3545 | 1231 |
144 | 145 | 2308 | SCIENCE AND COMPUTER TEACHER EDUCATION | 6483.0 | 2049.0 | 4434.0 | Education | 0.683943 | 59 | 5362 | ... | 1227 | 3247 | 266 | 0.047264 | 32000 | 28000 | 39000 | 4214 | 1106 | 591 |
145 | 146 | 5200 | PSYCHOLOGY | 393735.0 | 86648.0 | 307087.0 | Psychology & Social Work | 0.779933 | 2584 | 307933 | ... | 115172 | 174438 | 28169 | 0.083811 | 31500 | 24000 | 41000 | 125148 | 141860 | 48207 |
146 | 147 | 6002 | MUSIC | 60633.0 | 29909.0 | 30724.0 | Arts | 0.506721 | 419 | 47662 | ... | 24943 | 21425 | 3918 | 0.075960 | 31000 | 22300 | 42000 | 13752 | 28786 | 9286 |
147 | 148 | 2306 | PHYSICAL AND HEALTH EDUCATION TEACHING | 28213.0 | 15670.0 | 12543.0 | Education | 0.444582 | 259 | 23794 | ... | 7230 | 13651 | 1920 | 0.074667 | 31000 | 24000 | 40000 | 12777 | 9328 | 2042 |
148 | 149 | 6006 | ART HISTORY AND CRITICISM | 21030.0 | 3240.0 | 17790.0 | Humanities & Liberal Arts | 0.845934 | 204 | 17579 | ... | 6140 | 9965 | 1128 | 0.060298 | 31000 | 23000 | 40000 | 5139 | 9738 | 3426 |
149 | 150 | 6000 | FINE ARTS | 74440.0 | 24786.0 | 49654.0 | Arts | 0.667034 | 623 | 59679 | ... | 23656 | 31877 | 5486 | 0.084186 | 30500 | 21000 | 41000 | 20792 | 32725 | 11880 |
150 | 151 | 2901 | FAMILY AND CONSUMER SCIENCES | 58001.0 | 5166.0 | 52835.0 | Industrial Arts & Consumer Services | 0.910933 | 518 | 46624 | ... | 15872 | 26906 | 3355 | 0.067128 | 30000 | 22900 | 40000 | 20985 | 20133 | 5248 |
151 | 152 | 5404 | SOCIAL WORK | 53552.0 | 5137.0 | 48415.0 | Psychology & Social Work | 0.904075 | 374 | 45038 | ... | 13481 | 27588 | 3329 | 0.068828 | 30000 | 25000 | 35000 | 27449 | 14416 | 4344 |
152 | 153 | 1103 | ANIMAL SCIENCES | 21573.0 | 5347.0 | 16226.0 | Agriculture & Natural Resources | 0.752144 | 255 | 17112 | ... | 5353 | 10824 | 917 | 0.050862 | 30000 | 22000 | 40000 | 5443 | 9571 | 2125 |
153 | 154 | 6003 | VISUAL AND PERFORMING ARTS | 16250.0 | 4133.0 | 12117.0 | Arts | 0.745662 | 132 | 12870 | ... | 6253 | 6322 | 1465 | 0.102197 | 30000 | 22000 | 40000 | 3849 | 7635 | 2840 |
154 | 155 | 2312 | TEACHER EDUCATION: MULTIPLE LEVELS | 14443.0 | 2734.0 | 11709.0 | Education | 0.810704 | 142 | 13076 | ... | 2214 | 8457 | 496 | 0.036546 | 30000 | 24000 | 37000 | 10766 | 1949 | 722 |
155 | 156 | 5299 | MISCELLANEOUS PSYCHOLOGY | 9628.0 | 1936.0 | 7692.0 | Psychology & Social Work | 0.798920 | 60 | 7653 | ... | 3221 | 3838 | 419 | 0.051908 | 30000 | 20800 | 40000 | 2960 | 3948 | 1650 |
156 | 157 | 5403 | HUMAN SERVICES AND COMMUNITY ORGANIZATION | 9374.0 | 885.0 | 8489.0 | Psychology & Social Work | 0.905590 | 89 | 8294 | ... | 2405 | 5061 | 326 | 0.037819 | 30000 | 24000 | 35000 | 2878 | 4595 | 724 |
157 | 158 | 3402 | HUMANITIES | 6652.0 | 2013.0 | 4639.0 | Humanities & Liberal Arts | 0.697384 | 49 | 5052 | ... | 2225 | 2661 | 372 | 0.068584 | 30000 | 20000 | 49000 | 1168 | 3354 | 1141 |
158 | 159 | 4901 | THEOLOGY AND RELIGIOUS VOCATIONS | 30207.0 | 18616.0 | 11591.0 | Humanities & Liberal Arts | 0.383719 | 310 | 24202 | ... | 8767 | 13944 | 1617 | 0.062628 | 29000 | 22000 | 38000 | 9927 | 12037 | 3304 |
159 | 160 | 6007 | STUDIO ARTS | 16977.0 | 4754.0 | 12223.0 | Arts | 0.719974 | 182 | 13908 | ... | 5673 | 7413 | 1368 | 0.089552 | 29000 | 19200 | 38300 | 3948 | 8707 | 3586 |
160 | 161 | 2201 | COSMETOLOGY SERVICES AND CULINARY ARTS | 10510.0 | 4364.0 | 6146.0 | Industrial Arts & Consumer Services | 0.584776 | 117 | 8650 | ... | 2064 | 5949 | 510 | 0.055677 | 29000 | 20000 | 36000 | 563 | 7384 | 3163 |
161 | 162 | 1199 | MISCELLANEOUS AGRICULTURE | 1488.0 | 404.0 | 1084.0 | Agriculture & Natural Resources | 0.728495 | 24 | 1290 | ... | 335 | 936 | 82 | 0.059767 | 29000 | 23000 | 42100 | 483 | 626 | 31 |
162 | 163 | 5502 | ANTHROPOLOGY AND ARCHEOLOGY | 38844.0 | 11376.0 | 27468.0 | Humanities & Liberal Arts | 0.707136 | 247 | 29633 | ... | 14515 | 13232 | 3395 | 0.102792 | 28000 | 20000 | 38000 | 9805 | 16693 | 6866 |
163 | 164 | 6102 | COMMUNICATION DISORDERS SCIENCES AND SERVICES | 38279.0 | 1225.0 | 37054.0 | Health | 0.967998 | 95 | 29763 | ... | 13862 | 14460 | 1487 | 0.047584 | 28000 | 20000 | 40000 | 19957 | 9404 | 5125 |
164 | 165 | 2307 | EARLY CHILDHOOD EDUCATION | 37589.0 | 1167.0 | 36422.0 | Education | 0.968954 | 342 | 32551 | ... | 7001 | 20748 | 1360 | 0.040105 | 28000 | 21000 | 35000 | 23515 | 7705 | 2868 |
165 | 166 | 2603 | OTHER FOREIGN LANGUAGES | 11204.0 | 3472.0 | 7732.0 | Humanities & Liberal Arts | 0.690111 | 56 | 7052 | ... | 3685 | 3214 | 846 | 0.107116 | 27500 | 22900 | 38000 | 2326 | 3703 | 1115 |
166 | 167 | 6001 | DRAMA AND THEATER ARTS | 43249.0 | 14440.0 | 28809.0 | Arts | 0.666119 | 357 | 36165 | ... | 15994 | 16891 | 3040 | 0.077541 | 27000 | 19200 | 35000 | 6994 | 25313 | 11068 |
167 | 168 | 3302 | COMPOSITION AND RHETORIC | 18953.0 | 7022.0 | 11931.0 | Humanities & Liberal Arts | 0.629505 | 151 | 15053 | ... | 6612 | 7832 | 1340 | 0.081742 | 27000 | 20000 | 35000 | 4855 | 8100 | 3466 |
168 | 169 | 3609 | ZOOLOGY | 8409.0 | 3050.0 | 5359.0 | Biology & Life Science | 0.637293 | 47 | 6259 | ... | 2190 | 3602 | 304 | 0.046320 | 26000 | 20000 | 39000 | 2771 | 2947 | 743 |
169 | 170 | 5201 | EDUCATIONAL PSYCHOLOGY | 2854.0 | 522.0 | 2332.0 | Psychology & Social Work | 0.817099 | 7 | 2125 | ... | 572 | 1211 | 148 | 0.065112 | 25000 | 24000 | 34000 | 1488 | 615 | 82 |
170 | 171 | 5202 | CLINICAL PSYCHOLOGY | 2838.0 | 568.0 | 2270.0 | Psychology & Social Work | 0.799859 | 13 | 2101 | ... | 648 | 1293 | 368 | 0.149048 | 25000 | 25000 | 40000 | 986 | 870 | 622 |
171 | 172 | 5203 | COUNSELING PSYCHOLOGY | 4626.0 | 931.0 | 3695.0 | Psychology & Social Work | 0.798746 | 21 | 3777 | ... | 965 | 2738 | 214 | 0.053621 | 23400 | 19200 | 26000 | 2403 | 1245 | 308 |
172 | 173 | 3501 | LIBRARY SCIENCE | 1098.0 | 134.0 | 964.0 | Education | 0.877960 | 2 | 742 | ... | 237 | 410 | 87 | 0.104946 | 22000 | 20000 | 22000 | 288 | 338 | 192 |
172 rows × 21 columns
recent_grads.plot(x='Sample_size', y='Median', kind='scatter')
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb87f6ae48>
recent_grads.plot(x='Sample_size', y ='Unemployment_rate', kind ='scatter')
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb87ef2978>
recent_grads.plot(x='Full_time', y = 'Median', kind = 'scatter')
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb87ea2860>
recent_grads.plot(x = 'ShareWomen', y = 'Unemployment_rate', kind = 'scatter')
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb85de8b70>
recent_grads.plot(x = 'Men', y = 'Median', kind = 'scatter')
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb85dcf8d0>
recent_grads.plot(x='Women', y = 'Median', kind = 'scatter')
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb85d89e48>
cols = ['Sample_size','Median','Employed','Full_time','ShareWomen','Unemployment_rate','Men','Women' ]
fig = plt.figure(figsize = (4,10))
for i in range(0,4):
ax = fig.add_subplot(5,1,i+1)
ax = recent_grads[cols[i]].plot( kind = 'hist', rot = 30)
recent_grads['Sample_size'].hist(bins =25, range = (0,5000))
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb85ab48d0>
recent_grads['Median'].hist(bins = 30, range = (0,4000))
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb859b0fd0>
recent_grads['Employed'].hist(bins = 20, range = (0,4000))
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb85997c88>
recent_grads['Full_time'].hist(bins = 18, range = (0,2500))
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb85965b00>
recent_grads['ShareWomen'].hist(bins = 20, range = (0,4000))
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb858f0e80>
recent_grads['Unemployment_rate'].hist(bins = 15, range = (0,3000))
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb856edbe0>
recent_grads['Men'].hist(bins = 20, range = (0,3000))
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb85694828>
recent_grads['Women'].hist(bins = 24, range = (0,2500))
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb8564d908>
From my point of view, there are many ways to plot and see distribution of values in histogram, one way is to use the loop techniques within the column. The other way is directly to plot using series.plot() to see the distributions as shown above.
from pandas.plotting import scatter_matrix
scatter_matrix(recent_grads[['Sample_size', 'Median']], figsize=(8,8))
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fdb8595e7f0>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb854d1828>], [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdb8549b940>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb85457128>]], dtype=object)
scatter_matrix(recent_grads[['Sample_size','Median','Unemployment_rate']], figsize = (10,10))
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fdb854930f0>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb852f2d68>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb852c37b8>], [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdb8527e470>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb852476a0>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb85207b38>], [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdb851d7080>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb8518f780>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb8515dac8>]], dtype=object)
scatter_matrix(recent_grads[['Full_time_year_round','Median']], figsize = (10,10))# Full-time Vs median salary
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fdb84fec3c8>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb84fc61d0>], [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdb84f863c8>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb84f3da20>]], dtype=object)
scatter_matrix(recent_grads[['Median', 'ShareWomen']], figsize = (10,10))
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fdb84f96f98>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb84e625f8>], [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdb84da7d30>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fdb84de49b0>]], dtype=object)
Using scatter matrix plot, it can be seen that:
recent_grads[:10].plot.bar( x= 'Major', y = 'ShareWomen', legend = False)
recent_grads[163:].plot.bar( x= 'Major', y = 'ShareWomen', legend = False)
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb84c44b38>
recent_grads[:10].plot.bar( x= 'Major', y = 'Unemployment_rate',legend = False)
recent_grads[163:].plot.bar( x= 'Major', y = 'Unemployment_rate', legend = False)
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb84b547f0>
Based on bar plots, most unemployed females studied mining & mineral and Nuclear engineering. In addition, those females who studied Actural science, library science and clinical psychology also were unemployment
recent_grads[:10].plot.bar( x= 'Major', y = 'Men',legend = False, title = 'Men')
recent_grads[:10].plot.bar( x= 'Major', y = 'Women', legend = False, title = 'Women')
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb849f4908>
Based on majors, majority of men studied mechanical, Electircal and less electrical Engineering While, majority of Female studied electrical engineering with some equal number for mechanical and electrical engineering. In general, both men and women studied engineering with few numbers in other majors
boxplot = recent_grads.boxplot(column=['Median'], rot = 90, grid = True)
boxplot = recent_grads.boxplot(column=['Unemployment_rate'], rot = 90, grid = True)
columns=[ 'Men', ' Median']
recent_grads.plot.hexbin(x= 'Men', y = 'Median', gridsize = 20, title = 'Hexagonal binning plot for Men and Median Columns')
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb847420f0>
columns=[ 'ShareWomen', ' Unemployment_rate']
recent_grads.plot.hexbin(x= 'ShareWomen', y = 'Unemployment_rate', gridsize = 20, title = 'Hexagonal binning plot for Men and Median Columns')
<matplotlib.axes._subplots.AxesSubplot at 0x7fdb845e61d0>