
import pandas as pd


df = pd.read_csv('salaries_by_college_major.csv')


df.head()


df.shape

(51, 6)


df.columns

Index(['Undergraduate Major', 'Starting Median Salary',
       'Mid-Career Median Salary', 'Mid-Career 10th Percentile Salary',
       'Mid-Career 90th Percentile Salary', 'Group'],
      dtype='object')


df.isna()


df.tail()


clean_df = df.dropna()
clean_df.tail()


clean_df['Starting Median Salary'].max()

74300.0


clean_df['Starting Median Salary'].idxmax()

43


clean_df['Starting Median Salary'][43]

74300.0


clean_df['Undergraduate Major'].loc[43]

'Physician Assistant'


clean_df['Undergraduate Major'][43]

'Physician Assistant'


clean_df.loc[43]

Undergraduate Major                  Physician Assistant
Starting Median Salary                           74300.0
Mid-Career Median Salary                         91700.0
Mid-Career 10th Percentile Salary                66400.0
Mid-Career 90th Percentile Salary               124000.0
Group                                               STEM
Name: 43, dtype: object


print(clean_df['Mid-Career Median Salary'].max())
print(f"Index for the max mid career salary: {clean_df['Mid-Career Median Salary'].idxmax()}")
clean_df['Undergraduate Major'][8]

107000.0
Index for the max mid career salary: 8

'Chemical Engineering'


print(clean_df['Starting Median Salary'].min())
clean_df['Undergraduate Major'].loc[clean_df['Starting Median Salary'].idxmin()]

34000.0

'Spanish'


clean_df.loc[clean_df['Mid-Career Median Salary'].idxmin()]

Undergraduate Major                  Education
Starting Median Salary                 34900.0
Mid-Career Median Salary               52000.0
Mid-Career 10th Percentile Salary      29300.0
Mid-Career 90th Percentile Salary     102000.0
Group                                     HASS
Name: 18, dtype: object


# clean_df['Mid-Career 90th Percentile Salary'] - clean_df['Mid-Career 10th Percentile Salary']
# clean_df['Mid-Career 90th Percentile Salary'].subtract(clean_df['Mid-Career 10th Percentile Salary'])
spread_col = clean_df['Mid-Career 90th Percentile Salary'] - clean_df['Mid-Career 10th Percentile Salary']
clean_df.insert(1, 'Spread', spread_col)
clean_df.head()


low_risk = clean_df.sort_values('Spread')
low_risk[['Undergraduate Major', 'Spread']].head()


highest_potential = clean_df.sort_values('Mid-Career 90th Percentile Salary', ascending=False)
highest_potential[['Undergraduate Major', 'Mid-Career 90th Percentile Salary']].head()


highest_spread = clean_df.sort_values('Spread', ascending=False)
highest_spread[['Undergraduate Major', 'Spread']].head()


highest_spread = clean_df.sort_values('Mid-Career Median Salary', ascending=False)
highest_spread[['Undergraduate Major', 'Mid-Career Median Salary']].head()


clean_df.groupby('Group').count()


pd.options.display.float_format = '{:,.2f}'.format
clean_df.groupby('Group').mean()

	Undergraduate Major	Starting Median Salary	Mid-Career Median Salary	Mid-Career 10th Percentile Salary	Mid-Career 90th Percentile Salary	Group
0	Accounting	46000.0	77100.0	42200.0	152000.0	Business
1	Aerospace Engineering	57700.0	101000.0	64300.0	161000.0	STEM
2	Agriculture	42600.0	71900.0	36300.0	150000.0	Business
3	Anthropology	36800.0	61500.0	33800.0	138000.0	HASS
4	Architecture	41600.0	76800.0	50600.0	136000.0	Business

	Undergraduate Major	Starting Median Salary	Mid-Career Median Salary	Mid-Career 10th Percentile Salary	Mid-Career 90th Percentile Salary	Group
46	Psychology	35900.0	60400.0	31600.0	127000.0	HASS
47	Religion	34100.0	52000.0	29700.0	96400.0	HASS
48	Sociology	36500.0	58200.0	30700.0	118000.0	HASS
49	Spanish	34000.0	53100.0	31000.0	96400.0	HASS
50	Source: PayScale Inc.	NaN	NaN	NaN	NaN	NaN

	Undergraduate Major	Starting Median Salary	Mid-Career Median Salary	Mid-Career 10th Percentile Salary	Mid-Career 90th Percentile Salary	Group
45	Political Science	40800.0	78200.0	41200.0	168000.0	HASS
46	Psychology	35900.0	60400.0	31600.0	127000.0	HASS
47	Religion	34100.0	52000.0	29700.0	96400.0	HASS
48	Sociology	36500.0	58200.0	30700.0	118000.0	HASS
49	Spanish	34000.0	53100.0	31000.0	96400.0	HASS

	Undergraduate Major	Spread	Starting Median Salary	Mid-Career Median Salary	Mid-Career 10th Percentile Salary	Mid-Career 90th Percentile Salary	Group
0	Accounting	109800.0	46000.0	77100.0	42200.0	152000.0	Business
1	Aerospace Engineering	96700.0	57700.0	101000.0	64300.0	161000.0	STEM
2	Agriculture	113700.0	42600.0	71900.0	36300.0	150000.0	Business
3	Anthropology	104200.0	36800.0	61500.0	33800.0	138000.0	HASS
4	Architecture	85400.0	41600.0	76800.0	50600.0	136000.0	Business

	Undergraduate Major	Spread
40	Nursing	50700.0
43	Physician Assistant	57600.0
41	Nutrition	65300.0
49	Spanish	65400.0
27	Health Care Administration	66400.0

College Major and Salary Analysis¶

Upload the Data and Read the .csv File¶

Data Exploration and Data Cleaning¶

Accessing Columns and Individual Cells¶

Solution: Highest and Lowest Earning Degrees¶

Sorting Values & Adding Columns: Majors with the Most Potential vs Lowest Risk¶

Solution: Degrees with the Highest Potential¶

Grouping and Pivoting Data with Pandas¶

	Undergraduate Major	Starting Median Salary	Mid-Career Median Salary	Mid-Career 10th Percentile Salary	Mid-Career 90th Percentile Salary	Group
0	False	False	False	False	False	False
1	False	False	False	False	False	False
2	False	False	False	False	False	False
3	False	False	False	False	False	False
4	False	False	False	False	False	False
5	False	False	False	False	False	False
6	False	False	False	False	False	False
7	False	False	False	False	False	False
8	False	False	False	False	False	False
9	False	False	False	False	False	False
10	False	False	False	False	False	False
11	False	False	False	False	False	False
12	False	False	False	False	False	False
13	False	False	False	False	False	False
14	False	False	False	False	False	False
15	False	False	False	False	False	False
16	False	False	False	False	False	False
17	False	False	False	False	False	False
18	False	False	False	False	False	False
19	False	False	False	False	False	False
20	False	False	False	False	False	False
21	False	False	False	False	False	False
22	False	False	False	False	False	False
23	False	False	False	False	False	False
24	False	False	False	False	False	False
25	False	False	False	False	False	False
26	False	False	False	False	False	False
27	False	False	False	False	False	False
28	False	False	False	False	False	False
29	False	False	False	False	False	False
30	False	False	False	False	False	False
31	False	False	False	False	False	False
32	False	False	False	False	False	False
33	False	False	False	False	False	False
34	False	False	False	False	False	False
35	False	False	False	False	False	False
36	False	False	False	False	False	False
37	False	False	False	False	False	False
38	False	False	False	False	False	False
39	False	False	False	False	False	False
40	False	False	False	False	False	False
41	False	False	False	False	False	False
42	False	False	False	False	False	False
43	False	False	False	False	False	False
44	False	False	False	False	False	False
45	False	False	False	False	False	False
46	False	False	False	False	False	False
47	False	False	False	False	False	False
48	False	False	False	False	False	False
49	False	False	False	False	False	False
50	False	True	True	True	True	True

	Undergraduate Major	Mid-Career 90th Percentile Salary
17	Economics	210000.0
22	Finance	195000.0
8	Chemical Engineering	194000.0
37	Math	183000.0
44	Physics	178000.0

	Undergraduate Major	Spread
17	Economics	159400.0
22	Finance	147800.0
37	Math	137800.0
36	Marketing	132900.0
42	Philosophy	132500.0

	Undergraduate Major	Mid-Career Median Salary
8	Chemical Engineering	107000.0
12	Computer Engineering	105000.0
19	Electrical Engineering	103000.0
1	Aerospace Engineering	101000.0
17	Economics	98600.0

	Spread	Starting Median Salary	Mid-Career Median Salary	Mid-Career 10th Percentile Salary	Mid-Career 90th Percentile Salary
Group
Business	103,958.33	44,633.33	75,083.33	43,566.67	147,525.00
HASS	95,218.18	37,186.36	62,968.18	34,145.45	129,363.64
STEM	101,600.00	53,862.50	90,812.50	56,025.00	157,625.00