This notebook is created as a guided project in "Exploratory Data Visualization" course on DataQuest.io to visualize earnings based on college majors.
Data set can be downloaded from here

In [1]:
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix 
%matplotlib inline
In [2]:
recent_grads = pd.read_csv('recent-grads.csv')
recent_grads.head()
Out[2]:
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

In [3]:
recent_grads.describe()
Out[3]:
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
In [4]:
recent_grads.shape
Out[4]:
(173, 21)
In [5]:
recent_grads.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 173 entries, 0 to 172
Data columns (total 21 columns):
Rank                    173 non-null int64
Major_code              173 non-null int64
Major                   173 non-null object
Total                   172 non-null float64
Men                     172 non-null float64
Women                   172 non-null float64
Major_category          173 non-null object
ShareWomen              172 non-null float64
Sample_size             173 non-null int64
Employed                173 non-null int64
Full_time               173 non-null int64
Part_time               173 non-null int64
Full_time_year_round    173 non-null int64
Unemployed              173 non-null int64
Unemployment_rate       173 non-null float64
Median                  173 non-null int64
P25th                   173 non-null int64
P75th                   173 non-null int64
College_jobs            173 non-null int64
Non_college_jobs        173 non-null int64
Low_wage_jobs           173 non-null int64
dtypes: float64(5), int64(14), object(2)
memory usage: 28.5+ KB

We see that some of the columns have 172 values, we drop missing values so that all columns have equal number of values.

Dropping missing values

In [6]:
recent_grads.dropna(inplace=True)
recent_grads.shape
Out[6]:
(172, 21)
In [7]:
len(recent_grads['Major_category'].unique())
Out[7]:
16

There are 16 unique major categories in data set. For each category, we will compare:

  • the number of male and female
  • the number of employed and unemployed
  • the number of collge jobs, non college jobs and low wage jobs
  • the median earning
In [8]:
recent_grads.groupby('Major_category')['Men', 'Women'].sum().plot(kind='bar')
Out[8]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8f53a3def0>

We see that there is a significant gender gap in Education, Engineering, Health and Psychology & Social Work.

In [9]:
recent_grads.groupby('Major_category')['Employed', 'Unemployed'].sum().plot(kind='bar')
Out[9]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8f518db4a8>

0 unemployment in Agriculture & Natural Resources.

In [10]:
recent_grads.groupby('Major_category')['College_jobs','Non_college_jobs',
       'Low_wage_jobs'].sum().plot(kind='bar')
Out[10]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8f517ede10>
In [12]:
recent_grads.groupby('Major_category')['Median'].sum().plot(kind='bar')
Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8f51644128>

Engineering has highest Median salary while Business has second highest.

In [17]:
recent_grads[recent_grads['Major_category']=='Engineering']['Median']
Out[17]:
0     110000
1      75000
2      73000
3      70000
4      65000
5      65000
8      60000
9      60000
10     60000
11     60000
12     60000
13     60000
14     58000
15     57100
16     57000
17     56000
18     54000
22     52000
23     52000
25     50000
28     50000
30     50000
31     50000
33     50000
38     46000
50     44000
58     40000
65     40000
66     40000
Name: Median, dtype: int64
In [11]:
recent_grads['Median'].hist(bins=20, range=(recent_grads['Median'].min(),recent_grads['Median'].max()))
Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8f5169ad30>

30k - 35k is most common salary figure for Engineering students.

In [29]:
scatter_matrix(recent_grads[['Sample_size', 'Median']], figsize=(10,10))
Out[29]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9b100f5978>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b10000f98>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9b0ff50400>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b0ff84f98>]],
      dtype=object)
In [ ]: