Introduction¶

In this project, I'll explore using the pandas plotting functionality along with the Jupyter notebook interface to explore and visualiz data

I'll be working with a dataset on the job outcomes of students who graduated from college between 2010 and 2012.

Each row in the dataset represents a different major in college and contains information on gender diversity, employment rates, median salaries, and more. Here are some of the columns in the dataset:

nk - Rank by median earnings (the dataset is ordered by this column).
Major_code - Major code.
Major - Major description.
Major_category - Category of major.
Total - Total number of people with major.
Sample_size - Sample size (unweighted) of full-time.
Men - Male graduates.
Women - Female graduates.
ShareWomen - Women as share of total.
Employed - Number employed.
Median - Median salary of full-time, year-round workers.
Low_wage_jobs - Number in low-wage service jobs.
Full_time - Number employed 35 hours or more.
Part_time - Number employed less than 35 hours.

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

recent_grads = pd.read_csv("recent-grads.csv")

# first row formatted as a table.
print (recent_grads.iloc[0])

Warning: Cannot change to a different GUI toolkit: notebook. Using tk instead.
Rank                                        1
Major_code                               2419
Major                   PETROLEUM ENGINEERING
Total                                    2339
Men                                      2057
Women                                     282
Major_category                    Engineering
ShareWomen                           0.120564
Sample_size                                36
Employed                                 1976
Full_time                                1849
Part_time                                 270
Full_time_year_round                     1207
Unemployed                                 37
Unemployment_rate                   0.0183805
Median                                 110000
P25th                                   95000
P75th                                  125000
College_jobs                             1534
Non_college_jobs                          364
Low_wage_jobs                             193
Name: 0, dtype: object

# to become familiar with how data is structured
print (recent_grads.head())
print (recent_grads.tail())

   Rank  Major_code                                      Major    Total  \
0     1        2419                      PETROLEUM ENGINEERING   2339.0   
1     2        2416             MINING AND MINERAL ENGINEERING    756.0   
2     3        2415                  METALLURGICAL ENGINEERING    856.0   
3     4        2417  NAVAL ARCHITECTURE AND MARINE ENGINEERING   1258.0   
4     5        2405                       CHEMICAL ENGINEERING  32260.0   

       Men    Women Major_category  ShareWomen  Sample_size  Employed  ...  \
0   2057.0    282.0    Engineering    0.120564           36      1976  ...   
1    679.0     77.0    Engineering    0.101852            7       640  ...   
2    725.0    131.0    Engineering    0.153037            3       648  ...   
3   1123.0    135.0    Engineering    0.107313           16       758  ...   
4  21239.0  11021.0    Engineering    0.341631          289     25694  ...   

   Part_time  Full_time_year_round  Unemployed  Unemployment_rate  Median  \
0        270                  1207          37           0.018381  110000   
1        170                   388          85           0.117241   75000   
2        133                   340          16           0.024096   73000   
3        150                   692          40           0.050125   70000   
4       5180                 16697        1672           0.061098   65000   

   P25th   P75th  College_jobs  Non_college_jobs  Low_wage_jobs  
0  95000  125000          1534               364            193  
1  55000   90000           350               257             50  
2  50000  105000           456               176              0  
3  43000   80000           529               102              0  
4  50000   75000         18314              4440            972  

[5 rows x 21 columns]
     Rank  Major_code                   Major   Total     Men   Women  \
168   169        3609                 ZOOLOGY  8409.0  3050.0  5359.0   
169   170        5201  EDUCATIONAL PSYCHOLOGY  2854.0   522.0  2332.0   
170   171        5202     CLINICAL PSYCHOLOGY  2838.0   568.0  2270.0   
171   172        5203   COUNSELING PSYCHOLOGY  4626.0   931.0  3695.0   
172   173        3501         LIBRARY SCIENCE  1098.0   134.0   964.0   

               Major_category  ShareWomen  Sample_size  Employed  ...  \
168    Biology & Life Science    0.637293           47      6259  ...   
169  Psychology & Social Work    0.817099            7      2125  ...   
170  Psychology & Social Work    0.799859           13      2101  ...   
171  Psychology & Social Work    0.798746           21      3777  ...   
172                 Education    0.877960            2       742  ...   

     Part_time  Full_time_year_round  Unemployed  Unemployment_rate  Median  \
168       2190                  3602         304           0.046320   26000   
169        572                  1211         148           0.065112   25000   
170        648                  1293         368           0.149048   25000   
171        965                  2738         214           0.053621   23400   
172        237                   410          87           0.104946   22000   

     P25th  P75th  College_jobs  Non_college_jobs  Low_wage_jobs  
168  20000  39000          2771              2947            743  
169  24000  34000          1488               615             82  
170  25000  40000           986               870            622  
171  19200  26000          2403              1245            308  
172  20000  22000           288               338            192  

[5 rows x 21 columns]

#  to generate summary statistics for all of the numeric columns
recent_grads.describe()

Investigating Missing Values¶

# Look up the number of rows
raw_data_count = recent_grads.shape[0]
print (raw_data_count)

# Drop rows with missing values
recent_grads = recent_grads.dropna(axis=0, inplace=True)

# Look up the number of rows to ascertain if data has been droped
cleaned_data_count = recent_grads.shape[0]
print (cleaned_data_count)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-2-623ba833088f> in <module>
      1 # Look up the number of rows
----> 2 raw_data_count = recent_grads.shape[0]
      3 print (raw_data_count)
      4 
      5 # Drop rows with missing values

NameError: name 'recent_grads' is not defined

Comparing the raw data and cleaned data, it will be observed that number of rows droped to 172 in cleaned data. While raw data has 173 rows. This means a row has been removed for having missing value.

Scatter Plot¶

recent_grads.plot(x="Sample_size", y="Median", kind = "scatter", title = "Sample_size VS Median")
recent_grads.plot(x="Sample_size", y="Unemployment_rate", kind = "scatter", title = "Sample_size VS Uemployemny")
recent_grads.plot(x="Full_time", y="Median", kind = "scatter", title = "Full_time VS Median")
recent_grads.plot(x="ShareWomen", y="Unemployment_rate", kind = "scatter", title = "Sharewoman VS Unemployment_rate")
recent_grads.plot(x="Men",y="Median", kind = "scatter", title = "Men VS Median")
recent_grads.plot(x="Women",y="Median", kind = "scatter", title = "Sample_size VS Median")

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-18-6d7d435b7c0f> in <module>
----> 1 recent_grads.plot(x="Sample_size", y="Median", kind = "scatter", title = "Sample_size VS Median")
      2 recent_grads.plot(x="Sample_size", y="Unemployment_rate", kind = "scatter", title = "Sample_size VS Uemployemny")
      3 recent_grads.plot(x="Full_time", y="Median", kind = "scatter", title = "Full_time VS Median")
      4 recent_grads.plot(x="ShareWomen", y="Unemployment_rate", kind = "scatter", title = "Sharewoman VS Unemployment_rate")
      5 recent_grads.plot(x="Men",y="Median", kind = "scatter", title = "Men VS Median")

AttributeError: 'NoneType' object has no attribute 'plot'

	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