In [22]:
import pandas as pd
import matplotlib.pyplot as plt
print('done')
done
In [23]:
%matplotlib inline
In [24]:
recent_grads=pd.read_csv('recent-grads.csv')
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  \
0      ...              270                  1207          37   
1      ...              170                   388          85   
2      ...              133                   340          16   
3      ...              150                   692          40   
4      ...             5180                 16697        1672   

   Unemployment_rate  Median  P25th   P75th  College_jobs  Non_college_jobs  \
0           0.018381  110000  95000  125000          1534               364   
1           0.117241   75000  55000   90000           350               257   
2           0.024096   73000  50000  105000           456               176   
3           0.050125   70000  43000   80000           529               102   
4           0.061098   65000  50000   75000         18314              4440   

   Low_wage_jobs  
0            193  
1             50  
2              0  
3              0  
4            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  \
168      ...             2190                  3602         304   
169      ...              572                  1211         148   
170      ...              648                  1293         368   
171      ...              965                  2738         214   
172      ...              237                   410          87   

     Unemployment_rate  Median  P25th  P75th  College_jobs  Non_college_jobs  \
168           0.046320   26000  20000  39000          2771              2947   
169           0.065112   25000  24000  34000          1488               615   
170           0.149048   25000  25000  40000           986               870   
171           0.053621   23400  19200  26000          2403              1245   
172           0.104946   22000  20000  22000           288               338   

     Low_wage_jobs  
168            743  
169             82  
170            622  
171            308  
172            192  

[5 rows x 21 columns]
In [25]:
recent_grads.iloc[0]
Out[25]:
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
In [52]:
recent_grads['Unemployment_rate']=recent_grads['Unemployment_rate'].astype(int)
In [56]:
recent_grads['Unemployment_rate'].value_counts()
Out[56]:
0    172
Name: Unemployment_rate, dtype: int64
In [26]:
recent_grads.describe()
Out[26]:
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 [27]:
recent_grads=recent_grads.dropna()
recent_grads.describe().count()
Out[27]:
Rank                    8
Major_code              8
Total                   8
Men                     8
Women                   8
ShareWomen              8
Sample_size             8
Employed                8
Full_time               8
Part_time               8
Full_time_year_round    8
Unemployed              8
Unemployment_rate       8
Median                  8
P25th                   8
P75th                   8
College_jobs            8
Non_college_jobs        8
Low_wage_jobs           8
dtype: int64
In [28]:
cleaned_data_count=recent_grads.shape[0]
print('Rows: ',cleaned_data_count)
Rows:  172
In [29]:
recent_grads.plot(x='Sample_size',y='Median', kind='scatter')
recent_grads.plot(x='Sample_size',y='Unemployment_rate', kind='scatter')
recent_grads.plot(x='Full_time',y='Median', kind='scatter')
recent_grads.plot(x='ShareWomen',y='Unemployment_rate', kind='scatter')
recent_grads.plot(x='Men',y='Median', kind='scatter')
recent_grads.plot(x='Women',y='Median', kind='scatter')
Out[29]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f670bb1bbe0>
In [30]:
recent_grads['Sample_size'].hist(bins=20, range=(0,1500))
Out[30]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f670bca8f98>
In [31]:
recent_grads['Sample_size'].hist(bins=15, range=(0,3000))
Out[31]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f670b9d1c18>
In [32]:
recent_grads['Median'].hist()
Out[32]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f670b9a37f0>
In [33]:
recent_grads['Employed'].hist()
Out[33]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f670bc29780>
In [34]:
recent_grads['Full_time'].hist(bins=35, range=(0,150000))
Out[34]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f670ba394a8>
In [35]:
recent_grads['ShareWomen'].hist()
Out[35]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f670bc61390>
In [36]:
recent_grads['Unemployment_rate'].hist()
Out[36]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f670bc18780>
In [37]:
recent_grads['Men'].hist()
Out[37]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f670b8321d0>
In [38]:
recent_grads['Women'].hist(bins=25, range=(0,5000))
Out[38]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f670b7c47f0>
In [39]:
from pandas.plotting import scatter_matrix
In [40]:
scatter_matrix(recent_grads[['Sample_size','Median']], figsize=(4,4))
Out[40]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f670b80c978>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f670b6386d8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f670b5ff908>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f670b5b7d30>]],
      dtype=object)
In [41]:
scatter_matrix(recent_grads[['Sample_size','Median','Unemployment_rate']], figsize=(6,6))
Out[41]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f670b55d2e8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f670b4d0710>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f670b49e0b8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f670b453d68>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f670b41fe80>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f670b3e4470>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f670b32eb70>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f670b2ee2b0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f670b2b75f8>]],
      dtype=object)
In [49]:
#recent_grads[:10]["ShareWomen"].plot(kind='bar')
recent_grads[:-10]["ShareWomen"].plot(kind='bar')
Out[49]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f670aa40b00>
In [58]:
recent_grads[:10].plot.bar(x='Unemployment_rate', y='ShareWomen', legend=False)
recent_grads[163:].plot.bar(x='Unemployment_rate', y='ShareWomen',legend=False)
Out[58]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f6709909898>
In [ ]: