Introduction

we'll explore how using the pandas plotting functionality to analyse dataset on the job outcomes of students who graduated from college between 2010 and 2012. We will first read recent-grads.csv into pandas and assign the resulting DataFrame to recent_grads. Then use recent_grads.iloc[] to return the first row formatted as a table.

In [55]:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
recent_grads = pd.read_csv('recent-grads.csv')
print(recent_grads.iloc[0])

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

We will have to use recent_grads.head() and recent_grads.tail() to become familiar with how the data is structured

In [29]:
recent_grads.head()
Out[29]:
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 [30]:
recent_grads.tail()
Out[30]:
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

We use recent_grads.describe()To generate a summary statistics of all the numeric columns

In [31]:
recent_grads.describe()
Out[31]:
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 clean up the dataset, we have to drop rows with missing values. Because columns of values we pass in with matching lengths and missing values will cause matplotlib to throw errors.

In [32]:
raw_data_count = 173

Use recent_grads.dropna() to drop rows containing missing values and assign the resulting DataFrame back to recent_grads

In [33]:
recent_grads = recent_grads.dropna()
In [34]:
recent_grads.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 172 entries, 0 to 172
Data columns (total 21 columns):
Rank                    172 non-null int64
Major_code              172 non-null int64
Major                   172 non-null object
Total                   172 non-null float64
Men                     172 non-null float64
Women                   172 non-null float64
Major_category          172 non-null object
ShareWomen              172 non-null float64
Sample_size             172 non-null int64
Employed                172 non-null int64
Full_time               172 non-null int64
Part_time               172 non-null int64
Full_time_year_round    172 non-null int64
Unemployed              172 non-null int64
Unemployment_rate       172 non-null float64
Median                  172 non-null int64
P25th                   172 non-null int64
P75th                   172 non-null int64
College_jobs            172 non-null int64
Non_college_jobs        172 non-null int64
Low_wage_jobs           172 non-null int64
dtypes: float64(5), int64(14), object(2)
memory usage: 29.6+ KB
In [35]:
cleaned_data_count = 172

If you compare cleaned_data_count and raw_data_count, you'll notice that only one row contained missing values and was dropped.

Pandas, Scatter Plots

We will generate scatter plots to explore the following relations:

Sample_size and Median
Sample_size and Unemployment_rate
Full_time and Median
ShareWomen and Unemployment_rate
Men and Median
Women and Median
In [36]:
recent_grads.plot(x='Sample_size', y='Median', kind='scatter', title='Median vs. Sample_size')
Out[36]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f174b691668>
In [37]:
recent_grads.plot(x='Sample_size', y='Unemployment_rate', kind='scatter', title='Unemployment_rate vs. Sample_size')
Out[37]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f174b698f60>
In [38]:
recent_grads.plot(x='Full_time', y='Median', kind='scatter', title='Median vs. Full_time')
Out[38]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f174b5f6e80>
In [39]:
recent_grads.plot(x='ShareWomen', y='Unemployment_rate', kind='scatter', title='Unemployment_rate vs. ShareWomen')
Out[39]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f174b693b38>
In [40]:
recent_grads.plot(x='Men', y='Median', kind='scatter', title='Median vs. Men')
Out[40]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f174b65d240>
In [41]:
recent_grads.plot(x= 'Women', y='Median' ,kind= 'scatter', title= 'Median vs Women')
Out[41]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f174b51df28>

To explore the distribution of values in a column, we will generate histograms to explore the distributions of the following columns:

Sample_size
Median
Employed
Full_time
ShareWomen
Unemployment_rate
Men
Women
In [42]:
recent_grads['Sample_size'].hist(bins=20, range=(0,5000)).set_title('Distribution of Sample_size')
Out[42]:
<matplotlib.text.Text at 0x7f174b3f0160>
In [43]:
recent_grads['Median'].hist(bins=20, range=(20000,120000)).set_title('Distribution of Median')
Out[43]:
<matplotlib.text.Text at 0x7f174b397908>
In [44]:
recent_grads['Employed'].hist(bins=25, range=(0,300000)).set_title('Distribution of Employed')
Out[44]:
<matplotlib.text.Text at 0x7f174b2b6518>
In [45]:
recent_grads['Full_time'].hist(bins=25, range=(0,30000)).set_title('Distribution of Full_time')
Out[45]:
<matplotlib.text.Text at 0x7f174b1f8b70>
In [46]:
recent_grads['ShareWomen'].hist(bins=20, range=(0,3000)).set_title('Distribution of ShareWomen')
Out[46]:
<matplotlib.text.Text at 0x7f174b1309e8>
In [47]:
recent_grads['Unemployment_rate'].hist(bins=20, range=(0,3000)).set_title('Distribution of Unemployment_rate')
Out[47]:
<matplotlib.text.Text at 0x7f174b0e4da0>
In [48]:
recent_grads['Men'].hist(bins=20, range=(0,20000)).set_title('Distribution of Men')
Out[48]:
<matplotlib.text.Text at 0x7f174b01a4a8>
In [49]:
recent_grads['Women'].hist(bins=20, range=(0,20000)).set_title('Distribution of Women')
Out[49]:
<matplotlib.text.Text at 0x7f174af34ba8>

We will create a scatter matrix plot to enable us explore potential relationships and distributions simultaneously using scatter_matrix() function. First we have to inport the function from pandas.plotting module

In [50]:
from pandas.plotting import scatter_matrix
scatter_matrix(recent_grads[['Sample_size','Median']], figsize=(8,8))
Out[50]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f174b0aa4e0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f174ae83438>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f174ae4d748>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f174ae02dd8>]],
      dtype=object)
In [51]:
scatter_matrix(recent_grads[['Sample_size','Median', 'Unemployment_rate']], figsize=(12,12))
Out[51]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f174ad29cc0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f174ad235f8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f174ac730f0>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f174ac90048>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f174ac596a0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f174ac169e8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f174abdf908>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f174ab6e828>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f174aaee320>]],
      dtype=object)

Pandas, Bar Plots

In [53]:
recent_grads[:10].plot.bar(x='Major', y='ShareWomen')
recent_grads[-10:].plot.bar(x='Major', y='ShareWomen')
Out[53]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f174ac7df60>
In [54]:
recent_grads[:10].plot.bar(x='Major', y='Unemployment_rate')
recent_grads[-10:].plot.bar(x='Major', y='Unemployment_rate')
Out[54]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f1748bb9048>
In [ ]: