In this guided project, we'll explore how using the pandas plotting functionality with some standard visualizations.

We'll be working with a dataset on the job outcomes of students who graduated from college between 2010 and 2012. The original data on job outcomes was released by American Community Survey, which conducts surveys and aggregates the data. FiveThirtyEight cleaned the dataset and released it on their Github repo.

The name of the file is recent-grads.csv. 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:

  • Rank - 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.

Using visualizations, we can start to explore questions from the dataset like:

  1. Do students in more popular majors make more money?

    • Using scatter plots
  2. How many majors are predominantly male? Predominantly female?

    • Using histograms
  3. Which category of majors have the most students?
    • Using bar plots

We'll explore how to do these and more while primarily working in pandas. Before we start creating data visualizations, let's import the libraries we need and remove rows containing null values.

In [ ]:
import pandas as pd
import matplotlib.pyplot as plt

recent_grads = pd.read_csv('recent-grads.csv')
recent_grads.head()
Out[ ]:
Rank Major_code Major Total Men Women Major_category 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
0 1 2419 PETROLEUM ENGINEERING 2339.0 2057.0 282.0 Engineering 0.120564 36 1976 1849 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 7 640 556 170 388 85 0.117241 75000 55000 90000 350 257 50
2 3 2415 METALLURGICAL ENGINEERING 856.0 725.0 131.0 Engineering 0.153037 3 648 558 133 340 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 1069 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 23170 5180 16697 1672 0.061098 65000 50000 75000 18314 4440 972
In [ ]:
recent_grads.tail()
Out[ ]:
Rank Major_code Major Total Men Women Major_category 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
168 169 3609 ZOOLOGY 8409.0 3050.0 5359.0 Biology & Life Science 0.637293 47 6259 5043 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 1848 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 1724 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 3154 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 2 742 593 237 410 87 0.104946 22000 20000 22000 288 338 192
In [ ]:
recent_grads.describe()
Out[ ]:
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 [ ]:
raw_data_count = recent_grads.shape
raw_data_count
Out[ ]:
(173, 21)

There are currently 173 rows with 21 columns in it.

Looking into the values we will drop rows with missing values. Matplotlib expects that columns of values we pass in have matching lengths and missing values will cause matplotlib to throw errors.

In [ ]:
recent_grads = recent_grads.dropna()

cleaned_data_count = recent_grads.shape
cleaned_data_count
Out[ ]:
(172, 21)

After dropping null values we can see only one row was dropped which means only one row contained missing values.

Now, we will work with scatter plots to visualize this cleaned data.

With scatter plot we will explore the following questions:

  • Do students in more popular majors make more money?
  • Do students that majored in subjects that were majority female make more money?
  • Is there any link between the number of full-time employees and median salary?

For this, we will 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 [ ]:
# Scatter plot showing Sample size vs Median
recent_grads.plot(x='Sample_size', y='Median', kind='scatter', title='Sample Size vs. Median', figsize=(5,8))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bc890518>
In [ ]:
# Scatter plot showing Sample size vs Unemployment rate
recent_grads.plot(x='Sample_size', y='Unemployment_rate', kind='scatter', title='Sample Size vs. Unemployment rate', figsize=(5,8))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bc7d6b38>
In [ ]:
# Scatter plot showing Full time vs Median
recent_grads.plot(x='Full_time', y='Median', kind='scatter', title='Full Time vs. Median', figsize=(6,8))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bc300860>
In [ ]:
# Scatter plot showing Share Women vs Unemployment rate
recent_grads.plot(x='ShareWomen', y='Unemployment_rate', kind='scatter', title='Share Women vs. Unemployment_rate', figsize=(5,8))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bc2d9b00>
In [ ]:
# Scatter plot showing Men vs Median
recent_grads.plot(x='Men', y='Median', kind='scatter', title='Men vs. Median', figsize=(6,8))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bc292b00>
In [ ]:
# Scatter plot showing Women vs Median
recent_grads.plot(x='Women', y='Median', kind='scatter', title='Women vs. Median', figsize=(5,8))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bc1ac908>

Conclusions:

Do students in more popular majors make more money?

Full Time vs Median scatterplot does not show any strong trends that full-time workers with a popular major have a higher median income than other FT workers with unpopular majors.

Additionally, the Total vs Median scatterplot also does not indicate any significant bias towards having a higher median income for popular majors.

Do students that majored in subjects that were majority female make more money?

Share of Women vs Median scatterplot shows a downward trend indicating that a major with a larger percentage of women means less median income. Could this mean women are more prone to making less money in the majors that are popular among them?

Is there any link between the number of full-time employees and median salary?

Full Time vs Median scatterplot does not show any correlation between the two datasets.

Now, we would hop on to histogram for exploring the distribution of values in a column.

  • What percent of majors are predominantly male? Predominantly female?
  • What's the most common median salary range?
In [ ]:
recent_grads['Sample_size'].plot(kind='hist')
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bc16e550>

In the above Sample Size histogram, one can see that the majority of the sample sizes for the different majors are under a 1,000 count.

In [ ]:
recent_grads['Sample_size'].hist(bins=25, range=(0,5000))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bc113be0>

It looks like the ranges between 25K and 50K are most common. Let's zoom in further on this part.

In [ ]:
recent_grads['Median'].hist(bins=30, range=(20000,110000))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bc029e80>
In [ ]:
recent_grads['Full_time'].hist(bins=10, range=(0,250000))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bbf6d978>

It looks like very common (median) salaries are 35-36K and 40-41K.

Let's looking further in the relations between (1)total number of majors (2)the median salary (3) percentage of women by creating a scatter-matrix for these three.

In [ ]:
recent_grads['ShareWomen'].hist(bins=15,range=(0.0,1.1))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bbef2eb8>
In [ ]:
recent_grads['Unemployment_rate'].hist(bins=30,range=(0.000,0.180))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bbf12438>
In [ ]:
recent_grads['Men'].hist(bins=20, range=(0,175000))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bbf34908>
In [ ]:
recent_grads['Women'].hist(bins=20, range=(0,300000))
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62bbdfdc88>

What percent of majors are predominantly male? Predominantly female?

There are a total of 172 majors. Looking at the histogram above for column 'ShareWomen', it's clear that more than 50% of majors had predominantly more women than men. By applying a Boolean filter for all majors that had greater than 50% of women, we found out there are a total of 96 female-dominated majors. On the other hand, there are 76 majors that have predominantly men.

Scatter Matrix plot

In [ ]:
from pandas.plotting import scatter_matrix

scatter_matrix(recent_grads[['Sample_size', 'Median']], figsize=(10,10))
Out[ ]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f62bc141208>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f62bbf3bd68>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f62bbd34f60>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f62bbc52550>]],
      dtype=object)

For the above Median vs Sample Size scatter matrix, there is no noticeable correlations between the two columns. Both separate histograms are skewed to the left, meaning most of the sample sizes were lower than 1,000 and most of the average median incomes were below 40,000 USD.

In [ ]:
scatter_matrix(recent_grads[['Sample_size', 'Median', 'Unemployment_rate']], figsize=(10,10))
Out[ ]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f62bbb7edd8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f62bbb096d8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f62bbabd940>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f62bba70ba8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f62bba25e10>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f62bb9e60b8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f62bba19320>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f62bb9cc550>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f62bb9cc5c0>]],
      dtype=object)

In the scatter matrix plots above, there was no obvious trends or correlation among the three columns.

Bar plot

Now let's also create bar-plots for the first 10 and for the last 10 majors in the list to see what we can learn from that.

In [ ]:
recent_grads[:10].plot.barh(x='Major', y='ShareWomen', legend=False, color=['black', 'red', 'green', 'blue', 'cyan','black', 'red', 'green', 'blue', 'cyan'])
recent_grads[-10:].plot.barh(x='Major', y='ShareWomen', legend=False, color=['black', 'red', 'green', 'blue', 'cyan','black', 'red', 'green', 'blue'])
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f62b9f81780>

Given that the order of the majors are based on descending order of the median income for each major, it is clear on the barplots that women tend to dominate majors that are on the lower spectrum of income, while males tend to dominate the higher spectrum of income.

Conclusion:

Let's wrap-up with sharing some of the observations that we made above:

The most popular majors are not those that result in the highest (median) salaries. Majors with higher percentages of women (which is actually a majority) tend to have lower (median) salaries. Clearly, we've only be scratching the surface. The dataset contains a wealth of interesting data to explore. Possibly to be continued at another occassion!