In this guided project, we'll explore how using the pandas plotting functionality along with the Jupyter notebook interface allows us to explore data quickly using visualizations.
We'll be working with a dataset on the job outcomes of students who graduated from college between 2010 and 2012
Dataset columns:
Part_time - Number employed less than 35 hours.
#read file and show the structure
recent_grads = pd.read_csv('recent-grads.csv')
print(recent_grads.iloc[0])
print(recent_grads.head())
print(recent_grads.tail())
print(recent_grads.describe())
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 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] Rank Major_code Total Men Women \ count 173.000000 173.000000 172.000000 172.000000 172.000000 mean 87.000000 3879.815029 39370.081395 16723.406977 22646.674419 std 50.084928 1687.753140 63483.491009 28122.433474 41057.330740 min 1.000000 1100.000000 124.000000 119.000000 0.000000 25% 44.000000 2403.000000 4549.750000 2177.500000 1778.250000 50% 87.000000 3608.000000 15104.000000 5434.000000 8386.500000 75% 130.000000 5503.000000 38909.750000 14631.000000 22553.750000 max 173.000000 6403.000000 393735.000000 173809.000000 307087.000000 ShareWomen Sample_size Employed Full_time Part_time \ count 172.000000 173.000000 173.000000 173.000000 173.000000 mean 0.522223 356.080925 31192.763006 26029.306358 8832.398844 std 0.231205 618.361022 50675.002241 42869.655092 14648.179473 min 0.000000 2.000000 0.000000 111.000000 0.000000 25% 0.336026 39.000000 3608.000000 3154.000000 1030.000000 50% 0.534024 130.000000 11797.000000 10048.000000 3299.000000 75% 0.703299 338.000000 31433.000000 25147.000000 9948.000000 max 0.968954 4212.000000 307933.000000 251540.000000 115172.000000 Full_time_year_round Unemployed Unemployment_rate Median \ count 173.000000 173.000000 173.000000 173.000000 mean 19694.427746 2416.329480 0.068191 40151.445087 std 33160.941514 4112.803148 0.030331 11470.181802 min 111.000000 0.000000 0.000000 22000.000000 25% 2453.000000 304.000000 0.050306 33000.000000 50% 7413.000000 893.000000 0.067961 36000.000000 75% 16891.000000 2393.000000 0.087557 45000.000000 max 199897.000000 28169.000000 0.177226 110000.000000 P25th P75th College_jobs Non_college_jobs \ count 173.000000 173.000000 173.000000 173.000000 mean 29501.445087 51494.219653 12322.635838 13284.497110 std 9166.005235 14906.279740 21299.868863 23789.655363 min 18500.000000 22000.000000 0.000000 0.000000 25% 24000.000000 42000.000000 1675.000000 1591.000000 50% 27000.000000 47000.000000 4390.000000 4595.000000 75% 33000.000000 60000.000000 14444.000000 11783.000000 max 95000.000000 125000.000000 151643.000000 148395.000000 Low_wage_jobs count 173.000000 mean 3859.017341 std 6944.998579 min 0.000000 25% 340.000000 50% 1231.000000 75% 3466.000000 max 48207.000000
#setting the environment importing
import pandas as pd
import matplotlib.pyplot as plt
% matplotlib inline
from pandas.plotting import scatter_matrix
#check missing values and drop if it exists. Check new shape of dataset
raw_data_count=recent_grads.shape[0]
recent_grads=recent_grads.dropna(axis=0)
cleaned_data_count=recent_grads.shape[0]
print(raw_data_count, cleaned_data_count)
173 172
recent_grads.plot(x='Sample_size', y='Median', kind='scatter', title='Median vs. Sample_size')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbff101fd0>
recent_grads.plot(x='Sample_size', y='Unemployment_rate', kind='scatter', title='Unemployment_rate vs. Sample_size')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbfeaf89e8>
recent_grads.plot(x='Full_time', y='Median', kind='scatter', title='Median vs. Full_time')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbfead0208>
recent_grads.plot(x='ShareWomen', y='Unemployment_rate', kind='scatter', title='Unemployment_rate vs. ShareWomen')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbfea2f6d8>
recent_grads.plot(x='Men', y='Median', kind='scatter', title='Median vs. Men')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbfea14080>
recent_grads.plot(x='Women', y='Median', kind='scatter', title='Median vs. Woman')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbfe976e10>
** 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? **
Actually there are not strict correletions between the data so we can not say there is some relations between majors, sex of students, full-time job and money,
cols = ['Sample_size', 'Median', 'Employed', 'Full_time', 'ShareWomen', 'Unemployment_rate', 'Men', 'Women']
fig = plt.figure(figsize=(5,48))
for r in range(0,8):
ax=fig.add_subplot(8,1,r+1)
ax=recent_grads[cols[r]].plot(kind='hist', rot=30)
ax.set_title(cols[r])
** Question: What's the most common median salary range? **
Most common median salary is in range 30000-40000
Question: What percent of majors are predominantly female/male?
#calculate female predominality
fem_predominality = recent_grads[recent_grads['ShareWomen'] > 0.5]
fem_predominality.shape[0]/recent_grads.shape[0]
0.5581395348837209
#plot female predominality
ax1 = recent_grads['ShareWomen'].hist(bins=2,range=(0,1))
ax1.set_title('ShareWomen')
ax1.set_ylabel('Num of Majors')
<matplotlib.text.Text at 0x7efbfcd56438>
Thus we can see 56% of majors female predominality and 100-56=44% male predominality. For this task I prefer calculation itseft rather then plots
scatter_matrix(recent_grads[['Sample_size', 'Median']], figsize=(20,20))
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7efbfef7ef60>, <matplotlib.axes._subplots.AxesSubplot object at 0x7efbfee64d30>], [<matplotlib.axes._subplots.AxesSubplot object at 0x7efbfed3ba58>, <matplotlib.axes._subplots.AxesSubplot object at 0x7efbfee3dba8>]], dtype=object)
scatter_matrix(recent_grads[['Sample_size', 'Median', 'Unemployment_rate']], figsize=(20,20))
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7efbff000278>, <matplotlib.axes._subplots.AxesSubplot object at 0x7efbfedfce10>, <matplotlib.axes._subplots.AxesSubplot object at 0x7efbfcd3e7b8>], [<matplotlib.axes._subplots.AxesSubplot object at 0x7efbfeef7ac8>, <matplotlib.axes._subplots.AxesSubplot object at 0x7efbff0b74e0>, <matplotlib.axes._subplots.AxesSubplot object at 0x7efbfccf3630>], [<matplotlib.axes._subplots.AxesSubplot object at 0x7efbfccc0240>, <matplotlib.axes._subplots.AxesSubplot object at 0x7efbfcc7f048>, <matplotlib.axes._subplots.AxesSubplot object at 0x7efbfcbc7278>]], dtype=object)
There is no direct correlations between these columns
#plot first 10 rows in the dataset and ShareWomen percentage in this samples
recent_grads[:10]['ShareWomen'].plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbfc9e0b38>
#plot last 10 rows in the dataset and ShareWomen percentage in this samples
recent_grads[cleaned_data_count-10:]['ShareWomen'].plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbfc96d198>
#plot first 10 rows in the dataset and Unemployment_rate percentage in this samples
recent_grads[:10]['Unemployment_rate'].plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbfc91f8d0>
#plot last 10 rows in the dataset and Unemployment_rate percentage in this samples
recent_grads[cleaned_data_count-10:]['Unemployment_rate'].plot(kind='bar')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbfc839780>
** Task 1: Use a grouped bar plot to compare the number of men with the number of women in each category of majors**
#prepare data for plotting
recent_grads['ShareMen']=1-recent_grads['ShareWomen']
Share = recent_grads[['ShareMen','ShareWomen','Major']]
#plotting first 10 and last 10 majors:
Share.head(10).plot.barh(x='Major')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbf7c1c4a8>
Share.tail(10).plot.barh(x='Major')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbf7e27a90>
The percentage of Men in engineering majors is high, when there is a predominatory of female in non engineering majors. As the dataset is ranked by median earnings, we can see share of women in less median earnings majors is higher
** Task 2: Use a box plot to explore the distributions of median salaries and unemployment rate. **
recent_grads['Median'].plot(kind='box')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbf7d19550>
recent_grads['Unemployment_rate'].plot(kind='box')
<matplotlib.axes._subplots.AxesSubplot at 0x7efbf7bd9080>
Common median salary is in range 35000-45000, unemployment rate is in range 0.04-0.09
Conclusion: different kinds of visualizations helps us make explonatory data analysis. It is a quite powerful tool in distingushing relations between data, their distributions and main features