The following is a script I made to test features in Python, using a dataset I generated from a local PostgreSQL database.

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns 

#itables for viewing data
from itables import init_notebook_mode
from itables import show

#Read data
hr_data = pd.read_csv('~/Documents/Projects/SQL/Generated Data/HR_employee_list_and_salaries.csv')
hr_data = pd.DataFrame(hr_data)

print(hr_data.iloc[0:5])

             Name Department                   Email Address  \
0     Steven King  Executive     steven.king@sqltutorial.org   
1     Lex De Haan  Executive     lex.de haan@sqltutorial.org   
2   Neena Kochhar  Executive   neena.kochhar@sqltutorial.org   
3    John Russell      Sales    john.russell@sqltutorial.org   
4  Karen Partners      Sales  karen.partners@sqltutorial.org   

                           Title  Salary  Min Salary  Max Salary   Hire Date  \
0                      President  240000      200000      400000  2007-06-12   
1  Administration Vice President  170000      150000      300000  2013-01-08   
2  Administration Vice President  170000      150000      300000  2009-09-16   
3                  Sales Manager  140000      100000      200000  2016-09-26   
4                  Sales Manager  135000      100000      200000  2016-12-31   

      City ZIP Code State/Province                   Country  
0  Seattle    98199     Washington  United States of America  
1  Seattle    98199     Washington  United States of America  
2  Seattle    98199     Washington  United States of America  
3   Oxford  OX9 9ZB         Oxford            United Kingdom  
4   Oxford  OX9 9ZB         Oxford            United Kingdom  

# Testing Information and Summary functions.

## Information calls: 
### Shape
hr_data.shape

(33, 12)

# Index
hr_data.index

RangeIndex(start=0, stop=33, step=1)

# Columns
hr_data.columns

Index(['Name', 'Department', 'Email Address', 'Title', 'Salary', 'Min Salary',
       'Max Salary', 'Hire Date', 'City', 'ZIP Code', 'State/Province',
       'Country'],
      dtype='object')

# Info (seems to be similar to head in R)
hr_data.info

<bound method DataFrame.info of                  Name        Department                      Email Address  \
0         Steven King         Executive        steven.king@sqltutorial.org   
1         Lex De Haan         Executive        lex.de haan@sqltutorial.org   
2       Neena Kochhar         Executive      neena.kochhar@sqltutorial.org   
3        John Russell             Sales       john.russell@sqltutorial.org   
4      Karen Partners             Sales     karen.partners@sqltutorial.org   
5   Michael Hartstein         Marketing  michael.hartstein@sqltutorial.org   
6     Nancy Greenberg           Finance    nancy.greenberg@sqltutorial.org   
7     Shelley Higgins        Accounting    shelley.higgins@sqltutorial.org   
8        Den Raphaely        Purchasing       den.raphaely@sqltutorial.org   
9        Hermann Baer  Public Relations       hermann.baer@sqltutorial.org   
10   Alexander Hunold                IT   alexander.hunold@sqltutorial.org   
11      Daniel Faviet           Finance      daniel.faviet@sqltutorial.org   
12    Jonathon Taylor             Sales    jonathon.taylor@sqltutorial.org   
13    Jack Livingston             Sales    jack.livingston@sqltutorial.org   
14      William Gietz        Accounting      william.gietz@sqltutorial.org   
15          John Chen           Finance          john.chen@sqltutorial.org   
16         Adam Fripp          Shipping         adam.fripp@sqltutorial.org   
17      Matthew Weiss          Shipping      matthew.weiss@sqltutorial.org   
18     Payam Kaufling          Shipping     payam.kaufling@sqltutorial.org   
19  Jose Manuel Urman           Finance  jose manuel.urman@sqltutorial.org   
20     Ismael Sciarra           Finance     ismael.sciarra@sqltutorial.org   
21    Kimberely Grant             Sales    kimberely.grant@sqltutorial.org   
22          Luis Popp           Finance          luis.popp@sqltutorial.org   
23       Susan Mavris   Human Resources       susan.mavris@sqltutorial.org   
24     Shanta Vollman          Shipping     shanta.vollman@sqltutorial.org   
25    Charles Johnson             Sales    charles.johnson@sqltutorial.org   
26            Pat Fay         Marketing            pat.fay@sqltutorial.org   
27        Bruce Ernst                IT        bruce.ernst@sqltutorial.org   
28       David Austin                IT       david.austin@sqltutorial.org   
29    Valli Pataballa                IT    valli.pataballa@sqltutorial.org   
30    Jennifer Whalen    Administration    jennifer.whalen@sqltutorial.org   
31      Diana Lorentz                IT      diana.lorentz@sqltutorial.org   
32         Sarah Bell          Shipping         sarah.bell@sqltutorial.org   

                              Title  Salary  Min Salary  Max Salary  \
0                         President  240000      200000      400000   
1     Administration Vice President  170000      150000      300000   
2     Administration Vice President  170000      150000      300000   
3                     Sales Manager  140000      100000      200000   
4                     Sales Manager  135000      100000      200000   
5                 Marketing Manager  130000       90000      150000   
6                   Finance Manager  120000       82000      160000   
7                Accounting Manager  120000       82000      160000   
8                Purchasing Manager  110000       80000      150000   
9   Public Relations Representative  100000       45000      105000   
10                       Programmer   90000       40000      100000   
11                       Accountant   90000       42000       90000   
12             Sales Representative   86000       60000      120000   
13             Sales Representative   84000       60000      120000   
14                Public Accountant   83000       42000       90000   
15                       Accountant   82000       42000       90000   
16                    Stock Manager   82000       55000       85000   
17                    Stock Manager   80000       55000       85000   
18                    Stock Manager   79000       55000       85000   
19                       Accountant   78000       42000       90000   
20                       Accountant   77000       42000       90000   
21             Sales Representative   70000       60000      120000   
22                       Accountant   69000       42000       90000   
23   Human Resources Representative   65000       40000       90000   
24                    Stock Manager   65000       55000       85000   
25             Sales Representative   62000       60000      120000   
26         Marketing Representative   60000       40000       90000   
27                       Programmer   60000       40000      100000   
28                       Programmer   48000       40000      100000   
29                       Programmer   48000       40000      100000   
30         Administration Assistant   44000       30000       60000   
31                       Programmer   42000       40000      100000   
32                   Shipping Clerk   40000       25000       55000   

     Hire Date                 City ZIP Code State/Province  \
0   2007-06-12              Seattle    98199     Washington   
1   2013-01-08              Seattle    98199     Washington   
2   2009-09-16              Seattle    98199     Washington   
3   2016-09-26               Oxford  OX9 9ZB         Oxford   
4   2016-12-31               Oxford  OX9 9ZB         Oxford   
5   2016-02-12              Toronto  M5V 2L7        Ontario   
6   2014-08-12              Seattle    98199     Washington   
7   2014-06-02              Seattle    98199     Washington   
8   2014-12-02              Seattle    98199     Washington   
9   2014-06-02               Munich    80925        Bavaria   
10  2009-12-29            Southlake    26192          Texas   
11  2014-08-11              Seattle    98199     Washington   
12  2018-03-19               Oxford  OX9 9ZB         Oxford   
13  2018-04-18               Oxford  OX9 9ZB         Oxford   
14  2014-06-02              Seattle    98199     Washington   
15  2017-09-23              Seattle    98199     Washington   
16  2017-04-05  South San Francisco    99236     California   
17  2016-07-13  South San Francisco    99236     California   
18  2015-04-26  South San Francisco    99236     California   
19  2018-03-02              Seattle    98199     Washington   
20  2017-09-25              Seattle    98199     Washington   
21  2019-05-19               Oxford  OX9 9ZB         Oxford   
22  2019-12-02              Seattle    98199     Washington   
23  2014-06-02               London      NaN            NaN   
24  2017-10-05  South San Francisco    99236     California   
25  2019-12-30               Oxford  OX9 9ZB         Oxford   
26  2017-08-12              Toronto  M5V 2L7        Ontario   
27  2011-05-16            Southlake    26192          Texas   
28  2017-06-20            Southlake    26192          Texas   
29  2018-01-31            Southlake    26192          Texas   
30  2007-09-12              Seattle    98199     Washington   
31  2019-02-02            Southlake    26192          Texas   
32  2016-01-30  South San Francisco    99236     California   

                     Country  
0   United States of America  
1   United States of America  
2   United States of America  
3             United Kingdom  
4             United Kingdom  
5                     Canada  
6   United States of America  
7   United States of America  
8   United States of America  
9                    Germany  
10  United States of America  
11  United States of America  
12            United Kingdom  
13            United Kingdom  
14  United States of America  
15  United States of America  
16  United States of America  
17  United States of America  
18  United States of America  
19  United States of America  
20  United States of America  
21            United Kingdom  
22  United States of America  
23            United Kingdom  
24  United States of America  
25            United Kingdom  
26                    Canada  
27  United States of America  
28  United States of America  
29  United States of America  
30  United States of America  
31  United States of America  
32  United States of America  >

# Count number of non-NA values
hr_data.count()

Name              33
Department        33
Email Address     33
Title             33
Salary            33
Min Salary        33
Max Salary        33
Hire Date         33
City              33
ZIP Code          32
State/Province    32
Country           33
dtype: int64

# Summary Statistics: 
## Sum
hr_data['Salary'].sum()

3019000

# Cumulative Sum

hr_data['Salary'].cumsum()

0      240000
1      410000
2      580000
3      720000
4      855000
5      985000
6     1105000
7     1225000
8     1335000
9     1435000
10    1525000
11    1615000
12    1701000
13    1785000
14    1868000
15    1950000
16    2032000
17    2112000
18    2191000
19    2269000
20    2346000
21    2416000
22    2485000
23    2550000
24    2615000
25    2677000
26    2737000
27    2797000
28    2845000
29    2893000
30    2937000
31    2979000
32    3019000
Name: Salary, dtype: int64

# Describe

hr_data.describe

<bound method NDFrame.describe of                  Name        Department                      Email Address  \
0         Steven King         Executive        steven.king@sqltutorial.org   
1         Lex De Haan         Executive        lex.de haan@sqltutorial.org   
2       Neena Kochhar         Executive      neena.kochhar@sqltutorial.org   
3        John Russell             Sales       john.russell@sqltutorial.org   
4      Karen Partners             Sales     karen.partners@sqltutorial.org   
5   Michael Hartstein         Marketing  michael.hartstein@sqltutorial.org   
6     Nancy Greenberg           Finance    nancy.greenberg@sqltutorial.org   
7     Shelley Higgins        Accounting    shelley.higgins@sqltutorial.org   
8        Den Raphaely        Purchasing       den.raphaely@sqltutorial.org   
9        Hermann Baer  Public Relations       hermann.baer@sqltutorial.org   
10   Alexander Hunold                IT   alexander.hunold@sqltutorial.org   
11      Daniel Faviet           Finance      daniel.faviet@sqltutorial.org   
12    Jonathon Taylor             Sales    jonathon.taylor@sqltutorial.org   
13    Jack Livingston             Sales    jack.livingston@sqltutorial.org   
14      William Gietz        Accounting      william.gietz@sqltutorial.org   
15          John Chen           Finance          john.chen@sqltutorial.org   
16         Adam Fripp          Shipping         adam.fripp@sqltutorial.org   
17      Matthew Weiss          Shipping      matthew.weiss@sqltutorial.org   
18     Payam Kaufling          Shipping     payam.kaufling@sqltutorial.org   
19  Jose Manuel Urman           Finance  jose manuel.urman@sqltutorial.org   
20     Ismael Sciarra           Finance     ismael.sciarra@sqltutorial.org   
21    Kimberely Grant             Sales    kimberely.grant@sqltutorial.org   
22          Luis Popp           Finance          luis.popp@sqltutorial.org   
23       Susan Mavris   Human Resources       susan.mavris@sqltutorial.org   
24     Shanta Vollman          Shipping     shanta.vollman@sqltutorial.org   
25    Charles Johnson             Sales    charles.johnson@sqltutorial.org   
26            Pat Fay         Marketing            pat.fay@sqltutorial.org   
27        Bruce Ernst                IT        bruce.ernst@sqltutorial.org   
28       David Austin                IT       david.austin@sqltutorial.org   
29    Valli Pataballa                IT    valli.pataballa@sqltutorial.org   
30    Jennifer Whalen    Administration    jennifer.whalen@sqltutorial.org   
31      Diana Lorentz                IT      diana.lorentz@sqltutorial.org   
32         Sarah Bell          Shipping         sarah.bell@sqltutorial.org   

                              Title  Salary  Min Salary  Max Salary  \
0                         President  240000      200000      400000   
1     Administration Vice President  170000      150000      300000   
2     Administration Vice President  170000      150000      300000   
3                     Sales Manager  140000      100000      200000   
4                     Sales Manager  135000      100000      200000   
5                 Marketing Manager  130000       90000      150000   
6                   Finance Manager  120000       82000      160000   
7                Accounting Manager  120000       82000      160000   
8                Purchasing Manager  110000       80000      150000   
9   Public Relations Representative  100000       45000      105000   
10                       Programmer   90000       40000      100000   
11                       Accountant   90000       42000       90000   
12             Sales Representative   86000       60000      120000   
13             Sales Representative   84000       60000      120000   
14                Public Accountant   83000       42000       90000   
15                       Accountant   82000       42000       90000   
16                    Stock Manager   82000       55000       85000   
17                    Stock Manager   80000       55000       85000   
18                    Stock Manager   79000       55000       85000   
19                       Accountant   78000       42000       90000   
20                       Accountant   77000       42000       90000   
21             Sales Representative   70000       60000      120000   
22                       Accountant   69000       42000       90000   
23   Human Resources Representative   65000       40000       90000   
24                    Stock Manager   65000       55000       85000   
25             Sales Representative   62000       60000      120000   
26         Marketing Representative   60000       40000       90000   
27                       Programmer   60000       40000      100000   
28                       Programmer   48000       40000      100000   
29                       Programmer   48000       40000      100000   
30         Administration Assistant   44000       30000       60000   
31                       Programmer   42000       40000      100000   
32                   Shipping Clerk   40000       25000       55000   

     Hire Date                 City ZIP Code State/Province  \
0   2007-06-12              Seattle    98199     Washington   
1   2013-01-08              Seattle    98199     Washington   
2   2009-09-16              Seattle    98199     Washington   
3   2016-09-26               Oxford  OX9 9ZB         Oxford   
4   2016-12-31               Oxford  OX9 9ZB         Oxford   
5   2016-02-12              Toronto  M5V 2L7        Ontario   
6   2014-08-12              Seattle    98199     Washington   
7   2014-06-02              Seattle    98199     Washington   
8   2014-12-02              Seattle    98199     Washington   
9   2014-06-02               Munich    80925        Bavaria   
10  2009-12-29            Southlake    26192          Texas   
11  2014-08-11              Seattle    98199     Washington   
12  2018-03-19               Oxford  OX9 9ZB         Oxford   
13  2018-04-18               Oxford  OX9 9ZB         Oxford   
14  2014-06-02              Seattle    98199     Washington   
15  2017-09-23              Seattle    98199     Washington   
16  2017-04-05  South San Francisco    99236     California   
17  2016-07-13  South San Francisco    99236     California   
18  2015-04-26  South San Francisco    99236     California   
19  2018-03-02              Seattle    98199     Washington   
20  2017-09-25              Seattle    98199     Washington   
21  2019-05-19               Oxford  OX9 9ZB         Oxford   
22  2019-12-02              Seattle    98199     Washington   
23  2014-06-02               London      NaN            NaN   
24  2017-10-05  South San Francisco    99236     California   
25  2019-12-30               Oxford  OX9 9ZB         Oxford   
26  2017-08-12              Toronto  M5V 2L7        Ontario   
27  2011-05-16            Southlake    26192          Texas   
28  2017-06-20            Southlake    26192          Texas   
29  2018-01-31            Southlake    26192          Texas   
30  2007-09-12              Seattle    98199     Washington   
31  2019-02-02            Southlake    26192          Texas   
32  2016-01-30  South San Francisco    99236     California   

                     Country  
0   United States of America  
1   United States of America  
2   United States of America  
3             United Kingdom  
4             United Kingdom  
5                     Canada  
6   United States of America  
7   United States of America  
8   United States of America  
9                    Germany  
10  United States of America  
11  United States of America  
12            United Kingdom  
13            United Kingdom  
14  United States of America  
15  United States of America  
16  United States of America  
17  United States of America  
18  United States of America  
19  United States of America  
20  United States of America  
21            United Kingdom  
22  United States of America  
23            United Kingdom  
24  United States of America  
25            United Kingdom  
26                    Canada  
27  United States of America  
28  United States of America  
29  United States of America  
30  United States of America  
31  United States of America  
32  United States of America  >

# Mean

hr_data['Salary'].mean()

91484.84848484848

hr_data['Salary'].median()

82000.0

# Testing data cleaning functions
## Drop na values and then recount for correctness

hr_data = hr_data.dropna()

hr_data.count()


Name              32
Department        32
Email Address     32
Title             32
Salary            32
Min Salary        32
Max Salary        32
Hire Date         32
City              32
ZIP Code          32
State/Province    32
Country           32
dtype: int64

# Print rows that have executives making over $100,000

hr_data[hr_data['Salary'] >= 100000 ]
Name Department Email Address Title Salary Min Salary Max Salary Hire Date City ZIP Code State/Province Country
0 Steven King Executive steven.king@sqltutorial.org President 240000 200000 400000 2007-06-12 Seattle 98199 Washington United States of America
1 Lex De Haan Executive lex.de haan@sqltutorial.org Administration Vice President 170000 150000 300000 2013-01-08 Seattle 98199 Washington United States of America
2 Neena Kochhar Executive neena.kochhar@sqltutorial.org Administration Vice President 170000 150000 300000 2009-09-16 Seattle 98199 Washington United States of America
3 John Russell Sales john.russell@sqltutorial.org Sales Manager 140000 100000 200000 2016-09-26 Oxford OX9 9ZB Oxford United Kingdom
4 Karen Partners Sales karen.partners@sqltutorial.org Sales Manager 135000 100000 200000 2016-12-31 Oxford OX9 9ZB Oxford United Kingdom
5 Michael Hartstein Marketing michael.hartstein@sqltutorial.org Marketing Manager 130000 90000 150000 2016-02-12 Toronto M5V 2L7 Ontario Canada
6 Nancy Greenberg Finance nancy.greenberg@sqltutorial.org Finance Manager 120000 82000 160000 2014-08-12 Seattle 98199 Washington United States of America
7 Shelley Higgins Accounting shelley.higgins@sqltutorial.org Accounting Manager 120000 82000 160000 2014-06-02 Seattle 98199 Washington United States of America
8 Den Raphaely Purchasing den.raphaely@sqltutorial.org Purchasing Manager 110000 80000 150000 2014-12-02 Seattle 98199 Washington United States of America
9 Hermann Baer Public Relations hermann.baer@sqltutorial.org Public Relations Representative 100000 45000 105000 2014-06-02 Munich 80925 Bavaria Germany 
# String replace within a column 

hr_data['Email Address'] = hr_data['Email Address'].str.replace("sqltutorial.org", "repugs.edu")

print(hr_data['Email Address'])

0           steven.king@repugs.edu
1           lex.de haan@repugs.edu
2         neena.kochhar@repugs.edu
3          john.russell@repugs.edu
4        karen.partners@repugs.edu
5     michael.hartstein@repugs.edu
6       nancy.greenberg@repugs.edu
7       shelley.higgins@repugs.edu
8          den.raphaely@repugs.edu
9          hermann.baer@repugs.edu
10     alexander.hunold@repugs.edu
11        daniel.faviet@repugs.edu
12      jonathon.taylor@repugs.edu
13      jack.livingston@repugs.edu
14        william.gietz@repugs.edu
15            john.chen@repugs.edu
16           adam.fripp@repugs.edu
17        matthew.weiss@repugs.edu
18       payam.kaufling@repugs.edu
19    jose manuel.urman@repugs.edu
20       ismael.sciarra@repugs.edu
21      kimberely.grant@repugs.edu
22            luis.popp@repugs.edu
24       shanta.vollman@repugs.edu
25      charles.johnson@repugs.edu
26              pat.fay@repugs.edu
27          bruce.ernst@repugs.edu
28         david.austin@repugs.edu
29      valli.pataballa@repugs.edu
30      jennifer.whalen@repugs.edu
31        diana.lorentz@repugs.edu
32           sarah.bell@repugs.edu
Name: Email Address, dtype: object


C:\Users\bruce\AppData\Local\Temp\ipykernel_22892&#8291;3629824364.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  hr_data['Email Address'] = hr_data['Email Address'].str.replace("sqltutorial.org", "repugs.edu")

print(hr_data.iloc[0:1])

          Name Department           Email Address      Title  Salary  \
0  Steven King  Executive  steven.king@repugs.edu  President  240000   

   Min Salary  Max Salary   Hire Date     City ZIP Code State/Province  \
0      200000      400000  2007-06-12  Seattle    98199     Washington   

                    Country  
0  United States of America  

# Query 
hr_data.query('Salary > 100000')
Name Department Email Address Title Salary Min Salary Max Salary Hire Date City ZIP Code State/Province Country
0 Steven King Executive steven.king@repugs.edu President 240000 200000 400000 2007-06-12 Seattle 98199 Washington United States of America
1 Lex De Haan Executive lex.de haan@repugs.edu Administration Vice President 170000 150000 300000 2013-01-08 Seattle 98199 Washington United States of America
2 Neena Kochhar Executive neena.kochhar@repugs.edu Administration Vice President 170000 150000 300000 2009-09-16 Seattle 98199 Washington United States of America
3 John Russell Sales john.russell@repugs.edu Sales Manager 140000 100000 200000 2016-09-26 Oxford OX9 9ZB Oxford United Kingdom
4 Karen Partners Sales karen.partners@repugs.edu Sales Manager 135000 100000 200000 2016-12-31 Oxford OX9 9ZB Oxford United Kingdom
5 Michael Hartstein Marketing michael.hartstein@repugs.edu Marketing Manager 130000 90000 150000 2016-02-12 Toronto M5V 2L7 Ontario Canada
6 Nancy Greenberg Finance nancy.greenberg@repugs.edu Finance Manager 120000 82000 160000 2014-08-12 Seattle 98199 Washington United States of America
7 Shelley Higgins Accounting shelley.higgins@repugs.edu Accounting Manager 120000 82000 160000 2014-06-02 Seattle 98199 Washington United States of America
8 Den Raphaely Purchasing den.raphaely@repugs.edu Purchasing Manager 110000 80000 150000 2014-12-02 Seattle 98199 Washington United States of America 
# String matching and query 
hr_data[hr_data['Title'].str.contains("President|Manager")]
hr_data.query('Salary > 100000')
Name Department Email Address Title Salary Min Salary Max Salary Hire Date City ZIP Code State/Province Country
0 Steven King Executive steven.king@repugs.edu President 240000 200000 400000 2007-06-12 Seattle 98199 Washington United States of America
1 Lex De Haan Executive lex.de haan@repugs.edu Administration Vice President 170000 150000 300000 2013-01-08 Seattle 98199 Washington United States of America
2 Neena Kochhar Executive neena.kochhar@repugs.edu Administration Vice President 170000 150000 300000 2009-09-16 Seattle 98199 Washington United States of America
3 John Russell Sales john.russell@repugs.edu Sales Manager 140000 100000 200000 2016-09-26 Oxford OX9 9ZB Oxford United Kingdom
4 Karen Partners Sales karen.partners@repugs.edu Sales Manager 135000 100000 200000 2016-12-31 Oxford OX9 9ZB Oxford United Kingdom
5 Michael Hartstein Marketing michael.hartstein@repugs.edu Marketing Manager 130000 90000 150000 2016-02-12 Toronto M5V 2L7 Ontario Canada
6 Nancy Greenberg Finance nancy.greenberg@repugs.edu Finance Manager 120000 82000 160000 2014-08-12 Seattle 98199 Washington United States of America
7 Shelley Higgins Accounting shelley.higgins@repugs.edu Accounting Manager 120000 82000 160000 2014-06-02 Seattle 98199 Washington United States of America
8 Den Raphaely Purchasing den.raphaely@repugs.edu Purchasing Manager 110000 80000 150000 2014-12-02 Seattle 98199 Washington United States of America 
# I'm telling Python to print column names and list their type, every column in this particular dataset is an object 
print("<{}>".format(hr_data.columns))

# When I run this particular line, Python is telling me which columns are of a particular type
hr_data.dtypes

<Index(['Name', 'Department', 'Email Address', 'Title', 'Salary', 'Min Salary',
       'Max Salary', 'Hire Date', 'City', 'ZIP Code', 'State/Province',
       'Country'],
      dtype='object')>





Name              object
Department        object
Email Address     object
Title             object
Salary             int64
Min Salary         int64
Max Salary         int64
Hire Date         object
City              object
ZIP Code          object
State/Province    object
Country           object
dtype: object

# X axis automatically defaults to count, which is something I wish R did on its own! 

hr_data.plot(kind = 'line', 
             alpha = .5, 
             rot = 45)

<Axes: >

png