Projekt¶

Data Science Job Posting on Glassdoor¶

Scienceto on Data Science valdkonnas vabade töökohtade andmekogum Glassdoori saidil.

Impordime vajalikud teegid:

In [ ]:
import pandas as pd

import seaborn as sns

from prettytable import PrettyTable

Loeme faili sisse:

In [ ]:
path = 'Cleaned_DS_Jobs.csv'
df = pd.read_csv(path)

Vaatame, millest koosneb andmestik:

In [ ]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 660 entries, 0 to 659
Data columns (total 27 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Job Title          660 non-null    object 
 1   Salary Estimate    660 non-null    object 
 2   Job Description    660 non-null    object 
 3   Rating             660 non-null    float64
 4   Company Name       660 non-null    object 
 5   Location           660 non-null    object 
 6   Headquarters       660 non-null    object 
 7   Size               660 non-null    object 
 8   Type of ownership  660 non-null    object 
 9   Industry           660 non-null    object 
 10  Sector             660 non-null    object 
 11  Revenue            660 non-null    object 
 12  min_salary         660 non-null    int64  
 13  max_salary         660 non-null    int64  
 14  avg_salary         660 non-null    int64  
 15  job_state          660 non-null    object 
 16  same_state         660 non-null    int64  
 17  company_age        660 non-null    int64  
 18  python             660 non-null    int64  
 19  excel              660 non-null    int64  
 20  hadoop             660 non-null    int64  
 21  spark              660 non-null    int64  
 22  aws                660 non-null    int64  
 23  tableau            660 non-null    int64  
 24  big_data           660 non-null    int64  
 25  job_simp           660 non-null    object 
 26  seniority          660 non-null    object 
dtypes: float64(1), int64(12), object(14)
memory usage: 139.3+ KB

Andmestik koosneb 660 reast, milles kajastub ülaltoodud info nagu Töö nimetus (ingl. k. Job Title), ennustatav töötasu, töökirjeldus, hinnang, ettevõtte nimi jne. Andmed on float64 (1 kirje), int64 (12 kirjet) ja object (14 kirjet) kujul.

Vaatame, missugune andmestik välja näeb meie programmis:

In [ ]:
df.head()
Out[ ]:
Job Title Salary Estimate Job Description Rating Company Name Location Headquarters Size Type of ownership Industry ... company_age python excel hadoop spark aws tableau big_data job_simp seniority
0 Sr Data Scientist 137-171 Description\n\nThe Senior Data Scientist is re... 3.1 Healthfirst New York, NY New York, NY 1001 to 5000 employees Nonprofit Organization Insurance Carriers ... 27 0 0 0 0 1 0 0 data scientist senior
1 Data Scientist 137-171 Secure our Nation, Ignite your Future\n\nJoin ... 4.2 ManTech Chantilly, VA Herndon, VA 5001 to 10000 employees Company - Public Research & Development ... 52 0 0 1 0 0 0 1 data scientist na
2 Data Scientist 137-171 Overview\n\n\nAnalysis Group is one of the lar... 3.8 Analysis Group Boston, MA Boston, MA 1001 to 5000 employees Private Practice / Firm Consulting ... 39 1 1 0 0 1 0 0 data scientist na
3 Data Scientist 137-171 JOB DESCRIPTION:\n\nDo you have a passion for ... 3.5 INFICON Newton, MA Bad Ragaz, Switzerland 501 to 1000 employees Company - Public Electrical & Electronic Manufacturing ... 20 1 1 0 0 1 0 0 data scientist na
4 Data Scientist 137-171 Data Scientist\nAffinity Solutions / Marketing... 2.9 Affinity Solutions New York, NY New York, NY 51 to 200 employees Company - Private Advertising & Marketing ... 22 1 1 0 0 0 0 0 data scientist na

5 rows × 27 columns

Kuvame Salary Estimate, Rating, min_salary ja max_salary veerud, sorteerides min_salary järgi:

In [ ]:
display(df[['Salary Estimate', 'Rating', 'min_salary', 'max_salary']].sort_values(['min_salary'], ascending=False))
Salary Estimate Rating min_salary max_salary
499 212-331 4.1 212 331
519 212-331 3.5 212 331
501 212-331 3.6 212 331
502 212-331 5.0 212 331
503 212-331 3.5 212 331
... ... ... ... ...
460 31-56 3.9 31 56
459 31-56 4.0 31 56
458 31-56 3.3 31 56
470 31-56 2.9 31 56
469 31-56 4.1 31 56

660 rows × 4 columns

Sellest tabelist näeme, et Salary Estimate veerg on tuletatud min_salary ja max_salary veergude järgi.

Näitame kõige suuremat palka pakkuvaid ja kõige paremini hinnatuid ettevõtteid, sorteerime Rating'u järgi:

In [ ]:
filtered_top=df[(df['avg_salary'] > 200) & (df['Rating'] > 0)].sort_values(['Rating'], ascending=False)
display(filtered_top)
Job Title Salary Estimate Job Description Rating Company Name Location Headquarters Size Type of ownership Industry ... company_age python excel hadoop spark aws tableau big_data job_simp seniority
502 Data Scientist(s)/Machine Learning Engineer 212-331 Company: AI/Data Science\nLocation: New York C... 5.0 Blue Horizon Tek Solutions New York, NY Coconut Creek, FL 1 to 50 employees Company - Private Staffing & Outsourcing ... 33 1 1 0 0 0 0 0 data scientist na
509 Data Science Software Engineer 212-331 We love programming and the excitement that co... 4.8 Klaviyo Boston, MA Boston, MA 201 to 500 employees Company - Private Computer Hardware & Software ... 8 1 0 0 0 0 0 0 na na
504 Data Scientist 212-331 NO OPT CPT pls.\n\nÂ\n\nNeed local toÂBay Area... 4.7 Sharpedge Solutions Inc Seattle, WA Lombard, IL Unknown Company - Private Publishing ... -1 1 1 1 1 1 0 0 data scientist na
516 Data Scientist 212-331 Job Description\nTitle: Sports Data Scientist\... 4.5 Smith Hanley Associates Washington, DC New York, 061 1 to 50 employees Company - Private Staffing & Outsourcing ... 40 1 0 0 0 0 0 0 data scientist na
513 Manager, Field Application Scientist, Southeast 212-331 At 10x Genomics, accelerating our understandin... 4.2 10x Genomics Raleigh, NC Pleasanton, CA 501 to 1000 employees Company - Public Biotech & Pharmaceuticals ... 8 0 1 0 0 0 0 0 manager na
499 Senior Principal Data Scientist (Python/R) 212-331 Roche Diagnostics has built a new strategic ar... 4.1 Roche Pleasanton, CA Basel, Switzerland 10000+ employees Company - Public Biotech & Pharmaceuticals ... 124 1 1 1 1 0 0 1 data scientist senior
500 Real World Science, Data Scientist 212-331 Title: Real World Science, Data Scientist\nLoc... 4.0 AstraZeneca Wilmington, DE Cambridge, United Kingdom 10000+ employees Company - Public Biotech & Pharmaceuticals ... 107 1 0 1 0 0 0 0 data scientist na
511 Data Scientist 212-331 Role: Data ScientistÂ\n\nLocation: Washington,... 4.0 Comtech Global Inc Washington, DC Columbus, OH 51 to 200 employees Company - Private Health, Beauty, & Fitness ... -1 1 0 1 1 1 1 0 data scientist na
514 COMPUTER SCIENTIST - ENGINEER - RESEARCH COMPU... 212-331 Join the exciting new area of developing the a... 3.9 Southwest Research Institute Dayton, OH San Antonio, TX 1001 to 5000 employees Nonprofit Organization Research & Development ... 73 1 1 0 0 0 0 0 na na
507 ENGINEER - COMPUTER SCIENTIST - RESEARCH COMPU... 212-331 Join our Defense and Intelligence Solutions Di... 3.9 Southwest Research Institute Oklahoma City, OK San Antonio, TX 1001 to 5000 employees Nonprofit Organization Research & Development ... 73 1 1 0 0 0 0 0 na na
512 Data Scientist 212-331 The work is in support of targeting and watchl... 3.8 Aveshka, Inc. Washington, DC Arlington, VA 51 to 200 employees Company - Public IT Services ... 10 1 1 0 1 0 0 1 data scientist na
501 Data Scientist 212-331 Position: Data Scientist\nLocation: Denver\nSt... 3.6 Creative Circle United States Los Angeles, CA 201 to 500 employees Company - Public Staffing & Outsourcing ... 18 1 0 0 0 0 0 0 data scientist na
506 Data Scientist 212-331 Ke`aki Technologies is looking for a qualified... 3.6 Alaka`ina Foundation Family of Companies Fort Sam Houston, TX Honolulu, HI 501 to 1000 employees Government -1 ... -1 1 0 0 0 0 0 0 data scientist na
517 Scientist - Machine Learning 212-331 Scientist 1 - Machine Learning - Cell Science\... 3.5 Allen Institute Seattle, WA Seattle, WA 201 to 500 employees Nonprofit Organization Research & Development ... 17 0 0 0 0 0 0 0 mle na
519 Data Scientist 212-331 Aptive is seeking a Data Scientist to compile ... 3.5 Aptive Washington, DC Alexandria, VA 51 to 200 employees Company - Private Consulting ... 8 1 1 0 0 0 1 0 data scientist na
505 Data Scientist 212-331 Please review the job details below.\nWe are l... 3.5 Maxar Technologies Herndon, VA Westminster, CO 5001 to 10000 employees Company - Public Aerospace & Defense ... -1 1 0 1 1 0 1 0 data scientist na
503 Senior Data Scientist 212-331 Do you have a head for numbers? Like turning r... 3.5 Maxar Technologies Herndon, VA Westminster, CO 5001 to 10000 employees Company - Public Aerospace & Defense ... -1 1 1 0 0 0 0 1 data scientist senior
518 Data Scientist 212-331 Description\n\nThe Data Scientist will be part... 2.7 1-800-Flowers New York, NY Carle Place, NY 1001 to 5000 employees Company - Public Wholesale ... 44 1 1 0 0 0 1 0 data scientist na
508 Data Scientist 212-331 JOB DESCRIPTION:\n\nHexagon US Federal is look... 2.7 Hexagon US Federal Lexington Park, MD Chantilly, VA 51 to 200 employees Company - Public Aerospace & Defense ... 10 0 1 0 0 1 0 0 data scientist na

19 rows × 27 columns

Näitame kõige vähem palka pakkuvaid ettevõtteid ja sorteerime Rating'u järgi:

In [ ]:
filtered_bottom=df[(df['avg_salary'] < 50) & (df['Rating'] > 0)].sort_values(['Rating'], ascending=False)
display(filtered_bottom)
Job Title Salary Estimate Job Description Rating Company Name Location Headquarters Size Type of ownership Industry ... company_age python excel hadoop spark aws tableau big_data job_simp seniority
477 Data Scientist - TS/SCI FSP or CI Required 31-56 US Citizenship Required and (TS/SCI with FSP o... 5.0 Phoenix Operations Group Annapolis Junction, MD Woodbine, MD 1 to 50 employees Company - Private IT Services ... 9 1 0 0 1 0 0 1 data scientist na
476 Data Scientist 31-56 Join us in making roads safer by making driver... 4.9 Cambridge Mobile Telematics Cambridge, MA Cambridge, MA 1 to 50 employees Company - Private Enterprise Software & Network Solutions ... 10 1 0 0 1 1 0 0 data scientist na
471 Data Scientist 31-56 What will you be doing?\n\nThe Contractor shal... 4.7 Praxis Engineering Chantilly, VA Annapolis Junction, MD 201 to 500 employees Subsidiary or Business Segment Computer Hardware & Software ... 18 0 0 0 0 1 0 1 data scientist na
467 Data Scientist 31-56 Job Title : Data Scientist\n\nJob Location : W... 4.5 Radiant Digital Washington, DC New York, NY 1 to 50 employees Company - Private -1 ... -1 1 1 1 1 0 0 0 data scientist na
475 Decision Scientist 31-56 Are you passionate about Decision Science?\n\n... 4.5 Johns Hopkins University Applied Physics Labor... Laurel, MD Laurel, MD 5001 to 10000 employees Nonprofit Organization Aerospace & Defense ... 78 0 1 0 1 0 0 0 na na
469 Data Scientist 31-56 Responsibilities·\nAnalyze large datasets to g... 4.1 IntelliPro Group Inc. Santa Clara, CA Santa Clara, CA 201 to 500 employees Company - Private -1 ... -1 1 1 1 0 0 0 0 data scientist na
464 Data Scientist 31-56 Data Scientist\n\nTrexquant is a systematic he... 4.0 Trexquant Investment United States Stamford, CT 1 to 50 employees Company - Private Investment Banking & Asset Management ... 8 1 0 0 0 0 0 0 data scientist na
459 Data Scientist 31-56 As a member of the Data Science team, this rol... 4.0 Grid Dynamics Santa Clara, CA San Ramon, CA 1001 to 5000 employees Company - Private Enterprise Software & Network Solutions ... 14 1 1 0 0 0 1 1 data scientist na
473 Data Scientist 31-56 Job Description\n\n\nJob #: 1068351\n\nOur cli... 3.9 Apex Systems San Francisco, CA Glen Allen, VA 1001 to 5000 employees Subsidiary or Business Segment Staffing & Outsourcing ... 25 0 0 0 0 0 0 0 data scientist na
460 VP, Data Science 31-56 We are looking for a VP, Data Science to lead ... 3.9 7Park Data New York, NY New York, NY 51 to 200 employees Company - Private Research & Development ... 8 1 1 0 1 0 0 0 na na
462 Data Scientist 31-56 The Perduco Group, a LinQuest company, is seek... 3.9 LinQuest Colorado Springs, CO Los Angeles, CA 501 to 1000 employees Company - Private Aerospace & Defense ... 16 1 1 0 0 0 1 0 data scientist na
461 Senior Business Intelligence Analyst 31-56 Position Overview:\n\nThe Senior Business Inte... 3.7 Protolabs Maple Plain, MN Maple Plain, MN 1001 to 5000 employees Company - Public Miscellaneous Manufacturing ... 21 0 0 0 0 0 0 0 analyst senior
466 Data Scientist 31-56 Job Introduction\n\n\nFLEETCOR is seeking a Da... 3.7 Fleetcor Atlanta, GA Atlanta, GA 5001 to 10000 employees Company - Public Financial Transaction Processing ... 20 1 1 0 0 0 1 0 data scientist na
463 Data Engineer - Kafka 31-56 Western Digital®\n\nThe next big thing in\ndat... 3.5 Western Digital San Jose, CA San Jose, CA 10000+ employees Company - Public Computer Hardware & Software ... 50 1 0 1 1 1 0 0 data engineer na
472 Data Scientist 31-56 General Description: The Data Scientist will s... 3.4 USI Southfield, MI Saint Paul, MN 1001 to 5000 employees Company - Private Construction ... 22 1 1 0 0 0 0 0 data scientist na
458 Data & Machine Learning Scientist 31-56 Passionate about precision medicine and advanc... 3.3 Tempus Labs Chicago, IL Chicago, IL 501 to 1000 employees Company - Private Biotech & Pharmaceuticals ... 5 1 0 0 0 1 0 0 mle na
465 Data Scientist 31-56 Deepen understanding and usage of data across ... 3.0 Ameritas Life Insurance Corp Cincinnati, OH Lincoln, NE 1001 to 5000 employees Company - Private Insurance Agencies & Brokerages ... 133 0 0 0 0 0 0 0 data scientist na
474 Data Scientist 31-56 Company overview:\nFor more than three decades... 2.9 Pragmatics, Inc. United States Reston, VA 201 to 500 employees Company - Private IT Services ... 35 1 0 1 0 1 0 0 data scientist na
470 In-Line Inspection Data Analyst 31-56 In-Line Inspection Data Analyst\n\nQuest Integ... 2.9 Quest Integrity Tulsa, OK Kent, WA 201 to 500 employees Company - Public Oil & Gas Services ... 24 0 1 0 0 0 0 0 analyst na
468 Data Scientist 31-56 Child Care Aware® of America, our nation’s lea... 2.8 Child Care Aware of America Arlington, VA Arlington, VA 51 to 200 employees Nonprofit Organization Social Assistance ... 33 1 1 0 0 1 0 0 data scientist na

20 rows × 27 columns

Leiame keskmised hinnangutele erinevates kategooriates:

In [ ]:
top_avg_rate=round(filtered_top['Rating'].mean(), 4)
bottom_avg_rate=round(filtered_bottom['Rating'].mean(), 4)
df=df[(df['Rating'] > 0)]
all_avg_rate=round(df['Rating'].mean(), 4)
print(f"Top palgamaksjate keskmine Rating: {top_avg_rate}")
print(f"Väikseimate palgamaksjate keskmine Rating: {bottom_avg_rate}")
print(f"Kõikide ettevõtete keskmine Rating: {all_avg_rate}")

Top palgamaksjate keskmine Rating: 3.8684
Väikseimate palgamaksjate keskmine Rating: 3.83
Kõikide ettevõtete keskmine Rating: 3.8815

Siit võib järldada, et ettevõtte hinnang ei ole seotud palganumbri suurusega, vaid peaks vaatama pigem töökultuuri, ülemuste suhtumist või veel midagi kolmandat üldse.

Kuidas hinnangud ja palgad sõltuvad ettevõtte suurusest?¶

Uurin, mis suurustega ettevõtted on csv failis.

In [ ]:
df.Size.unique()
Out[ ]:
array(['1001 to 5000 employees', '5001 to 10000 employees',
       '501 to 1000 employees', '51 to 200 employees', '10000+ employees',
       '201 to 500 employees', '1 to 50 employees', 'Unknown'],
      dtype=object)

Eemaldan 'Unknown' väärtustega kirjed ja arvutan välja ettevõtete suuruste järgi hinnangute keskmised:

In [ ]:
df2=df[(df['Size'] != 'Unknown')]
df2.Size = df2.Size.str.strip('_ employees')
size_categories=df2.Size.unique()
print('Töötajate arv:')
for i in size_categories:
    filtered_size=df2[(df2['Size'] == i)]
    rating=round(filtered_size['Rating'].mean(), 4)
    print(f"{i}: \n - Keskmine hinnang: {rating}")

Töötajate arv:
1001 to 5000: 
 - Keskmine hinnang: 3.6183
5001 to 10000: 
 - Keskmine hinnang: 3.7279
501 to 1000: 
 - Keskmine hinnang: 3.7325
51 to 200: 
 - Keskmine hinnang: 4.0128
10000+: 
 - Keskmine hinnang: 3.7038
201 to 500: 
 - Keskmine hinnang: 4.0012
1 to 50: 
 - Keskmine hinnang: 4.3535

C:\Users\kaare\AppData\Local\Temp\ipykernel_15840\788928302.py:2: 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
  df2.Size = df2.Size.str.strip('_ employees')

Nende andmete järgi meeldib inimestele töödata pigem väikestes ettevõtetes, kus:

1-50 töötajaga ettevõtete keskmine hinnang oli 4.3535;

51-200 ja 201-500 töötajaga ettevõtetel vastavalt 4.0128 ja 4.0012;

501-1000, 1001-5000, 5001-10000 ja 10000+ töötajaga ettevõtetel vastavalt 3.7325, 3.6183, 3.7279, 3.7038.

Kuvan tulemused ka boxplot graafikuga seaborn teeki kasutades.

In [ ]:
catplot = sns.catplot(
    data=df2,
    x="Size", y="Rating",
    kind="box"
)
catplot.set_xticklabels(rotation=50)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x21f09d45c90>

Jooniselt on näha, et väiksemates ettevõtetes meeldib inimestele rohkem tööd teha kui suurettevõtetes.

Kas tegevusvaldkond mõjutab hinnangut?¶

Uurin, kas tegevusvaldkonnas esineb kirjeid, mis tuleks kustutada.

In [ ]:
df.Industry.unique()
Out[ ]:
array(['Insurance Carriers', 'Research & Development', 'Consulting',
       'Electrical & Electronic Manufacturing', 'Advertising & Marketing',
       'Computer Hardware & Software', 'Biotech & Pharmaceuticals',
       'Consumer Electronics & Appliances Stores',
       'Enterprise Software & Network Solutions', 'IT Services', 'Energy',
       'Chemical Manufacturing', 'Federal Agencies',
       'Health Care Services & Hospitals', 'Internet',
       'Investment Banking & Asset Management', 'Aerospace & Defense',
       'Utilities', '-1', 'Express Delivery Services',
       'Staffing & Outsourcing', 'Insurance Agencies & Brokerages',
       'Consumer Products Manufacturing', 'Industrial Manufacturing',
       'Food & Beverage Manufacturing', 'Banks & Credit Unions',
       'Video Games', 'Shipping', 'Telecommunications Services',
       'Lending', 'Cable, Internet & Telephone Providers', 'Real Estate',
       'Venture Capital & Private Equity', 'Miscellaneous Manufacturing',
       'Oil & Gas Services', 'Transportation Equipment Manufacturing',
       'Telecommunications Manufacturing', 'Transportation Management',
       'News Outlet', 'Architectural & Engineering Services',
       'Food & Beverage Stores', 'Other Retail Stores',
       'Hotels, Motels, & Resorts', 'State & Regional Agencies',
       'Financial Transaction Processing', 'Timber Operations',
       'Colleges & Universities', 'Travel Agencies', 'Accounting',
       'Logistics & Supply Chain', 'Farm Support Services',
       'Social Assistance', 'Construction',
       'Department, Clothing, & Shoe Stores', 'Publishing',
       'Health, Beauty, & Fitness', 'Wholesale', 'Rail'], dtype=object)

Filtreerime välja ettevõtted, millel valdkonda pole märgitud, ehk on valdkonnaks pandud '-1'.

In [ ]:
industry=df[(df['Industry'] != '-1')]
industries=industry.Industry.unique()
display(industries)

array(['Insurance Carriers', 'Research & Development', 'Consulting',
       'Electrical & Electronic Manufacturing', 'Advertising & Marketing',
       'Computer Hardware & Software', 'Biotech & Pharmaceuticals',
       'Consumer Electronics & Appliances Stores',
       'Enterprise Software & Network Solutions', 'IT Services', 'Energy',
       'Chemical Manufacturing', 'Federal Agencies',
       'Health Care Services & Hospitals', 'Internet',
       'Investment Banking & Asset Management', 'Aerospace & Defense',
       'Utilities', 'Express Delivery Services', 'Staffing & Outsourcing',
       'Insurance Agencies & Brokerages',
       'Consumer Products Manufacturing', 'Industrial Manufacturing',
       'Food & Beverage Manufacturing', 'Banks & Credit Unions',
       'Video Games', 'Shipping', 'Telecommunications Services',
       'Lending', 'Cable, Internet & Telephone Providers', 'Real Estate',
       'Venture Capital & Private Equity', 'Miscellaneous Manufacturing',
       'Oil & Gas Services', 'Transportation Equipment Manufacturing',
       'Telecommunications Manufacturing', 'Transportation Management',
       'News Outlet', 'Architectural & Engineering Services',
       'Food & Beverage Stores', 'Other Retail Stores',
       'Hotels, Motels, & Resorts', 'State & Regional Agencies',
       'Financial Transaction Processing', 'Timber Operations',
       'Colleges & Universities', 'Travel Agencies', 'Accounting',
       'Logistics & Supply Chain', 'Farm Support Services',
       'Social Assistance', 'Construction',
       'Department, Clothing, & Shoe Stores', 'Publishing',
       'Health, Beauty, & Fitness', 'Wholesale', 'Rail'], dtype=object)

Kuvame boxplot graafikul hinnangud tegevusvaldkonna kaupa.

In [ ]:
plot = sns.catplot(data=industry, x="Industry", y="Rating", kind="box", aspect=2)
plot.set_xticklabels(rotation=90)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x21f0a0138d0>

Vastavalt joonisele saame välja filtreerida tegevusvaldkonnad, kus inimestele meeldib rohkem töödata.

In [ ]:
result=[]
for i in industries:
    filtered_industry=df[(df['Industry'] == i)]
    rating=round(filtered_industry['Rating'].mean(), 1)
    if rating > 4:
        result.append([rating, i])
result.sort(reverse=True)

t = PrettyTable(['Rating', 'Industry'])
for i in result:
    t.add_row(i)
print(t)

+--------+-----------------------------------------+
| Rating |                 Industry                |
+--------+-----------------------------------------+
|  4.7   |                Publishing               |
|  4.6   |  Transportation Equipment Manufacturing |
|  4.5   |               Video Games               |
|  4.4   |     Venture Capital & Private Equity    |
|  4.4   |                 Lending                 |
|  4.3   |       Computer Hardware & Software      |
|  4.2   |                Accounting               |
|  4.1   |          Staffing & Outsourcing         |
|  4.1   |               IT Services               |
|  4.1   | Enterprise Software & Network Solutions |
+--------+-----------------------------------------+

Saame välja tuua ka valdkonnad, kus inimestele ei meeldi töödata.

In [ ]:
result=[]
for i in industries:
    filtered_industry=df[(df['Industry'] == i)]
    rating=round(filtered_industry['Rating'].mean(), 1)
    if rating < 3.5:
        result.append([rating, i])
result.sort()

t = PrettyTable(['Rating', 'Industry'])
for i in result:
    t.add_row(i)
print(t)

+--------+---------------------------------------+
| Rating |                Industry               |
+--------+---------------------------------------+
|  2.6   |               Wholesale               |
|  2.8   |                  Rail                 |
|  2.8   |           Social Assistance           |
|  2.9   | Cable, Internet & Telephone Providers |
|  2.9   |           Oil & Gas Services          |
|  3.0   |        Colleges & Universities        |
|  3.1   |        Logistics & Supply Chain       |
|  3.2   |       Express Delivery Services       |
|  3.2   |       State & Regional Agencies       |
|  3.2   |      Telecommunications Services      |
|  3.3   |    Insurance Agencies & Brokerages    |
|  3.3   |              News Outlet              |
|  3.3   |    Telecommunications Manufacturing   |
|  3.3   |           Timber Operations           |
|  3.4   |         Chemical Manufacturing        |
|  3.4   |              Construction             |
|  3.4   |                Shipping               |
+--------+---------------------------------------+

Kuidas mõjutab valdkond palgataset?¶

Kuvame boxplot graafikul keskmised töötasud valdkonna kaupa.

In [ ]:
plot = sns.catplot(data=industry, x="Industry", y="avg_salary", kind="box", aspect=2)
plot.set_xticklabels(rotation=90)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x21f0b8344d0>