범주형 데이터 변환 및 분석하기

Python으로 하는 탐색적 데이터 분석

George Boorman

Curriculum Manager, DataCamp

데이터 미리보기

print(salaries.select_dtypes("object").head())

  Designation                 Experience    Employment_Status    Employee_Location    Company_Size
0 Data Scientist              Mid           FT                   DE                   L
1 Machine Learning Scientist  Senior        FT                   JP                   S
2 Big Data Engineer           Senior        FT                   GB                   M
3 Product Data Analyst        Mid           FT                   HN                   S
4 Machine Learning Engineer   Senior        FT                   US                   L

직무명

print(salaries["Designation"].value_counts())

Data Scientist                              143
Data Engineer                               132
Data Analyst                                 97
Machine Learning Engineer                    41
Research Scientist                           16
Data Science Manager                         12
Data Architect                               11
Big Data Engineer                             8
Machine Learning Scientist                    8
...

직무명

print(salaries["Designation"].nunique())

직무명

Bar plot displaying the five most popular data roles - data scientist, data engineer, data analyst, machine learning engineer, and research scientist

범주에서 값 추출하기

현재 형식은 인사이트 도출에 한계가 있습니다
pandas.Series.str.contains()
- 열에서 특정 문자열 또는 여러 문자열을 검색합니다

salaries["Designation"].str.contains("Scientist")

0       True
1       True
2      False
3      False
       ...  
604    False
605    False
606     True
Name: Designation, Length: 607, dtype: bool

문자열에서 여러 구문 찾기

관심 단어: Machine Learning 또는 AI

salaries["Designation"].str.contains("Machine Learning|AI")

0      False
1       True
2      False
3      False
       ...  
604    False
605    False
606     True
Name: Designation, Length: 607, dtype: bool

문자열에서 여러 구문 찾기

관심 단어: Data로 시작하는 모든 단어

salaries["Designation"].str.contains("^Data")

0       True
1      False
2      False
3      False
       ...  
604     True
605     True
606    False
Name: Designation, Length: 607, dtype: bool

문자열에서 여러 구문 찾기

job_categories = ["Data Science", "Data Analytics", 
                  "Data Engineering", "Machine Learning",
                  "Managerial", "Consultant"]

문자열에서 여러 구문 찾기

data_science = "Data Scientist|NLP"

data_analyst = "Analyst|Analytics"

data_engineer = "Data Engineer|ETL|Architect|Infrastructure"

ml_engineer = "Machine Learning|ML|Big Data|AI"

manager = "Manager|Head|Director|Lead|Principal|Staff"

consultant = "Consultant|Freelance"

문자열에서 여러 구문 찾기

conditions = [
    (salaries["Designation"].str.contains(data_science)),

    (salaries["Designation"].str.contains(data_analyst)),

    (salaries["Designation"].str.contains(data_engineer)),

    (salaries["Designation"].str.contains(ml_engineer)),

    (salaries["Designation"].str.contains(manager)),

    (salaries["Designation"].str.contains(consultant))
]

범주형 열 생성하기

salaries["Job_Category"] =

범주형 열 생성하기

salaries["Job_Category"] = np.select(conditions,

범주형 열 생성하기

salaries["Job_Category"] = np.select(conditions, 
                                     job_categories,

범주형 열 생성하기

salaries["Job_Category"] = np.select(conditions, 
                                     job_categories, 
                                     default="Other")

직무 범주 미리보기

print(salaries[["Designation", "Job_Category"]].head())

                  Designation      Job_Category
0              Data Scientist      Data Science
1  Machine Learning Scientist  Machine Learning
2           Big Data Engineer  Data Engineering
3        Product Data Analyst    Data Analytics
4   Machine Learning Engineer  Machine Learning

직무 범주 빈도 시각화하기

sns.countplot(data=salaries, x="Job_Category")

plt.show()

Bar plot displaying the count of jobs by category

연습해 봅시다!

Python으로 하는 탐색적 데이터 분석