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Python으로 하는 탐색적 데이터 분석

George Boorman

Curriculum Manager, DataCamp

검사 및 검증

a histogram of book ratings

books["year"] = books["year"].astype(int)
books.dtypes

name       object
author     object
rating    float64
year        int64
genre      object
dtype: object

집계

books.groupby("genre").agg(
    mean_rating=("rating", "mean"),
    std_rating=("rating", "std"),
    median_year=("year", "median")
)

|  genre      | mean_rating | std_rating | median_year |
|-------------|-------------|------------|-------------|
|   Childrens |    4.780000 |   0.122370 |      2015.0 |
|     Fiction |    4.570229 |   0.281123 |      2013.0 |
| Non Fiction |    4.598324 |   0.179411 |      2013.0 |

결측 데이터 처리하기

print(salaries.isna().sum())

Working_Year            12
Designation             27
Experience              33
Employment_Status       31
Employee_Location       28
Company_Size            40
Remote_Working_Ratio    24
Salary_USD              60
dtype: int64

결측 데이터 처리하기

결측값 삭제

평균, 중앙값, 최빈값 대치

하위 그룹별로 대치

salaries_dict = salaries.groupby("Experience")["Salary_USD"].median().to_dict()
salaries["Salary_USD"] = salaries["Salary_USD"].fillna(salaries["Experience"].map(salaries_dict))

범주형 데이터 분석

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

Bar plot displaying the count of jobs by category

람다 함수 적용

Apply a lambda function

salaries["std_dev"] = salaries.groupby("Experience")["Salary_USD"].transform(lambda x: x.std())

이상치 처리하기

sns.boxplot(data=salaries,
            y="Salary_USD")
plt.show()

Box plot of salaries for data professionals, showing the 25th percentile at the bottom of the box, the 50th percentile as the middle line, and the 75th percentile at the top of the box

시간에 따른 패턴

sns.lineplot(data=divorce, x="marriage_month", y="marriage_duration")
plt.show()

A line plot showing the relationship between month of marriage and marriage duration

상관관계

sns.heatmap(divorce.corr(numeric_only=True), annot=True)
plt.show()

A heat map of divorce correlations

분포

sns.kdeplot(data=divorce, x="marriage_duration", hue="education_man", cut=0)
plt.show()

marriage duration kde with hue set to education_man and cut equal to zero

교차표

pd.crosstab(planes["Source"], planes["Destination"],
            values=planes["Price"], aggfunc="median")

Destination  Banglore   Cochin   Delhi  Hyderabad  Kolkata  New Delhi
Source                                                               
Banglore          NaN      NaN  4823.0        NaN      NaN    10976.5
Chennai           NaN      NaN     NaN        NaN   3850.0        NaN
Delhi             NaN  10262.0     NaN        NaN      NaN        NaN
Kolkata        9345.0      NaN     NaN        NaN      NaN        NaN
Mumbai            NaN      NaN     NaN     3342.0      NaN        NaN

pd.cut()

Provide the bins

planes["Price_Category"] = pd.cut(planes["Price"],
                                  labels=labels,
                                  bins=bins)

데이터 스누핑

Heatmap with correlation coefficient scores for each number of stops

가설 생성하기

sns.barplot(data=planes, x="Airline", y="Duration")
plt.show()

Bar plot of duration versus airline

다음 단계

Python으로 살펴보는 표본추출(Sampling)

Python으로 배우는 가설 검정

scikit-learn으로 배우는 지도 학습

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Python으로 하는 탐색적 데이터 분석