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Python으로 하는 탐색적 데이터 분석

George Boorman

Curriculum Manager, DataCamp

상관관계

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

Heatmap showing 0.54 Pearson correlation coefficient between Price and Duration

데이터 유형 확인하기

print(planes.dtypes)

Airline                    object
Date_of_Journey    datetime64[ns]
Source                     object
Destination                object
Route                      object
Dep_Time           datetime64[ns]
Arrival_Time       datetime64[ns]
Duration                  float64
Total_Stops                object
Additional_Info            object
Price                     float64
dtype: object

경유 횟수

print(planes["Total_Stops"].value_counts())

1 stop      4107
non-stop    2584
2 stops     1127
3 stops       29
4 stops        1
Name: Total_Stops, dtype: int64

경유 횟수 정리하기

planes["Total_Stops"] = planes["Total_Stops"].str.replace(" stops", "")

planes["Total_Stops"] = planes["Total_Stops"].str.replace(" stop", "")

planes["Total_Stops"] = planes["Total_Stops"].str.replace("non-stop", "0")

planes["Total_Stops"] = planes["Total_Stops"].astype(int)

상관관계

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

Heatmap showing 0.62 Pearson correlation coefficient between Price and Total Stops and 0.74 correlation between Duration and Total Stops

날짜

print(planes.dtypes)

Airline                    object
Date_of_Journey    datetime64[ns]
Source                     object
Destination                object
Route                      object
Dep_Time           datetime64[ns]
Arrival_Time       datetime64[ns]
Duration                  float64
Total_Stops                 int64
Additional_Info            object
Price                     float64
dtype: object

월과 요일 추출하기

planes["month"] = planes["Date_of_Journey"].dt.month

planes["weekday"] = planes["Date_of_Journey"].dt.weekday

print(planes[["month", "weekday", "Date_of_Journey"]].head())

   month  weekday   Date_of_Journey
0      9        4        2019-09-06
1     12        3        2019-12-05
2      1        3        2019-01-03
3      6        0        2019-06-24
4     12        1        2019-12-03

출발 및 도착 시간

planes["Dep_Hour"] = planes["Dep_Time"].dt.hour
planes["Arrival_Hour"] = planes["Arrival_Time"].dt.hour

상관관계

Heatmap showing no relationship between datetime attributes and price

범주 만들기

print(planes["Price"].describe())

count     7848.000000
mean      9035.413609
std       4429.822081
min       1759.000000
25%       5228.000000
50%       8355.000000
75%      12373.000000
max      54826.000000
Name: Price, dtype: float64

구간	티켓 유형
<= 5228	이코노미
> 5228 <= 8355	프리미엄 이코노미
> 8335 <= 12373	비즈니스
> 12373	퍼스트

기술 통계

twenty_fifth = planes["Price"].quantile(0.25)

median = planes["Price"].median()

seventy_fifth = planes["Price"].quantile(0.75)

maximum = planes["Price"].max()

레이블과 빈

labels = ["Economy", "Premium Economy", "Business Class", "First Class"]

bins = [0, twenty_fifth, median, seventy_fifth, maximum]

pd.cut()

Call pd-dot-cut

planes["Price_Category"] = pd.cut(

pd.cut()

Pass the data

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

pd.cut()

Set the labels

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

pd.cut()

Provide the bins

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

가격 범주

print(planes[["Price","Price_Category"]].head())

     Price   Price_Category
0  13882.0      First Class
1   6218.0  Premium Economy
2  13302.0      First Class
3   3873.0          Economy
4  11087.0   Business Class

항공사별 가격 범주

sns.countplot(data=planes, x="Airline", hue="Price_Category")
plt.show()

항공사별 가격 범주

Countplot showing the number of flights per airline in different price categories, with Jet Airways having the largest number of First Class tickets

연습해 봅시다!

Python으로 하는 탐색적 데이터 분석