[Python] Pandas 복제, 결측치 실습

단원별 심화 연습 문제¶

In [1]:

!pip install seaborn==0.13.0

Defaulting to user installation because normal site-packages is not writeable
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Installing collected packages: seaborn
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[notice] A new release of pip available: 22.2.2 -> 24.1.1
[notice] To update, run: pip install --upgrade pip

In [2]:

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

# set floating point formatting
pd.options.display.float_format = '{:,.6f}'.format

Q3¶

범위

• (이전 범위 포함)
• 결측치 채우기

In [3]:

import seaborn as sns

titanic = sns.load_dataset('titanic')
titanic.head()

Out[3]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.000000	1	0	7.250000	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.000000	1	0	71.283300	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.000000	0	0	7.925000	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.000000	1	0	53.100000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.000000	0	0	8.050000	S	Third	man	True	NaN	Southampton	no	True

각 컬럼별 결측치를 출력하세요

In [5]:

# 코드를 입력해 주세요
titanic.isnull().sum()

Out[5]:

survived         0
pclass           0
sex              0
age            177
sibsp            0
parch            0
fare             0
embarked         2
class            0
who              0
adult_male       0
deck           688
embark_town      2
alive            0
alone            0
dtype: int64

[출력 결과]

survived         0
pclass           0
sex              0
age            177
sibsp            0
parch            0
fare             0
embarked         2
class            0
who              0
adult_male       0
deck           688
embark_town      2
alive            0
alone            0
dtype: int64

age 컬럼의 결측 데이터를 다음의 조건에 맞도록 채워 주세요

• who가 man인 데이터에서 age가 결측치인 데이터의 값을 남자 나이의 median값으로 결측치를 채워 주세요
• who가 woman인 데이터에서 age가 결측치인 데이터의 값을 여자 나이의 25% Quantile값으로 결측치를 채워 주세요
• who가 child인 데이터에서 age가 결측치인 데이터의 값을 아이 나이의 평균값으로 결측치를 채워 주세요

In [6]:

# 코드를 입력해 주세요
df = titanic
# 각 그룹별 통계값 계산
man_median_age = df[df['who'] == 'man']['age'].median()
woman_quantile_age = df[df['who'] == 'woman']['age'].quantile(0.25)
child_mean_age = df[df['who'] == 'child']['age'].mean()

# 결측치 채우기
df.loc[(df['who'] == 'man') & (df['age'].isna()), 'age'] = man_median_age
df.loc[(df['who'] == 'woman') & (df['age'].isna()), 'age'] = woman_quantile_age
df.loc[(df['who'] == 'child') & (df['age'].isna()), 'age'] = child_mean_age

In [7]:

# 검증코드
print(f"결측치: {titanic['age'].isnull().sum()}")
print(f"age mean: {titanic['age'].mean():.4f}")

결측치: 0
age mean: 29.3425

[출력 결과]

결측치: 0
age mean: 29.3425

제출¶

제출을 위해 새로 로드된 타이타닉 데이터셋에서 age 컬럼의 결측치를 다음 조건에 맞춰 채운 결과를 result_df에 저장하세요.

• who가 man인 데이터에서 age가 결측치인 데이터의 값을 남자 나이의 median값으로 결측치를 채워 주세요
• who가 woman인 데이터에서 age가 결측치인 데이터의 값을 여자 나이의 25% Quantile값으로 결측치를 채워 주세요
• who가 child인 데이터에서 age가 결측치인 데이터의 값을 아이 나이의 평균값으로 결측치를 채워 주세요

In [10]:

titanic = sns.load_dataset('titanic')
df = titanic
# 각 그룹별 통계값 계산
man_median_age = df[df['who'] == 'man']['age'].median()
woman_quantile_age = df[df['who'] == 'woman']['age'].quantile(0.25)
child_mean_age = df[df['who'] == 'child']['age'].mean()

# 결측치 채우기
df.loc[(df['who'] == 'man') & (df['age'].isna()), 'age'] = man_median_age
df.loc[(df['who'] == 'woman') & (df['age'].isna()), 'age'] = woman_quantile_age
df.loc[(df['who'] == 'child') & (df['age'].isna()), 'age'] = child_mean_age
result_df = df
df.head()

Out[10]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.000000	1	0	7.250000	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.000000	1	0	71.283300	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.000000	0	0	7.925000	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.000000	1	0	53.100000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.000000	0	0	8.050000	S	Third	man	True	NaN	Southampton	no	True