[Python] Pandas 통계 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
from IPython.display import Image

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

Q2¶

범위

• (이전 범위 포함)
• 통계 및 연산

/mnt/elice/dataset/StudentsPerformance.csv파일을 읽어서 DataFrame 형식으로 출력합니다. (상위 5개 행을 출력 합니다.)

In [3]:

# 데이터셋 로드
import pandas as pd

students = pd.read_csv('/mnt/elice/dataset/StudentsPerformance.csv')
students.head()

Out[3]:

	gender	race/ethnicity	parental level of education	lunch	test preparation course	math score	reading score	writing score
0	female	group B	bachelor's degree	standard	none	72	72	74
1	female	group C	some college	standard	completed	69	90	88
2	female	group B	master's degree	standard	none	90	95	93
3	male	group A	associate's degree	free/reduced	none	47	57	44
4	male	group C	some college	standard	none	76	78	75

다음의 요구사항에 맞는 데이터프레임을 출력하세요

• students 데이터프레임의 race/ethnicity가 group B인 그룹을 필터합니다.
• gender가 male인 대상을 필터합니다.
• math score, reading score, writing score에 대한 평균(mean)과 표준편차(std)를 출력합니다.

In [5]:

# 코드를 입력해 주세요
group_b_students = students[students['race/ethnicity'] == 'group B']
group_b_male_students = group_b_students[group_b_students['gender'] == 'male']
result = group_b_male_students[['math score', 'reading score', 'writing score']].agg(['mean', 'std'])
result

Out[5]:

	math score	reading score	writing score
mean	65.930233	62.848837	60.220930
std	14.156928	14.695752	14.854907

[출력 결과]

	math score	reading score	writing score
mean	65.930233	62.848837	60.220930
std	14.156928	14.695752	14.854907

다음의 요구사항에 맞는 데이터프레임을 출력하세요

• students데이터프레임에서 gender가 male인 대상을 필터합니다.
• lunch가 standard 대상을 필터합니다.
• reading score가 80 이상인 데이터를 필터합니다.
• math score, reading score, writing score에 대한 요약통계(describe)를 출력합니다.

In [6]:

# 코드를 입력해 주세요
male_students = students[students['gender'] == 'male']
standard_lunch_male_students = male_students[male_students['lunch'] == 'standard']
filtered_students = standard_lunch_male_students[standard_lunch_male_students['reading score'] >= 80]
result = filtered_students[['math score', 'reading score', 'writing score']].describe()
result

Out[6]:

	math score	reading score	writing score
count	68.000000	68.000000	68.000000
mean	87.897059	85.470588	82.955882
std	6.388281	4.872924	6.391304
min	75.000000	80.000000	71.000000
25%	83.000000	82.000000	78.000000
50%	87.000000	84.000000	82.000000
75%	92.250000	87.000000	86.250000
max	100.000000	100.000000	100.000000

[출력 결과]

	math score	reading score	writing score
count	68.000000	68.000000	68.000000
mean	87.897059	85.470588	82.955882
std	6.388281	4.872924	6.391304
min	75.000000	80.000000	71.000000
25%	83.000000	82.000000	78.000000
50%	87.000000	84.000000	82.000000
75%	92.250000	87.000000	86.250000
max	100.000000	100.000000	100.000000

위의 표를 참고하여 math score, reading score, writing score의 IQR을 산정합니다.

• IQR(Inter Quantile Range) = ((3rd Quantile, 75%) - (1st Quantile, 25%)) * 1.5
  • IQR Bottom = (1st Quantile, 25%) - IQR
  • IQR Top: (3rd Quantile, 75%) + IQR

In [7]:

Image(url='https://img1.daumcdn.net/thumb/R800x0/?scode=mtistory2&fname=https%3A%2F%2Fblog.kakaocdn.net%2Fdn%2FcAqc6V%2FbtqyQLiddUd%2FiXQVu1nYTo2rx3Q8xZBqy0%2Fimg.png')

Out[7]:

위에 명시된 공식을 참고하여 IQR Bottom을 산출해 주세요

In [19]:

# 코드를 입력해 주세요
#IQR(Inter Quantile Range) = ((3rd Quantile, 75%) - (1st Quantile, 25%)) * 1.5
#IQR Bottom = (1st Quantile, 25%) - IQR
#IQR Top: (3rd Quantile, 75%) + IQR
IQR = (result.quantile(0.75) - result.quantile(0.25))*1.5
IQR_bottom = result.quantile(0.25) - IQR
IQR_bottom

Out[19]:

math score      49.647059
reading score   63.720588
writing score   49.955882
dtype: float64

[출력 결과]

math score       69.125
reading score    74.500
writing score    65.625
dtype: float64

위에 명시된 공식을 참고하여 IQR Top을 산출해 주세요

In [20]:

# 코드를 입력해 주세요
IQR_top = result.quantile(0.75) + IQR
IQR_top

Out[20]:

math score      112.588235
reading score    99.132353
writing score   104.073529
dtype: float64

[출력 결과]

math score       106.125
reading score     94.500
writing score     98.625
dtype: float64

제출¶

제출을 위해 새로 로드된 /mnt/elice/dataset/StudentsPerformance.csv 데이터셋에서 다음 요구사항에 맞는 데이터프레임을 result_df에 저장하세요.

• students데이터프레임에서 gender가 male인 대상을 필터합니다.
• lunch가 standard 대상을 필터합니다.
• reading score가 80 이상인 데이터를 필터합니다.
• math score, reading score, writing score에 대한 요약통계(describe)

In [24]:

students = pd.read_csv('/mnt/elice/dataset/StudentsPerformance.csv')
cond = students['gender'] == 'male'
cond2 = students['lunch'] == 'standard'
cond3 = students['reading score']>=80
filtered_students = students.loc[cond & cond2 & cond3, ['math score', 'reading score', 'writing score']]

result_df = filtered_students.describe()
result_df

Out[24]:

	math score	reading score	writing score
count	68.000000	68.000000	68.000000
mean	87.897059	85.470588	82.955882
std	6.388281	4.872924	6.391304
min	75.000000	80.000000	71.000000
25%	83.000000	82.000000	78.000000
50%	87.000000	84.000000	82.000000
75%	92.250000	87.000000	86.250000
max	100.000000	100.000000	100.000000

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