Kaggle Courses - Basic Data Exploration

folder_open Kaggle / Courses / Intro to Machine Learning | date_range last modified at / created at 25 Oct 2024

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맨 위로

• 이전 글
• Using Pandas to Get Familiar With Your Data
• 데이터 분석
• Exercises
  • Step 1: Loading Data
  • Step 2: Review The Data

이전 글

Kaggle Courses - How Models Work

Using Pandas to Get Familiar With Your Data

먼저 데이터와 친숙해져보자. Pandas 라이브러리를 사용할 것이다.

import pandas as pd

pandas 라이브러리의 가장 중요한 부분은 DataFrame이다. 엑셀의 sheet, SQL database의 table과 같은 타입을 의미한다.

이제 데이터를 살펴보자. 오스트레일리아 멜버른의 집값에 관한 데이터이다. (data about home prices in Melbourne, Australia.)

# save filepath to variable for easier access
melbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'
# read the data and store data in DataFrame titled melbourne_data
melbourne_data = pd.read_csv(melbourne_file_path) 
# print a summary of the data in Melbourne data
melbourne_data.describe()

결과는 직접 실행하여 살펴보자.

데이터 분석

먼저 count는 누락되지 않은 행의 수를 알려준다. 누락값은 다양한 이유가 있다. 예를 들어, 침실이 하나인 주택을 조사할 때 두 번째 침실의 크기가 수집되지 않았을 때 등이다.

두 번째 값인 mean 은 평균을 의미한다. std는 수치적으로 얼마나 분포되어 있는지 측정하는 표준 편차이다. 그 다음의 퍼센트에이지에 따라 각 수치값이 오름차순이다. max가 가장 큰 값이다.

Exercises

Step 1: Loading Data

Read the Iowa data file into a Pandas DataFrame called home_data.

import pandas as pd

# Path of the file to read
iowa_file_path = '../input/home-data-for-ml-course/train.csv'

# Fill in the line below to read the file into a variable home_data
home_data = ____

# Call line below with no argument to check that you've loaded the data correctly
step_1.check()

정답

import pandas as pd

# Path of the file to read
iowa_file_path = '../input/home-data-for-ml-course/train.csv'

# Fill in the line below to read the file into a variable home_data
home_data = pd.read_csv(iowa_file_path)

# Call line below with no argument to check that you've loaded the data correctly
step_1.check()

Step 2: Review The Data

Use the command you learned to view summary statistics of the data. Then fill in variables to answer the following questions

# Print summary statistics in next line
____

# What is the average lot size (rounded to nearest integer)?
avg_lot_size = ____

# As of today, how old is the newest home (current year - the date in which it was built)
newest_home_age = ____

# Checks your answers
step_2.check()

정답

# Print summary statistics in next line
home_data.describe()

# What is the average lot size (rounded to nearest integer)?
avg_lot_size = round(home_data['LotArea'].mean())

# As of today, how old is the newest home (current year - the date in which it was built)
newest_home_age = 2024 - home_data['YearBuilt'].max()

# Checks your answers
step_2.check()