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<!DOCTYPE HTML>
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<title>범죄데이터로 파이썬 실습 (Lamda) · GitBook</title>
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<ul class="summary">
<li class="chapter " data-level="1.1" data-path="./">
<a href="./">
Introduction
</a>
</li>
<li class="chapter " data-level="1.2" data-path="Tutorial_180628_Git_and_Github.html">
<a href="Tutorial_180628_Git_and_Github.html">
Git과 Github, 기본 개념과 설명
</a>
</li>
<li class="chapter " data-level="1.3" data-path="Tutorial_180629_Git_with_constitution.html">
<a href="Tutorial_180629_Git_with_constitution.html">
헌법개정안으로 깃베쉬, 소스트리, 브랜치 이해하기
</a>
</li>
<li class="chapter " data-level="1.4" data-path="Tutorial_180629_Github_practice_Statistics1.html">
<a href="Tutorial_180629_Github_practice_Statistics1.html">
확률통계 기초와 깃허브 실습
</a>
</li>
<li class="chapter " data-level="1.5" data-path="Tutorial_180629_Statistics.html">
<a href="Tutorial_180629_Statistics.html">
통계 기본 개념과 설명
</a>
</li>
<li class="chapter " data-level="1.6" data-path="Tutorial_180702_Programming_Intro.html">
<a href="Tutorial_180702_Programming_Intro.html">
스크래치 실습을 통한 프로그래밍 맛보기
</a>
</li>
<li class="chapter " data-level="1.7" data-path="Tutorial_180702_tidydata.html">
<a href="Tutorial_180702_tidydata.html">
데이터다루기(tidydata)와 프로그래밍기초
</a>
</li>
<li class="chapter " data-level="1.8" data-path="Tutorial_180703_Python_introduction.html">
<a href="Tutorial_180703_Python_introduction.html">
파이썬 기초
</a>
</li>
<li class="chapter active" data-level="1.9" data-path="Tutorial_180705_PythonReview_Lamda.html">
<a href="Tutorial_180705_PythonReview_Lamda.html">
범죄데이터로 파이썬 실습 (Lamda)
</a>
</li>
<li class="chapter " data-level="1.10" data-path="Tutorial_180705_Resume_01.html">
<a href="Tutorial_180705_Resume_01.html">
특강-자기소개서 워크숍(1)
</a>
</li>
<li class="chapter " data-level="1.11" data-path="Tutorial_180706_Civic_hacking_seminar.html">
<a href="Tutorial_180706_Civic_hacking_seminar.html">
특강-시빅해킹
</a>
</li>
<li class="chapter " data-level="1.12" data-path="Tutorial_180709_StaticBlogging_JekyllandRuby.html">
<a href="Tutorial_180709_StaticBlogging_JekyllandRuby.html">
지킬과 루비로 정적 블로그 만들기
</a>
</li>
<li class="chapter " data-level="1.13" data-path="Tutorial_180710_Lecture_Cooperation.html">
<a href="Tutorial_180710_Lecture_Cooperation.html">
특강-협업
</a>
</li>
<li class="chapter " data-level="1.14" data-path="Tutorial_180710_Lecture_Speciality.html">
<a href="Tutorial_180710_Lecture_Speciality.html">
특강-전문성
</a>
</li>
<li class="chapter " data-level="1.15" data-path="Tutorial_180712_DataVisualization101.html">
<a href="Tutorial_180712_DataVisualization101.html">
데이터 시각화 이해
</a>
</li>
<li class="chapter " data-level="1.16" data-path="Tutorial_180712_AttraciveResume2.html">
<a href="Tutorial_180712_AttraciveResume2.html">
특강-자기소개서 워크숍(2)
</a>
</li>
<li class="chapter " data-level="1.17" data-path="Tutorial_180713_Pandas101.html">
<a href="Tutorial_180713_Pandas101.html">
자료의 요약 과제를 통한 판다스 실습
</a>
</li>
<li class="chapter " data-level="1.18" data-path="Tutorial_180713_ExperimentDesignLecture.html">
<a href="Tutorial_180713_ExperimentDesignLecture.html">
특강-실험계획에 관해 알아보기
</a>
</li>
<li class="chapter " data-level="1.19" data-path="Tutorial_180716_Crawling_Shuffle.html">
<a href="Tutorial_180716_Crawling_Shuffle.html">
저작권과 직업윤리를 인지하고 크롤링 셔플 실습
</a>
</li>
<li class="chapter " data-level="1.20" data-path="Tutorial_180716_Markup_Html5lib.html">
<a href="Tutorial_180716_Markup_Html5lib.html">
Markup Html5lib, 파이썬으로 크롤링하기
</a>
</li>
<li class="chapter " data-level="1.21" data-path="Tutorial_180717_10minPandas.html">
<a href="Tutorial_180717_10minPandas.html">
Pandas 10분 완성
</a>
</li>
<li class="chapter " data-level="1.22" data-path="Tutorial_180717_PandasPetition.html">
<a href="Tutorial_180717_PandasPetition.html">
국민청원 첫시작 판다스로 국민청원하기
</a>
</li>
<li class="chapter " data-level="1.23" data-path="Tutorial_180719_BeautifulSoup.html">
<a href="Tutorial_180719_BeautifulSoup.html">
Beautiful Soup을 사용하여 크롤링
</a>
</li>
<li class="chapter " data-level="1.24" data-path="Tutorial_180719_plotnine.md">
<span>
국민청원 데이터 시각화와 자연어 처리-plontnine 실습하기
</a>
</li>
<li class="chapter " data-level="1.25" data-path="Tutorial_180720_coupang.html">
<a href="Tutorial_180720_coupang.html">
특강 - 비전공자가 데이터 분석가로 취업하기
</a>
</li>
<li class="chapter " data-level="1.26" data-path="Tutorial_180720_ProbabilityDistribution.html">
<a href="Tutorial_180720_ProbabilityDistribution.html">
통계학-자료의 요약, 확률분포(ProbabilityDistribution)
</a>
</li>
<li class="chapter " data-level="1.27" data-path="Tutorial_180723_Machine_learning.html">
<a href="Tutorial_180723_Machine_learning.html">
기계학습의 기초(지도학습/비지도학습/머신러닝)
</a>
</li>
<li class="chapter " data-level="1.28" data-path="Tutorial_180723_word_vectorsization.html">
<a href="Tutorial_180723_word_vectorsization.html">
텍스트 데이터 시각화 Word_vectorsization
</a>
</li>
<li class="chapter " data-level="1.29" data-path="Tutorial_180726_naver.html">
<a href="Tutorial_180726_naver.html">
특강 - AI R&D Director
</a>
</li>
<li class="chapter " data-level="1.30" data-path="Tutorial_180726_library.md">
<span>
전국도서관표준데이터 분석
</a>
</li>
<li class="chapter " data-level="1.31" data-path="Tutorial_180730_Categorizing_v2.html">
<a href="Tutorial_180730_Categorizing_v2.html">
국민청원 카테고리 분류하기
</a>
</li>
<li class="chapter " data-level="1.32" data-path="Tutorial_180730_Kaggle_NLP_v2.html">
<a href="Tutorial_180730_Kaggle_NLP_v2.html">
Kaggle NLP로 예측율 높이기
</a>
</li>
<li class="chapter " data-level="1.33" data-path="Tutorial_180730_Statistics.html">
<a href="Tutorial_180730_Statistics.html">
Hypothesis test
</a>
</li>
<li class="chapter " data-level="1.34" data-path="Tutorial_180731_MTPlanning.html">
<a href="Tutorial_180731_MTPlanning.html">
데잇걸즈 MT 계획으로 애자일 프로세스 실습해보기
</a>
</li>
<li class="chapter " data-level="1.35" data-path="Tutorial_180802_ZigZag.html">
<a href="Tutorial_180802_ZigZag.html">
특강 - 쇼핑몰 데이터 분석 이야기
</a>
</li>
<li class="chapter " data-level="1.36" data-path="Tutorial_180803_GettingJobGithub.html">
<a href="Tutorial_180803_GettingJobGithub.html">
깃허브로 취업하기
</a>
</li>
<li class="chapter " data-level="1.37" data-path="Tutorial_180803_Statistics4.html">
<a href="Tutorial_180803_Statistics4.html">
회귀분석(Linear regression)
</a>
</li>
<li class="chapter " data-level="1.38" data-path="Tutorial_180806_Folium_practice.md">
<span>
공공데이터 상권정보 분석해 보기
</a>
</li>
<li class="chapter " data-level="1.39" data-path="Tutorial_180806_Geolocatin_API_practice.md">
<span>
서울창업허브(공덕역) 맛집지도
</a>
</li>
<li class="chapter " data-level="1.40" data-path="Tutorial_180806_XGBoost.md">
<span>
XGBoost 분산형 그래디언트 부스팅 알고리즘
</a>
</li>
<li class="chapter " data-level="1.41" data-path="Tutorial_180807_Git_review.html">
<a href="Tutorial_180807_Git_review.html">
깃과 깃헙 복습, 다른 사람의 레파지토리에 기여하기
</a>
</li>
<li class="chapter " data-level="1.42" data-path="Tutorial_180810_Apt_analysis_Statistics5.html">
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아파트 분양가 분석 및 회귀분석2
</a>
</li>
<li class="chapter " data-level="1.43" data-path="Tutorial_180813_kaggle_Titanic.html">
<a href="Tutorial_180813_kaggle_Titanic.html">
스프레드시트로 캐글 타이타닉 참가하기
</a>
</li>
<li class="chapter " data-level="1.44" data-path="Tutorial_180814_Python_Class.html">
<a href="Tutorial_180814_Python_Class.html">
객체지향 프로그래밍
</a>
</li>
<li class="chapter " data-level="1.45" data-path="Tutorial_180814_Test_Driven_Development.html">
<a href="Tutorial_180814_Test_Driven_Development.html">
테스트 주도 개발
</a>
</li>
<li class="chapter " data-level="1.46" data-path="Tutorial_180817_Colors_in_Data_Visualization.md">
<span>
데이터 시각화와 색의 활용
</a>
</li>
<li class="chapter " data-level="1.47" data-path="Tutorial_180817_Statistics5.html">
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통계-회귀분석3
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<a href="." >범죄데이터로 파이썬 실습 (Lamda)</a>
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<h1 id="today-we-learned">Today We Learned</h1>
<h2 id="20180705-thu-cloud">2018/07/05 thu :cloud:</h2>
<h3 id="python-정리하기">python 정리하기</h3>
<blockquote>
<p><strong>수업 목표</strong></p>
<blockquote>
<p>범죄 통계 데이터 분석 과제 점검<br>함수와 이름 공간 복습<br>Lambda 란?<br>과제 실습 1 (딕셔너리 - 평균 구하기)<br>과제 실습 2 (분산, 표준편차 구하기) </p>
</blockquote>
</blockquote>
<p>'재귀함수' = 영화 Inception </p>
<ul>
<li></li>
<li>함수 속 함수 = 꿈 속의 꿈</li>
<li>def <strong>_</strong> 별 단계 = 꿈</li>
<li>return result<ul>
<li>return은 킥 (꿈속에서 벗어나는 계기)</li>
<li>result는 토템 (꿈 속에서 갖고 탈출)</li>
</ul>
</li>
</ul>
<h1 id="강력범죄-데이터">강력범죄 데이터</h1>
<pre><code>%matplotlib inline
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
df = pd.read_csv('https://s3.ap-northeast-2.amazonaws.com/data10902/messy/crime_clean.csv',encoding='utf-8')
data = [d for d in df.get_values().tolist() if not math.isnan(d[-1])]
</code></pre><ul>
<li>데이터를 불러온다.</li>
</ul>
<pre><code>df.groupby(["젠더", "유형"]).agg({"명":"sum"})
</code></pre><ul>
<li>젠더별 유형(가해자,피해자) 명 수의 합을 표로 볼 수 있다.</li>
</ul>
<pre><code>df.groupby(["젠더", "대분류","유형"]).agg({"명":"mean"})
</code></pre><ul>
<li>젠더-대분류-유형을 그룹화하여 명 수의 평균을 볼 수 있다.</li>
</ul>
<pre><code>def get_sum(data):
result = 0
for datum in data:
result = result + datum
return result
def get_len(data):
result = 0
for datum in data:
result = result + 1
return result
def get_average(data):
total = get_sum(data)
n = get_len(data)
result = total / n
return result
score = [50, 60, 70]
average = get_average(score)
print(average)
</code></pre><p><strong>Lambda</strong>
-
"한 줄 짜리 간단한 함수를 정의하는 간결한 문법" </p>
<p>lambda 는</p>
<ul>
<li>Second-order function(2차 함수)</li>
<li>함수 속 함수 </li>
</ul>
<p>리스트 이름 순으로 정렬하기</p>
<pre><code>students = [('alan', 50), ('dave', 60), ('brad', 30), ('cate', 40)]
def by_name(student):
return student[0]
#0번째 element 기준으로 정렬하겠다
#이름 순으로 정렬됨
sorted(students, key=by_name)
>>> [('alan',50),('brad',30),('cate',40),('dave',60)]
#위 함수를 한 줄로 정리하는 방법 (1회용)
sorted(students, key=lambda s:s[0])
# 리스트 함수명따로지정x 변수:표현식
</code></pre><p>리스트 나이 순으로 정렬하기</p>
<pre><code>def by_age(student):
return student[1]
sorted(students, key=by_age)
#위와 같은 결과 나오는 lambda함수
sorted(students, key = lambda s:s[1])
>>> [('brad',30),('cate',40),('alan',50),('dave',60)]
</code></pre><p>더 쉽게</p>
<pre><code>def blah (x) :
return x+2
blah = (lambda x : x+2)
같은 값이 나오는 함수
</code></pre><h1 id="실습-1-40m">실습 #1 40m</h1>
<ul>
<li>당신은 농구팀 코치이며, 네 명의 지원자 중 한 명을 추가로 선발하고자 합니다. 다음은 각 지원자 별 최근 열 번 경기에서의 득점 기록입니다.<pre><code>candidates = {
'alan': [8, 14, 6, 8, 14, 9, 14, 9, 15, 5],
'brad': [11, 4, 11, 7, 9, 7, 8, 7, 10, 6],
'cate': [16, 22, 13, 15, 12, 3, 20, 17, 13, 23],
'dave': [24, 15, 18, 12, 9, 19, 23, 13, 14, 18],
}
</code></pre></li>
<li><p>평균 득점이 가장 높은 선수를 선발하고자 합니다. 어떤 선수를 선발해야 하는지, 그 이유는 무엇인지 설명하는 보고서를 작성하세요.</p>
</li>
<li><p>힌트: 자료의 요약 수업 내용을 참고하세요.
```
average = []</p>
</li>
</ul>
<p>for name in candidates.keys():
total = 0
n = 0
for i in candidates[name]:
total += i
n += 1
average.append(total/n)</p>
<p>print(average)</p>
<p>lst = list(zip(candidates.keys(), average))
print(lst)</p>
<pre><code>```
sorted_averages = sorted(named_averages, key=lambda s: s[1], reverse=True)
print(sorted_averages)
</code></pre><pre><code>[('dave', 16.5), ('cate', 15.4), ('alan', 10.2), ('brad', 8.0)]
</code></pre><ul>
<li>평균이 제일 높은 dave를 택한다.</li>
</ul>
<h1 id="실습-2-40m">실습 #2 40m</h1>
<ul>
<li>당신은 농구팀 코치이며, 네 명의 지원자 중 한 명을 추가로 선발하고자 합니다. 다음은 각 지원자 별 최근 경기에서의 득점 기록 일부를 임의로 추출한 데이터입니다. 선수별로 경기 횟수가 다릅니다.<pre><code>candidates = {
'alan': [8, 14, 6, 8, 14, 9, 14, 9, 15, 5],
'brad': [11, 4, 11, 7, 9, 7, 8, 7, 6],
'cate': [16, 22, 15, 12, 3, 20, 17, 13, 23],
'dave': [24, 15, 18, 18, 12, 9, 19, 23, 13, 14, 18],
}
</code></pre></li>
<li><p>매 경기에서의 기복이 가장 적은 선수를 선발하고자 합니다. 어떤 선수를 선발해야 하는지, 그 이유는 무엇인지 설명하는 보고서를 작성하세요.</p>
</li>
<li><p>힌트: 자료의 요약 수업 내용을 참고하세요.</p>
</li>
</ul>
<pre><code>import math
candidates = {
'alan': [8, 14, 6, 8, 14, 9, 14, 9, 15, 5],
'brad': [11, 4, 11, 7, 9, 7, 8, 7, 6],
'cate': [16, 22, 15, 12, 3, 20, 17, 13, 23],
'dave': [24, 15, 18, 18, 12, 9, 19, 23, 13, 14, 18],
}
sd = []
for name, scores in candidates.items():
# 합계 구하기
total = sum(scores)
# value의 전체 개수 구하기
n = len(scores)
# 평균구하기
mean = total / n
# 분산구하기
vsum = 0
for x in scores:
vsum = vsum + (x - mean)**2 #
var = vsum / n-1
# 표준편차 구하기
std = math.sqrt(var)
sd.append(std)
named_sd = list(zip(candidates.keys(), sd))
print(named_sd)
</code></pre><pre><code>[('alan', 3.370459909270543), ('brad', 1.9019158631804098), ('cate', 5.647024782032472), ('dave', 4.237768007566061)]
</code></pre><ul>
<li>로 표준편차가 작은 brad를 택해야 한다.</li>
</ul>
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