제곧내

1. json.loads() method string을 포함한 JSON 데이터를 파이썬 오브젝트 set으로 변환해줌 2. json.load() function To read a file from JSON format json.loads() : for loading JSON data from a string ("loads" is short for "load string") json.load() : for loading from a file object 3. Pandas의 pandas.read_json() 4. Pandas의 pandas.DataFrame( ) contructor - JSON의 dictionary 리스트를 (바로 통과시켜서) dataframe으로 변환함 pass the list of dict..

Data.gov - A directory of government data downloads /r/datasets - A subreddit that has hundreds of interesting data sets Awesome datasets - A list of data sets hosted on GitHub rs.io - A great blog post with hundreds of interesting data sets 출처 : dataquest

https://github.com/dataquestio/loan-prediction

1. re module: built-in module for regular expressions 2. re.search() function - The regex pattern / The string we want to search that pattern for - a set by placing the characters we want to match for in square brackets [ ] 예시 ) 예시2) string_list = ["Julie's favorite color is Blue.", "Keli's favorite color is Green.", "Craig's favorite colors are blue and red."] blue_mentions = 0 pattern = "[Bb]l..

(1) Series.map( ) Series.map(function_name) 이런 식으로 function 적용시키는 method임 (2) Series.apply() def label(element, x): if element > x: return 'High' else: return 'Low' economy_impact_apply = happiness2015['Economy'].apply(label, x=0.8) 이런 식으로 어떤 Series에 element-wise하게 함수 적용시키고 싶을 때 apply method를 쓴다. 단, apply 메소드는 vectorization이 없을 때만 쓸 것! 왜냐면 pandas uses vectorization, the process of applying opera..

1. for 문으로 리스트 내의 값을 순차적으로 하나씩 불러와서 누적하여 더하기 (for 반복문으로 평균 구하기) 2. for 문으로 distribution 구하기 3.

1. 수직 바그래프 Series.plot.bar() method 관련 문서 : https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.bar.html 2.수평 바그래프 Series.plot.barh() test = wnba['Exp_ordinal'].value_counts().iloc[[3,0,2,1,4]] test.plot.barh() 3. 파이 그래프 Series.plot.pie() 예를 들어, wnba['Pos'].value_counts().plot.pie() wnba['Exp_ordinal'].value_counts().plot.pie(figsize =(6,6), autopct = '%.2f%%', title='P..

(1) pd.concat( [ df1, df2 ] , ignore_index=True ) (2) pd.merge( left = , right= , on = '컬럼이름', how = 'left' ) Inner: only includes elements that appear in both dataframes with a common key [디폴트] Outer: includes all data from both dataframes Left: includes all of the rows from the "left" dataframe along with any rows from the "right" dataframe with a common key; the result retains all columns fro..