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software_development:python_pandas [2022/08/04 14:48]
prgram [rename]
software_development:python_pandas [2023/05/16 15:17] (current)
prgram [encoding_errors - 'ignore']
Line 1: Line 1:
 ====== python pandas ====== ====== python pandas ======
 {{INLINETOC}} {{INLINETOC}}
 +
 +=== etc : list ===
 +<code python>
 +set( [list] ) # unique value
 +[list].sort() #​자동적용?​
 +[list1] + [list2] ​ #​list합치기
 +</​code>​
  
 ===== shape of df ===== ===== shape of df =====
Line 20: Line 27:
 <code python> <code python>
 df.groupby([컬럼들]).agg({'​컬럼':​sum}).reset_index() df.groupby([컬럼들]).agg({'​컬럼':​sum}).reset_index()
 +
 +df.groupby([COLUMNS])['​COLUMN'​].max().reset_index()
  
 df = df.assign(date=pd.to_numeric(df['​date'​],​ errors='​coerce'​)).groupby(['​코드',​ '​종목명'​]).agg({'​date':​np.min}).reset_index().drop_duplicates() df = df.assign(date=pd.to_numeric(df['​date'​],​ errors='​coerce'​)).groupby(['​코드',​ '​종목명'​]).agg({'​date':​np.min}).reset_index().drop_duplicates()
Line 59: Line 68:
 df = df.sort_index(axis='​columns',​ level = '​MULTILEVEL INDEX NAME/​no'​) df = df.sort_index(axis='​columns',​ level = '​MULTILEVEL INDEX NAME/​no'​)
 #2 #2
 +df.columns
 col_order = ['​a','​b','​c'​] col_order = ['​a','​b','​c'​]
 df = df.reindex(col_order,​ axis='​columns'​) df = df.reindex(col_order,​ axis='​columns'​)
Line 97: Line 107:
 iloc: Select by position iloc: Select by position
 loc: Select by label loc: Select by label
 +  ​
 +df.loc[:,​~df.columns.isin(['​a','​b'​])]  ​
 +
 +df[~( df['​a'​].isin(['​1','​2','​3'​]) & df['​b'​]=='​3'​ )] #​row-wise
 +df.loc[~( df['​a'​].isin(['​1','​2','​3'​]) & df['​b'​]=='​3'​ ), 8] #​row-wise & column
 </​code>​ </​code>​
  
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   ​   ​
 =====I/O file===== =====I/O file=====
 +
 +=== encoding_errors - '​ignore'​===
 +Encoding 제대로 했는데도 안되면..
 +공공데이터가 이런 경우가 많음.
 +
 +Error tokenizing data. C error: EOF inside string starting at row 0 | 판다스 에러
 +https://​con2joa.tistory.com/​m/​60
 +quoting=csv.QUOTE_NONE 파라미터
 +
 +<code python>
 +import chardet
 +with open(file, '​rb'​) as rawdata:
 +    result = chardet.detect(rawdata.read(100000))
 +result
 +
 +
 +data = pd.read_csv( file, encoding='​cp949',​ encoding_errors='​ignore'​)
 +# on_bad_lines='​skip'​
 +# error_bad_lines=False
 +</​code>​
  
 === to_numberic === === to_numberic ===