Python,Pandasで削除する方法(2)は、サンプルコードがあります

drop_duplicates()

df.drop_duplicates() ， 。 ， 。
：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html#pandas.DataFrame.drop_duplicates

DataFrame.drop_duplicates(subset=None, keep='first', inplace=False)
Return DataFrame with duplicate rows removed, optionally only considering certain columns.
#           df，          

Parameters:	
subset : column label or sequence of labels, optional
subset：             
Only consider certain columns for identifying duplicates, by default use all of the columns
#                ，          

keep : {‘first’, ‘last’, False}, default ‘first’（  'first'）
first : Drop duplicates except for the first occurrence.
#             。
last : Drop duplicates except for the last occurrence.
#     (         )。
False : Drop all duplicates.
#        （     ）
inplace : boolean, default False
Whether to drop duplicates in place or to return a copy
#             

dic = {'A': [1, 2, 1, 1], 'B': [2, 5, 2, 4], 
       'C': [3, 5, 3, 10], 'D': [4, 9, 4, 5]}
df = pd.DataFrame(dic, index=['one', 'two', 'three', 'four'])
print(df)
       A  B   C  D
one    1  2   3  4
two    2  5   5  9
three  1  2   3  4
four   1  4  10  5


#                （     ）
df.drop_duplicates()
#           (     )
df.drop_duplicates(subset='A')
#              （      ）
df.drop_duplicates(keep='last')
#       （    ）
df.drop_duplicates(keep=False)
#             
print(df)
#inplace=True ，         
df.drop_duplicates(inplace=True)
print(df)

dropna()

df.dropna ， 。 ， ， 。
：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html#pandas.DataFrame.dropna

DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)
Remove missing values.
     
  
  Parameters:	
axis : {0 or ‘index’, 1 or ‘columns’}, default 0（  0）
Determine if rows or columns which contain missing values are removed.
#             
0, or ‘index’ : Drop rows which contain missing values.
#0  'index'，        
1, or ‘columns’ : Drop columns which contain missing value.
#1  'columns'，        
    
how : {‘any’, ‘all’}, default ‘any’(  'any'
Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.
#            ，      
‘any’ : If any NA values are present, drop that row or column.
#       ，        
‘all’ : If all values are NA, drop that row or column.
#               ，       

thresh : int, optional
Require that many non-NA values.
#         （thresh  ） na   。

subset : array-like, optional
Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.
#           ，        ，            。
inplace : bool, default False
If True, do operation inplace and return None.
   True，         

df = pd.DataFrame({'name': ['Alfred', 'Batman', 'Catwoman', np.nan],
                  'toy': [np.nan, 'Batmobile', 'Bullwhip', np.nan],
                  'born': [pd.NaT, pd.Timestamp('1940-06-25'), pd.NaT, np.nan]})

print(df)

#            
df.dropna()
#           
df.dropna(axis=1)
df.dropna(axis='columns')
#            
df.dropna(how='all')
#              
df.dropna(subset=['name', 'toy'])
#           ，inplace=True        
print(df)
df.dropna(inplace=True)