学习pandas-索引操作

客官°小女子只卖身不卖艺 2023-06-07 05:57 31阅读 0赞

import pandas as pd
    df1 = pd.DataFrame(
            { 'var1':1.0,
             'var2':[1,2,3,4],
             'var3':['test','tran','test','tran'],
             'var4':'cons'},
             index = ['a','b','c','d']        
            )
    
    
    df2 = pd.read_csv('123.csv',encoding='utf-8',index_col='店家')

# 设置索引 #

‘’’  
df.set\_index(  
keys:colums be assigned as index, composite index need list type  
drop = True:if or not delet the columns after creat index  
append = False:if or not add index on the base of original index,replace is default  
inplace = False:if or not directly modify original df  
)  
‘’’

df2_new = df2.set_index('地址')
    df2_new1 = df2.set_index('地址',append = True,drop = False)
    print(df2_new1)

# restore index to columns #

‘’’  
df.reset\_index(  
drop = False:if or not delet index directly,do not restore to columns  
inplace = False:if or not directly modify original df  
level = None:specify restore which level to columns,for composite index  
)  
‘’’

df2_copy = df2.copy()
    df2_copy.set_index('电话',inplace = True,append=True)
    '''print(df2_copy)'''
    
    #df2_copy.reset_index(inplace = True)
    
    '''print(df2_copy.reset_index(level = ['电话']))'''
    print(df2_copy.index)

# modify index names #

‘’‘df2\_copy.index.names = \[\]’’’

# modify index values (replace all in essense) #

df1.index = \[‘a’,‘b’,‘c’,‘new’\]  
print(df1.index)

# forced index change #

‘’’  
df.reindex(  
labels:Like array structure,to rebuild index  
axis: for which axis to rebuild  
(‘index’,‘columns’) or number (0,1)  
copy=True:create nem object,do not change original df  
level:levels to be rebuild  
\#missing value  
method:  
pad/ffill(use the foregoing data );backfill/bfill(use back data);nearest(use nearist data)  
fill\_value = np.NaN:(value to fill in )  
limit = None:max\_step size to find value to fill in

)  
‘’’

print(df1.reindex())

print(df1.reindex(\[‘a’,‘b’,‘c’,‘u’\],fill\_value=‘jon\_skeet’))