[英]how to add a dataframe with another dataframe and updated common values based on a column
my first data frame-我的第一个数据框-
df1 = pd.DataFrame({'CONTRACT':['Tom', 'nick', 'krish', 'jack'],
'Net_Qty':[20, 21, 19, 18]})
CONTRACT Net_Qty
0 Tom 20
1 nick 21
2 krish 19
3 jack 18
second data frame-第二个数据框-
df2 = pd.DataFrame({'CONTRACT':['Tom', 'nick', 'amit', 'joy'],
'Net_Qty':[30, 40, 45, 54]})
CONTRACT Net_Qty
0 Tom 30
1 nick 40
2 amit 45
3 joy 54
I want dataframe dataframe Like this (all values of df2 and uncommon values of df1)-我想要这样的数据框数据框(df2的所有值和df1的不常见值)-
CONTRACT Net_Qty
0 Tom 30
1 nick 40
2 krish 19
4 jack 18
2 amit 45
3 joy 54
I tried like this-我试过这样-
cols = list(df1.columns)
df1.loc[df1.CONTRACT.isin(
df2.CONTRACT), cols] = df2[cols]
print(df1)
but its not working fine.......但它不能正常工作.......
Can anyone please suggest a better way-任何人都可以请提出一个更好的方法 -
Use pd.concat
and drop_duplicates
:使用pd.concat
和drop_duplicates
:
out = pd.concat([df2, df1]).drop_duplicates('CONTRACT', ignore_index=True)
print(out)
# Output
CONTRACT Net_Qty
0 Tom 30
1 nick 40
2 amit 45
3 joy 54
4 krish 19
5 jack 18
I resolved it with this code我用这段代码解决了它
df1 = pd.DataFrame({'CONTRACT':['Tom', 'nick', 'krish', 'jack'],'Net_Qty':[20, 21, 19, 18]})
df2 = pd.DataFrame({'CONTRACT':['Tom', 'nick', 'amit', 'joy'],'Net_Qty':[30, 40, 45, 54]})
df_merge = pd.merge(df1, df2, on = 'CONTRACT', how = 'outer')
df_merge[['Net_Qty_x', 'Net_Qty_y']] = df_merge[['Net_Qty_x', 'Net_Qty_y']].replace({np.nan : None})
condition_list = [df_merge['Net_Qty_y'].values != None]
choice_list = [df_merge['Net_Qty_y']]
df_merge['Net_Qty'] = np.select(condition_list, choice_list, df_merge['Net_Qty_x'])
df_merge['Net_Qty'] = df_merge['Net_Qty'].astype(int)
df_merge = df_merge[['CONTRACT', 'Net_Qty']]
df_merge
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