DataFrame“数据”
temp = data[(data['block_name']=='坪山') & (data['community_id']!=186)][['community_id','block_name','dstrct_name','month_name','avg_price']]\
.loc[data['month_name'].isin(data[(data['community_id']==186) & (data['avg_price'].isnull())]['month_name'])]\
.groupby(['month_name']).agg(np.mean).reset_index()
temp
输出：
month_name avg_price
0 2017-06 24796.0
1 2017-07 25169.5
2 2017-08 24253.0
3 2017-09 25838.0
4 2017-10 26275.0
5 2017-11 26592.0
6 2017-12 26783.0
现在我要使用上面的结果来填充下一个：
data.loc[(data['community_id']==186) & (data['avg_price'].isnull()),['month_name','avg_price']]
其输出：
month_name avg_price
70 2017-06 NaN
71 2017-07 NaN
72 2017-08 NaN
73 2017-09 NaN
74 2017-10 NaN
75 2017-11 NaN
76 2017-12 NaN
我尝试这样做：
data.loc[(data['community_id']==186) & (data['avg_price'].isnull()),'avg_price'] = data[(data['block_name']=='坪山') & (data['community_id']!=186)][['community_id','block_name','dstrct_name','month_name','avg_price']]\
.loc[dd['month_name'].isin(data[(data['community_id']==186) & (data['avg_price'].isnull())]['month_name'])]\
.groupby(['month_name']).agg(np.mean).reset_index()['avg_price']
然后我跑步
data.loc[(data['community_id']==186) & (data['avg_price'].isnull()),['month_name','avg_price']]
再次发现它不起作用
month_name avg_price
70 2017-06 NaN
71 2017-07 NaN
72 2017-08 NaN
73 2017-09 NaN
74 2017-10 NaN
75 2017-11 NaN
76 2017-12 NaN
我只想更改“数据”。