我有一个包含数百列的数据框，我需要查看所有列名。我做了什么:In[37]:data_all2.columns输出是:Out[37]:Index(['customer_id','incoming','outgoing','awan','bank','family','food','government','internet','isipulsa',...'overdue_3months_feature78','overdue_3months_feature79','overdue_3months_feature80','overdue_3months_feature81','overdu
我有一个包含数百列的数据框，我需要查看所有列名。我做了什么:In[37]:data_all2.columns输出是:Out[37]:Index(['customer_id','incoming','outgoing','awan','bank','family','food','government','internet','isipulsa',...'overdue_3months_feature78','overdue_3months_feature79','overdue_3months_feature80','overdue_3months_feature81','overdu
我有以下数据框timeXYX_t0X_tp0X_t1X_tp1X_t2X_tp200.0028760100NaNNaNNaNNaNNaN10.0029860100NaN0NaNNaNNaN20.03736711011.0000000NaN0NaN30.03737421020.50000011.0000000NaN40.03738931030.33333320.50000011.00000050.03739341040.25000030.33333320.500000....10303089.9622132562682560.0000002560.0039062550.003922103
我有以下数据框timeXYX_t0X_tp0X_t1X_tp1X_t2X_tp200.0028760100NaNNaNNaNNaNNaN10.0029860100NaN0NaNNaNNaN20.03736711011.0000000NaN0NaN30.03737421020.50000011.0000000NaN40.03738931030.33333320.50000011.00000050.03739341040.25000030.33333320.500000....10303089.9622132562682560.0000002560.0039062550.003922103
我想修改一个pandasMultiIndexDataFrame，使每个索引组都包含指定范围之间的日期。我希望每个组用值0(或NaN)填写缺失的日期2013-06-11到2013-12-31。GroupA,GroupB,Date,Valueloc_agroup_a2013-06-11222013-07-02352013-07-09142013-07-3092013-08-0642013-09-03402013-10-0118group_b2013-07-0942013-08-0622013-09-035group_c2013-07-0912013-09-032loc_bgroup_a20
我想修改一个pandasMultiIndexDataFrame，使每个索引组都包含指定范围之间的日期。我希望每个组用值0(或NaN)填写缺失的日期2013-06-11到2013-12-31。GroupA,GroupB,Date,Valueloc_agroup_a2013-06-11222013-07-02352013-07-09142013-07-3092013-08-0642013-09-03402013-10-0118group_b2013-07-0942013-08-0622013-09-035group_c2013-07-0912013-09-032loc_bgroup_a20
我有一个从数据库加载数据的数据框df。大多数列是json字符串，而有些甚至是json列表。例如:idnamecolumnAcolumnB1John{"dist":"600","time":"0:12.10"}[{"pos":"1st","value":"500"},{"pos":"2nd","value":"300"},{"pos":"3rd","value":"200"},{"pos":"total","value":"1000"}]2Mike{"dist":"600"}[{"pos":"1st","value":"500"},{"pos":"2nd","value":"300"},
我有一个从数据库加载数据的数据框df。大多数列是json字符串，而有些甚至是json列表。例如:idnamecolumnAcolumnB1John{"dist":"600","time":"0:12.10"}[{"pos":"1st","value":"500"},{"pos":"2nd","value":"300"},{"pos":"3rd","value":"200"},{"pos":"total","value":"1000"}]2Mike{"dist":"600"}[{"pos":"1st","value":"500"},{"pos":"2nd","value":"300"},
我想将df.groupby()与apply()结合使用，将函数应用于每组的每一行。我通常使用以下代码，它通常可以工作(注意，这是没有groupby()):df.apply(myFunction,args=(arg1,))使用groupby()我尝试了以下操作:df.groupby('columnName').apply(myFunction,args=(arg1,))但是，我收到以下错误:TypeError:myFunction()gotanunexpectedkeywordargument'args'因此，我的问题是:如何将groupby()和apply()与需要参数的函数一起使用？
我想将df.groupby()与apply()结合使用，将函数应用于每组的每一行。我通常使用以下代码，它通常可以工作(注意，这是没有groupby()):df.apply(myFunction,args=(arg1,))使用groupby()我尝试了以下操作:df.groupby('columnName').apply(myFunction,args=(arg1,))但是，我收到以下错误:TypeError:myFunction()gotanunexpectedkeywordargument'args'因此，我的问题是:如何将groupby()和apply()与需要参数的函数一起使用？
