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Remove the lines in Dataframe satisfying the condition
📁 python, pandas, jupyter
How to remove the lines in Pandas Dataframe, in which the values in the column
Details:
I am engaged in data processing in data.Stp.remove (1005072) , where a seven -digit number is an array index, but everything is in vain.
code:
Cols = [ 'ttt' , 'ik05' , 'ik06' , 'ik07' , 'ik08' , 'ik10' , , 'ik14' , 'ik16' , 'ik20' , , 'DA06' , 'DA07' , 'DA08' , , 'DA11' , 'DA14' , 'da16' , , 'gz1' , 'gz2' , 'gz3' , 'gz4' , '' ''" "" "" "" 'lld' , 'stp' ] def lasread (fname): data pd.read_table (fname, deelim_whitespace = true , na_values = '-999.25' , index_col = false ) retu data [cols] data = none for name in laas: print (name) if data no : : : : Data = lasread (name) else : data = pd.concat ([data, lasread (name)]) ndata = data.asas_matrix (columns = none ) df = data [cols] df1 = df. grampby ( 'stp' ) df1.head () dfp = pd.dataframe () for name, group in df1: k, p = stats.mstats.normaltest (group [span class = ""> 5 : 5 ]) dfp [name] = pd = dfp.transpose () d.columns = colsgoodindex = d [D.ik20 & gt; 0.05 ] [D.da20 & gt; 0.05 ]. Index - rejection by foster ** Data.Stp.remove ( 1005072 ) ** filteDData = Data [cols] [Data.stp == - 39 ] for f in goodindex: a = data.stp == F] FILThedDATA = PD.Concat ([FILThedData, A]) print (filledData) 
