A B C A 0 1.5 13 B 0.5 100.2 7.3 C 1.3 34 0.01
To this table I want to replace by several criteria, but only the first replacement works:
df[df<1]='N' # Works df[(df>1)&(df<10)]#='L' # Doesn't work df[(df>10)&(df<50)]='M' # Doesn't work df[df>50]='H' # Doesn't work
If I instead do the selection for the 2nd line based on
float, still doesn't work:
((df.applymap(type)==float) & (df<10) & (df>1)) #Doesn't work
I was wondering how to apply
pd.DataFrame().mask in here, or any other way. How should I solve this?
Alternatively, I know I may read column by column and apply the substitutions on each series, but this seems a bit counter productive
Edit: Could anyone explain why the 4 simple assignments above do not work?
m1 = df < 1 m2 = (df>1)&(df<10) m3 = (df>10)&(df<50) m4 = df>5 vals = list('NLMH') df = pd.DataFrame(np.select([m1,m2,m3,m4], vals), index=df.index, columns=df.columns) print (df) A B C A N L M B N H L C L M N