I am trying to do the equivalent of the below commands in python:
test <- data.frame(convert_me=c('Convert1','Convert2','Convert3'),
values=rnorm(3,45, 12), age_col=c('23','33','44'))
test
library(reshape2)
t <- dcast(test, values ~ convert_me+age_col, length )
t
That is, this:
convert_me values age_col
Convert1 21.71502 23
Convert2 58.35506 33
Convert3 60.41639 44
becomes this:
values Convert2_33 Convert1_23 Convert3_44
21.71502 0 1 0
58.35506 1 0 0
60.41639 0 0 1
I know that with dummy variables I can get the value of the columns and transform as the name of the column, but is there a way to merge them(combination) easily, as R does?
You can use the crosstab function for this:
In [14]: pd.crosstab(index=df['values'], columns=[df['convert_me'], df['age_col']])
Out[14]:
convert_me Convert1 Convert2 Convert3
age_col 23 33 44
values
21.71502 1 0 0
58.35506 0 1 0
60.41639 0 0 1
or the pivot_table (with len as the aggregating function, but here you have to fillna the NaNs with zeros manually):
In [18]: df.pivot_table(index=['values'], columns=['age_col', 'convert_me'], aggfunc=len).fillna(0)
Out[18]:
age_col 23 33 44
convert_me Convert1 Convert2 Convert3
values
21.71502 1 0 0
58.35506 0 1 0
60.41639 0 0 1
See here for the docs on this: http://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations
Most functions in pandas will return a multi-level (hierarchical) index, in this case for the columns. If you want to 'melt' this into one level like in R you can do:
In [15]: df_cross = pd.crosstab(index=df['values'], columns=[df['convert_me'], df['age_col']])
In [16]: df_cross.columns = ["{0}_{1}".format(l1, l2) for l1, l2 in df_cross.columns]
In [17]: df_cross
Out[17]:
Convert1_23 Convert2_33 Convert3_44
values
21.71502 1 0 0
58.35506 0 1 0
60.41639 0 0 1
Collected from the Internet
Please contact [email protected] to delete if infringement.
Comments