Reputation: 2889
This is a follow-up to a previously asked question (asked by me :)) Oneliner to create string column from multiple columns
I want to merge a subset columns in a dataframe to a new create a new string-column. @Zero was kind enough to give me the solution to this problem
import pandas as pd
df = pd.DataFrame({'gender' : ['m', 'f', 'f'],\
'code' : ['K2000', 'K2000', 'K2001']})
col_names = df.columns
df_str = df[col_names].astype(str).apply('_'.join, axis=1)
df_str
Out[17]:
0 K2000_m
1 K2000_f
2 K2001_f
dtype: object
However if I introduce interval data this fails
df = pd.DataFrame({'gender' : ['m', 'f', 'f'],\
'code' : ['K2000', 'K2000', 'K2001'],\
'num' : pd.cut([3, 6, 9], [0, 5, 10])})
col_names = df.columns
df_str = df[col_names].astype(str).apply('_'.join, axis=1)
Ideally I would also like to transform the data to categorical data (which also fails)
df_cat = pd.concat([df['gender'].astype('category'), \
df['code'].astype('category'), \
df['num'].astype('category')], axis=1)
df_cat_str = df_cat[col_names].astype(str).apply('_'.join, axis=1)
What is going on here? And how can i acheive the desired output
0 K2000_m_(0, 5]
1 K2000_f_(5, 10]
2 K2001_f_(5, 10]
As with the previous question col_names
should be a list containing any subset of the columns (not necessarily all columns as in this example)
Upvotes: 2
Views: 546
Reputation: 863791
You need convert each column to str
separately in lambda function:
df_str = df[col_names].apply(lambda x: '_'.join(x.astype(str)), axis=1)
print (df_str)
0 K2000_m_(0, 5]
1 K2000_f_(5, 10]
2 K2001_f_(5, 10]
dtype: object
Upvotes: 1