Convert pandas DataFrame column of comma separated strings to one-hot encoded

Question

I have a large dataframe (‘data’) made up of one column. Each row in the column is made of a string and each string is made up of comma separated categories. I wish to one h

Answer 1

Figured there is a simpler answer, or I felt this as more simple compared to multiple operations that we have to make.

1. Make sure the column has unique values separated be commas

2. Use get dummies in built parameter to specify the separator as comma. The default for this is pipe separated.

   data = {"mesh": ["A, B, C", "C,B", ""]}
   sof_df=pd.DataFrame(data)
   sof_df.mesh=sof_df.mesh.str.replace(' ','')
   sof_df.mesh.str.get_dummies(sep=',')
   

OUTPUT:

    A   B   C
0   1   1   1
1   0   1   1
2   0   0   0

Answer 2

Note that you're not dealing with OHEs.

str.split + stack + get_dummies + sum

df = pd.DataFrame(data)
df

      mesh
0  A, B, C
1      C,B
2         

(df.mesh.str.split('\s*,\s*', expand=True)
   .stack()
   .str.get_dummies()
   .sum(level=0))
df

   A  B  C
0  1  1  1
1  0  1  1
2  0  0  0

---

apply + value_counts

(df.mesh.str.split(r'\s*,\s*', expand=True)
   .apply(pd.Series.value_counts, 1)
   .iloc[:, 1:]
   .fillna(0, downcast='infer'))

   A  B  C
0  1  1  1
1  0  1  1
2  0  0  0

---

pd.crosstab

x = df.mesh.str.split('\s*,\s*', expand=True).stack()
pd.crosstab(x.index.get_level_values(0), x.values).iloc[:, 1:]
df

col_0  A  B  C
row_0         
0      1  1  1
1      0  1  1
2      0  0  0