Context:
i am new in Pandas and I need a function that creates new columns based on existing columns. The new columns name will have the name from the original column plus new characters (example: create a "As NEW" column from "As" column). Can i access the old column header string to make the name of the new column?
Problem:
i have df['columnA'] and need to get "columnA" string
If I understand you correctly, this may be what you're looking for.
You can use str.contains() for the columns, then use string formatting to create the new column name.
df = pd.DataFrame({'col1':['A', 'A', 'B','B'], 'As': ['B','B','C','C'], 'col2': ['C','C','A','A'], 'col3': [30,10,14,91]})
col = df.columns[df.columns.str.contains('As')]
df['%s New' % col[0]] = 'foo'
print (df)
As col1 col2 col3 As New
0 B A C 30 foo
1 B A C 10 foo
2 C B A 14 foo
3 C B A 91 foo
Assuming that you have an empty DataFrame df with columns, you could access the columns of df as a list with:
>>> df.columns
Index(['columnA', 'columnB'], dtype='object')
.columns will allow you to overwrite the columns of df, but you don't need to pass in another Index. You can pass it a regular list, like so:
>>> df.columns = ['columna', 'columnb']
>>> df
Empty DataFrame
Columns: [columna, columnb]
Index: []
This can be done through the columns attribute.
cols = df.columns
# Do whatever operation you want on the list of strings in cols
df.columns = cols
