giving an easier name to many columns and applying function to them in pandas
Question:
I have to apply many functions to certain columns in my df. But there are many columns. Is there any way to give these groups of columns a certain name and then apply functions to them?
I am looking for something like
df[['One', 'Two',...'Sixty']] = values
values.apply(lambda x: x.astype(str).str.lower())
Answers:
Probably you could first create a list of column names and then use pd.DataFrame.isin as follows:
values = ['One', 'Two',...'Sixty']
df.loc[:, df.columns.isin(values)].apply(lambda x: x.astype(str).str.lower())
You can group the columns then use the apply option in pandas:
cols_to_group = ['col1', 'col2']
df.loc[:, cols_to_group] = df.loc[:, cols_to_group].apply(my_function)
my_function can contain the functionality you need to apply to the group.
If performance is not an issue, you can use pandas.DataFrame.applymap :
cols = ['One', 'Two',...'Sixty']
df[cols] = df[cols].astype(str).applymap(lambda x: x.lower())
I have to apply many functions to certain columns in my df. But there are many columns. Is there any way to give these groups of columns a certain name and then apply functions to them?
I am looking for something like
df[['One', 'Two',...'Sixty']] = values
values.apply(lambda x: x.astype(str).str.lower())
Probably you could first create a list of column names and then use pd.DataFrame.isin as follows:
values = ['One', 'Two',...'Sixty']
df.loc[:, df.columns.isin(values)].apply(lambda x: x.astype(str).str.lower())
You can group the columns then use the apply option in pandas:
cols_to_group = ['col1', 'col2']
df.loc[:, cols_to_group] = df.loc[:, cols_to_group].apply(my_function)
my_function can contain the functionality you need to apply to the group.
If performance is not an issue, you can use pandas.DataFrame.applymap :
cols = ['One', 'Two',...'Sixty']
df[cols] = df[cols].astype(str).applymap(lambda x: x.lower())