python dataframe pandas drop multiple column using column name
Question:
I am beginner to python
I understand that to drop a column you use df.drop(‘column name’, axis=1, inplace =True)
file format is in .csv file
I wan to use above syntax for large data sets and more in robust way
suppose I have 500 columns and I want to keep column no 100 to 140 using column name not by indices and rest want to drop , how would I write above syntax so that I can achieve my goal and also in 100 to 140 column , I want to drop column no 105, 108,110 by column name
Answers:
Instead of using a string parameter for the column name, use a list of strings refering to the column names you want to delete.
df = df.loc[:, 'col_100_name' : 'col_140_name']
.loc always selects using both ends inclusive. Here I am selecting all rows, and only the columns that you want to select (by names).
After this (or before – it doesn’t matter) you can drop the other columns by names as usual:
df.drop(['col_105_name', 'col_108_name', 'col_110_name'], axis=1, inplace=True)
I am beginner to python
I understand that to drop a column you use df.drop(‘column name’, axis=1, inplace =True)
file format is in .csv file
I wan to use above syntax for large data sets and more in robust way
suppose I have 500 columns and I want to keep column no 100 to 140 using column name not by indices and rest want to drop , how would I write above syntax so that I can achieve my goal and also in 100 to 140 column , I want to drop column no 105, 108,110 by column name
Instead of using a string parameter for the column name, use a list of strings refering to the column names you want to delete.
df = df.loc[:, 'col_100_name' : 'col_140_name']
.loc always selects using both ends inclusive. Here I am selecting all rows, and only the columns that you want to select (by names).
After this (or before – it doesn’t matter) you can drop the other columns by names as usual:
df.drop(['col_105_name', 'col_108_name', 'col_110_name'], axis=1, inplace=True)