<\/span><\/h2>\n\n\n\nYou can select multiple columns from a dataframe by passing a list of column names that you want, in square brackets […] as shown.<\/p>\n\n\n\n
df1 = df[['id','Name']]
print(df1)
## Output
id Name
0 1 John
1 2 Jane
2 3 Jim
3 4 Kim<\/pre>\n\n\n\nPlease use the correct case for each column name, otherwise you will get an error. This method is very useful if you already know the column names, or you want to select columns that are not contiguous.<\/p>\n\n\n\n
Also, in this method, a new copy of dataframe with the required columns instead of creating a view. This means the dataframe df1 will occupy a different memory space, and any changes to it will not affect the original dataframe, and vice versa.<\/p>\n\n\n\n
If you need to select a lot of contiguous columns, then entering their names one by one may be tedious. In this case, you can directly use df.columns[…] to retrieve a list of column names that you want to select in the result. Here is an example to select 1st and 2nd columns, that is, columns first to second.<\/p>\n\n\n\n
df1 = df[df.columns[0:2]]
print(df1)
## Output
id Age
0 1 27
1 2 24
2 3 22
3 4 32<\/pre>\n\n\n\nThis method is useful if you want to select many contiguous columns, and\/or if you do not know their names.<\/p>\n\n\n\n