If you know the column names prior, you can do it by setting usecols parameter

When you know which columns to use

Suppose you have csv file with columns ['id','name','last_name'] and you want just ['name','last_name']. You can do it as below:

import pandas as pd
df = pd.read_csv("sample.csv", usecols = ['name','last_name'])

when you want first N columns

If you don't know the column names but you want first N columns from dataframe. You can do it by

import pandas as pd
df = pd.read_csv("sample.csv", usecols = [i for i in range(n)])

Edit

When you know name of the column to be dropped

# Read column names from file
cols = list(pd.read_csv("sample_data.csv", nrows =1))
print(cols)

# Use list comprehension to remove the unwanted column in **usecol**
df= pd.read_csv("sample_data.csv", usecols =[i for i in cols if i != 'name'])

Get the column headers from your CSV using pd.read_csv with nrows=1, then do a subsequent read with usecols to pull everything but the column(s) you want to omit.

headers = [*pd.read_csv('sample.csv', nrows=1)]
df = pd.read_csv('sample.csv', usecols=[c for c in headers if c != 'name']))

Alternatively, you can do the same thing (read only the headers) very efficiently using the CSV module,

import csv

with open("sample.csv", 'r') as f:
    header = next(csv.reader(f))
    # For python 2, use
    # header = csv.reader(f).next()

df = pd.read_csv('sample.csv', usecols=list(set(header) - {'name'}))

Using df= df.drop(['ID','prediction'],axis=1) made the work for me. I dropped 'ID' and 'prediction' columns. Make sure you put them in square brackets like ['column1','column2']. There is no need for other complicated solutions.