Q:

Remove Column with Duplicate Values in Pandas

I have a database with sample as below:

Data frame is generated when I load data in Python as per below code
import os
import pandas as pd
data_dir="D:\\userdata\\adbharga\\Desktop\\AVA\\PythonCoding\\VF-Aus\\4G Cell Graphs"
os.chdir(data_dir)
df = pd.read_csv('CA Throughput(Kbit_s) .csv',index_col=None, header=0)

Output:

Is there any way by which we can avoid reading duplicate columns in Pandas, or remove the duplicate columns post reading.
Pl Note: Column Name is different once data is read in Pandas, so command like df=df.loc[:,~df.columns.duplicated()] won't work.
Actual database is very big and has many duplicate column with Dates only.

A:

There are 2 ways you can do this.
Ignore columns when reading the data
pandas.read_csv has the argument usecols, which accepts an integer list.
So you can try:
# work out required columns
df = pd.read_csv('file.csv', header=0)
cols = [0] + list(range(1, len(df.columns), 2))

# use column integer list
df = pd.read_csv('file.csv', usecols=cols)

Remove columns from dataframe
You can use similar logic with pd.DataFrame.iloc to remove unwanted columns.
# cols as defined in previous example

df = df.iloc[:, cols]

