How do I change integers in a column to NULL & another column to 0 or 0.0 if float?

How do I change integers in a column to NULL & another column to 0 or 0.0 if float?.

I don’t know how to handle this Python question and need guidance.

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Can someone help me change the integers in the column Country to the string NULL and as well as changing the column Order ID to 0.0 if it’s a float or 0 ?

So basically, if you find incorrect/missing data and its text type for that column, change it to NULL. If you find incorrect/missing data and its a number type for that column, change it to 0. (or 0.0 if its a float). Order ID (either missing OR won’t be a number) and Country (either missing OR will be a number as a string)

IT’S JUST FOR COLUMNS ORDER ID AND COUNTRY.

I’ve been trying to figure out how to do it and been struggling.

Here’s the original csv file(google spreadsheet): https://docs.google.com/spreadsheets/d/1H7Bpw3of5x…

I’ve also attached the csv and the original to the files as well.

Here’s my code:

import pandas as pd

import numpy as np

# Part 1: Cleaning the Data

print(“nPart 1: Cleaning the Data”)

df = pd.read_csv(‘Marvel_Mart_Sales_Project_Master.csv’)

df.head()

print(df[‘Order Priority’].isnull().sum())

df[‘Order Priority’].fillna(“NULL”, inplace=True)

print(df[‘Item Type’].isnull().sum())

df[‘Item Type’].fillna(“NULL”, inplace=True)

print(df[‘Country’].isnull().sum())

print(df[‘Order ID’].isnull().sum())

df[‘Order ID’] = pd.to_numeric(df[‘Order ID’],errors=’coerce’)

print(df[‘Order ID’].isnull().sum())

df[‘Order ID’].fillna(0, inplace=True)

df.to_csv(“Marvel_Mart_Sales_clean.csv”, index=False)

How do I change integers in a column to NULL & another column to 0 or 0.0 if float?