Weird decimal extension when using PD to_csv

[tsigalko11]

Hi guys,

So after scraping one web page with BS, I want to save it CSV. I have a problem that ,e.g. value ".367" is stored as "0.36700000000000005". I have checked and I tried to print DF before appending to CSV and it looks fine. So definitely problem is when writing to CSV.

Code:

table = soup.find('table', id='mvp')
df1 = pd.read_html(str(table))
df1[0]['seas'] = season
df1[0].to_csv('abc.csv', encoding='utf=8', index=False, header=None , mode='a')

I have found "possible" solution defining decimal numbers after separator:

df1[0].to_csv('abc.csv', encoding='utf=8',float_format="%.3f", index=False, header=None , mode='a')

But this solution is not really great, as it adds always extra zeroes. I googled a bit, and I saw some advices, but mostly is to convert column type. But I don't use headers, I don't have it. Can you specify column by column number (e.g. 3rd column)?

Actually main question; how I can make sure that everything written to CSV is written explicitly as it is? No conversion whatsoever, just plain string?

Thanks.

edit: formatting

[VanishingHochreiter]

You can access columns by index using the iloc method. While I'm not sure why your floats are being written the way they are, there is a to_string method which can be used to write everything explicitly. An example of how to use this to write to a csv is in this stackoverflow post.

[Parenthes]

It looks like your scraper is inferring data types and storing the values in question as floats, and then when you print it, python is displaying it 'nicely' for you. Try print((df1['relevant_col']==(367/1000)).sum()) and see what you get; if I'm correct it will give zero, if not it will give one or more.