Pandas : to_csv() got an unexpected keyword argument

Question

While I am trying to use some of the parameters in dataframe to_csv function, it throws an TypeError, such as `TypeError: to_csv() got an unexpected keyword argument 'doublequote'

df.to_csv('transactions.x', header=False, doublequote=False) or df.to_csv('transactions.x', doublequote=False)

My pandas version is 0.19.2 (Checked with print(pd.__version__)) I am using Python 3.5

The following official document is based on 0.19.2. Although, I am having type errors, it is stated that these parameters can be used as an optional. http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html

Do you guys have any idea about it?

Thank you.

SOLUTION

Thanks for brain storming with all commenters.

After using following the command df = df.groupby(['Transactions'])['Items'].apply(','.join), dataframe becomes series.

In order to cast series to dataframe, this command df = df.groupby(['Transactions'])['Items'].apply(','.join).to_frame() should be used instead.

Finally, to export it as a CSV with non-quote style by avoiding escape char, you need to end up with the following command df.to_csv('transactions.x', header=False, quoting=csv.QUOTE_NONE, escapechar=' ') #or whatever escapechar.

Hopefully, it helps for everyone. Thanks

Solution

Would this help:

pd.to_csv('test.csv', quoting=csv.QUOTE_NONE)

As per your comment, read docs on series.

You can use to_frame before saving to resolve your issue.

Comments

[TheWho]

Same issue, it still throws a same TypeError issue.

[TheWho]

No, all the parameters (quoting, doublequote, etc...) are becoming invalidated since somehow dataframe behaves as a series.

[TheWho]

Thank you but, By typing following command df = df.groupby(['Transactions'])[['Items']].apply(','.join) throws the same error, on the other hand if I want to use df = df.groupby(['Transactions'])[['Items']].apply(','.join).to_f‌​rame() , it does not throw error but output is showing the "Items" instead of real items such as a, b, c, .... If i use the last option, df = df.groupby(['Transactions'])['Items'].apply(','.join).to_fra‌​me() , it thows the following error _csv.Error: need to escape, but no escapechar set

[TheWho]

I already tried them Thank you but, By typing following command df = df.groupby(['Transactions'])[['Items']].apply(','.join) throws the same error, on the other hand if I want to use df = df.groupby(['Transactions'])[['Items']].apply(','.join).to_f‌​‌​rame() , it does not throw error but output is showing the "Items" instead of real items such as a, b, c, .... If i use the last option, df = df.groupby(['Transactions'])['Items'].apply(','.join).to_fra‌​‌​me() , it thows the following error _csv.Error: need to escape, but no escapechar set

[matanster]

To turn a series to a data frame of a single column, I think you should rather use to_frame as suggested in @zipa's answer below, or at least I'm not sure myself why you need that elaborate way that you use here.