Pandas（Python）-将列表附加到具有列名称的空数据框的问题

Question

I am trying to append a list looking like this

myList = ['2018-01-12', 'MMM', 'BUY', 42, 236.5229]

to an empty dataframe (with "header" / columns names).

To create the dataframe I've done the following:

tradeLog = pd.DataFrame(columns=["DATE", "TICKER", "ORDER_TYPE", "AMOUNT", "PRICE"])

I am trying to append the list as a row in the following way:

tradeLog.append(myList, ignore_index=True)

(NOTICE: My goal i to iterate over some data - a lot of lists in the same format - and the add them one by one to the dataframe)

Answer 1

The pandas documentation reads

DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=None)

other : DataFrame or Series/dict-like object, or list of these The data to append.

So you need to have to transform your list prior to appending it to your DataFrame:

something that might work is to zip the list of your columns to the content of your myList so it would be:

tradeLog = pd.DataFrame(columns=["DATE", "TICKER", "ORDER_TYPE", "AMOUNT", "PRICE"])
myList = ['2018-01-12', 'MMM', 'BUY', 42, 236.5229]
myDict = dict(zip(tradeLog.columns.tolist(), myList))

tradeLog.append(myDict, ignore_index=True)

or tradeLog.append(pd.DataFrame(myDict), ignore_index=True)

This being said you need to ensure your lists are always the same length as your columns names list.

Answer 2

DataFrame.append() is for appending rows from an other pandas dataframe or series (see the docs ).

So, if it is absolutely necessary to do this line by line, you can

tradeLog = tradeLog.append(pd.Series(myList, index=tradeLog.columns), ignore_index=True)

(Nb: tradeLog.loc[len(tradeLog)] = ... appends to the end only as long as you have a simple integer index on tradeLog , but might break for more complex use cases.)

You might also want to consider this remark from the docs:

Iteratively appending rows to a DataFrame can be more computationally intensive than a single concatenate. A better solution is to append those rows to a list and then concatenate the list with the original DataFrame all at once.