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I'm new to pandas, therefore perhaps I'm asking a very stupid question. Normally initialization of data frame in pandas would be column-wise, where I put in dict with key of column names and values of list-like object with same length.
But I would love to initialize row-wise without dynamically concat-ing rows. Say I have a list of namedtuple, is there a optimized operation that will give me a pandas data frame directly from it?
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To convert a Python tuple to DataFrame, use the pd. DataFrame() constructor that accepts a tuple as an argument and it returns a DataFrame.
Using loc[] to Append The New List to a DataFrame. By using df. loc[index]=list you can append a list as a row to the DataFrame at a specified Index, In order to add at the end get the index of the last record using len(df) function.
In a similar vein to creating a Series from a namedtuple, you can use the _fields attribute:
In [11]: Point = namedtuple('Point', ['x', 'y']) In [12]: points = [Point(1, 2), Point(3, 4)] In [13]: pd.DataFrame(points, columns=Point._fields) Out[13]: x y 0 1 2 1 3 4 Assuming they are all of the same type, in this example all Points.
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