pandas add row instead of column
Question: I'm new to pandas, but trying to simply add a row

    
    
    class Security:
        def __init__(self):
            self.structure = ['timestamp', 'open', 'high', 'low', 'close', 'vol']
            self.df = pd.DataFrame(columns=self.structure)  # index =
        def whats_inside(self):
            return self.df
        """
        Some skipped code...
        """
        def add_data(self, timestamp, open, high, low, close, vol):
            data = [timestamp, open, high, low, close, vol]
            self.df = self.df.append (data)
    
    sec = Security()
    print sec.whats_inside()
    sec.add_data ('2015/06/01', '1', '2', '0.5', '1', '100')
    print sec.whats_inside()
    

but the output is:

    
    
                0 close high  low open timestamp  vol
    0  2015/06/01   NaN  NaN  NaN  NaN       NaN  NaN
    1           1   NaN  NaN  NaN  NaN       NaN  NaN
    2           2   NaN  NaN  NaN  NaN       NaN  NaN
    3         0.5   NaN  NaN  NaN  NaN       NaN  NaN
    4           1   NaN  NaN  NaN  NaN       NaN  NaN
    5         100   NaN  NaN  NaN  NaN       NaN  NaN
    

This means, I'm adding a column instead of row. Yes, I've tried to google but
still didnt get the point how do make it simple pythonic way.

p.s. I know that's simple, but I'm just missing something important.


Answer: There are several ways to add a new row. Perhaps the easiest one is (if you
want to add the row to the end) is to use `loc`:

    
    
    df.loc[len(df)] = ['val_a', 'val_b', .... ]
    

`loc` expects an index. `len(df)` will return the number of rows in the
dataframe so the new row will be added to the end of the dataframe.

'['val_a', 'val_b', .... ]' is a list of values of the row, in the same order
of the columns, so the list's length must be equal to the number of columns,
otherwise you will get a `ValueError` exception. An exception for this is that
if you want all the columns to have the same values you are allowed to have
that value as a single element in the list, for example `df.loc[len(df)] =
['aa']`.

**NOTE:** a good idea will be to always use `reset_index` before using this
method because if you ever delete a row or work on a filtered dataframe you
are not guaranteed that the rows' indexes will be in sync with the number of
rows.

