Append an empty row in dataframe using pandas

Question

I am trying to append an empty row at the end of dataframe but unable to do so, even trying to understand how pandas work with append function and still not getting it.

Here's the code:

import pandas as pd  excel_names = ["ARMANI+EMPORIO+AR0143-book.xlsx"] excels = [pd.ExcelFile(name) for name in excel_names] frames = [x.parse(x.sheet_names[0], header=None,index_col=None).dropna(how='all') for x in excels] for f in frames:     f.append(0, float('NaN'))     f.append(2, float('NaN')) 

There are two columns and random number of row.

with "print f" in for loop i Get this:

                             0                 1 0                   Brand Name    Emporio Armani 2                 Model number            AR0143 4                  Part Number            AR0143 6                   Item Shape       Rectangular 8   Dial Window Material Type           Mineral 10               Display Type          Analogue 12                 Clasp Type            Buckle 14               Case Material   Stainless steel 16              Case Diameter    31 millimetres 18               Band Material           Leather 20                 Band Length  Women's Standard 22                 Band Colour             Black 24                 Dial Colour             Black 26            Special Features       second-hand 28                    Movement            Quartz 

Answer 1

Add a new pandas.Series using pandas.DataFrame.append().

If you wish to specify the name (AKA the "index") of the new row, use:

df.append(pandas.Series(name='NameOfNewRow')) 

If you don't wish to name the new row, use:

df.append(pandas.Series(), ignore_index=True) 

where df is your pandas.DataFrame.

Answer 2

You can add it by appending a Series to the dataframe as follows. I am assuming by blank you mean you want to add a row containing only "Nan". You can first create a Series object with Nan. Make sure you specify the columns while defining 'Series' object in the -Index parameter. The you can append it to the DF. Hope it helps!

from numpy import nan as Nan import pandas as pd  >>> df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], ...                     'B': ['B0', 'B1', 'B2', 'B3'], ...                     'C': ['C0', 'C1', 'C2', 'C3'], ...                     'D': ['D0', 'D1', 'D2', 'D3']}, ...                     index=[0, 1, 2, 3])  >>> s2 = pd.Series([Nan,Nan,Nan,Nan], index=['A', 'B', 'C', 'D']) >>> result = df1.append(s2) >>> result      A    B    C    D 0   A0   B0   C0   D0 1   A1   B1   C1   D1 2   A2   B2   C2   D2 3   A3   B3   C3   D3 4  NaN  NaN  NaN  NaN