Python – Pandas dataframe, replacing the last line with iloc

I’m trying to replace the last row of the Pandas data frame with iloc, but I can’t get it to work. There are a lot of solutions out there, but the simplest (and slowest) are:

How to do a FIFO push-operation for rows on Pandas dataframe in Python?

Why doesn’t this method work in the code below?

def append_from_dataframe(self,timeframe,new_dataframe):

new_dataframe.reset_index(inplace=True)
    temp_dataframe = self.timeframedict.get(timeframe)
    num_rows_existing = temp_dataframe.shape[0]
    num_rows_new = new_dataframe.shape[0]
    overlap = (num_rows_existing + num_rows_new) - 500
    # slow, replace with numpy array eventually
    if overlap >= 1:
        # number of rows to shift 
        i = overlap * -1
        #shift the dataframe back in time
        temp_dataframe = temp_dataframe.shift(i)
        #self.timeframedict.get(timeframe) = self.timeframedict.get(timeframe).shift(overlap)
        #replace the last i rows with the new values
        temp_dataframe.iloc[i:] = new_dataframe
        self.timeframedict.update({timeframe:temp_dataframe})

else:
        #TODO - see this https://stackoverflow.com/questions/10715965/add-one-row-in-a-pandas-dataframe
        self.timeframedict.update({timeframe:self.timeframedict.get(timeframe).append(new_dataframe)})

Replace another row with the contents of the data frame:

ipdb> new_dataframe
       Timestamp    Open    High     Low   Close   Volume      localtime
0  1533174420000  423.43  423.44  423.43  423.44  0.73765  1533174423776

temp_dataframe.shift(i) moves the value back to one bit, replacing the value – with NaN

ipdb> temp_dataframe.iloc[i:] 
     Timestamp  Open  High  Low  Close  Volume  localtime
499        NaN   NaN   NaN  NaN    NaN     NaN        NaN

But temp_dataframe.iloc[i:] = new_dataframe does not replace anything.

EDIT: I

should add that now I can replace 1 line with:

temp_dataframe.iloc[-1] = new_dataframe.iloc[0]

However, I can’t get the multi-line version to work

df = pd. DataFrame({'a':[1,2,3,4,5],'b':['foo','bar','foobar','foobaz','food']})

Output:

df
Out[117]: 
   a       b
0  1     foo
1  2     bar
2  3  foobar
3  4  foobaz
4  5    food

Replace the last two lines (foobaz and food) with the second and first rows, respectively:

df.iloc[-2:]=[df.iloc[1],df.iloc[0]] 

df
Out[119]: 
   a       b
0  1     foo
1  2     bar
2  3  foobar
3  2     bar
4  1     foo

You can also do this to get the same result:

df.iloc[-2:]=df.iloc[1::-1].values