Pandas iterate over DataFrame row pairs

Question

How can I iterate over pairs of rows of a Pandas DataFrame?

For example:

content = [(1,2,[1,3]),(3,4,[2,4]),(5,6,[6,9]),(7,8,[9,10])]
df = pd.DataFra         


        

Answer 1

To get the output you've shown use:

for row in df.index[:-1]:
    print 'row 1:'
    print df.iloc[row].squeeze()
    print 'row 2:'
    print df.iloc[row+1].squeeze()
    print

Answer 2

shift the dataframe & concat it back to the original using axis=1 so that each interval & the next interval are in the same row

df_merged = pd.concat([df, df.shift(-1).add_prefix('next_')], axis=1)
df_merged
#Out:
   a  b interval     next_a     next_b    next_interval
0  1  2   [1, 3]        3.0        4.0           [2, 4]
1  3  4   [2, 4]        5.0        6.0           [6, 9]
2  5  6   [6, 9]        7.0        8.0          [9, 10]
3  7  8  [9, 10]        NaN        NaN              NaN

define an intersects function that works with your lists representation & apply on the merged data frame ignoring the last row where the shifted_interval is null

def intersects(left, right):
    return left[1] > right[0]

df_merged[:-1].apply(lambda x: intersects(x.interval, x.next_interval), axis=1)
#Out:
0     True
1    False
2    False
dtype: bool

Answer 3

You could try the iloc indexing.

Exmaple:

for i in range(df.shape[0] - 1):                        
    idx1,idx2=i,i+1                         
    row1,row2=df.iloc[idx1],df.iloc[idx2]   
    print(row1)                             
    print(row2)                             
    print()                                                                            

Answer 4

If you want to keep the loop for, using zip and iterrows could be a way

for (indx1,row1),(indx2,row2) in zip(df[:-1].iterrows(),df[1:].iterrows()):
    print "row1:\n", row1
    print "row2:\n", row2
    print "\n"

To access the next row at the same time, start the second iterrow one row after with df[1:].iterrows(). and you get the output the way you want.

row1:
a    1
b    2
Name: 0, dtype: int64
row2:
a    3
b    4
Name: 1, dtype: int64


row1:
a    3
b    4
Name: 1, dtype: int64
row2:
a    5
b    6
Name: 2, dtype: int64


row1:
a    5
b    6
Name: 2, dtype: int64
row2:
a    7
b    8
Name: 3, dtype: int64

But as said @RafaelC, doing for loop might not be the best method for your general problem.