Insert Multiple Elements Into Pandas Series Where Similarities Exist

Here I'd like to insert the row 'None' between wherever there are two rows with 'href' in the tag--note, each row with href is NOT identical. im

Solution 1:

Than can be easily achieved if you go to numpy.

In your example:

dups = table.str.contains('href') & table.shift(1).str.contains('href')

array = np.insert(table.values, dups[dups].index, "<td class='test'>None</td>")

pd.Series(array)

Solution 2:

Ecotrazar's solution above is both faster and more elegant. Here is my version using for loops and his numpy insert method.

import pandas as pd

table = pd.Series(

        ["<td class='test'><a class='test' href=...", # 0 "<td class='test'>A</td>",                    # 1"<td class='test'><a class='test' href=...",  # 2"<td class='test'>B</td>",                    # 3"<td class='test'><a class='test' href=...",  # 4"<td class='test'><a class='test' href=...",  # 5"<td class='test'>C</td>",                    # 6"<td class='test'><a class='test' href=...",  # 7 "<td class='test'>F</td>",                    # 8"<td class='test'><a class='test' href=...",  # 9 "<td class='test'><a class='test' href=...",  # 10 "<td class='test'>X</td>"])                   # 11

insertAt = []
for i inrange(0, len(table)):

  if'href'in table[i] and'href'in table[i + 1] and i == 0:
    print(i + 1, ' is duplicated')
    insertAt.append(True)
  elif i == 0:
     insertAt.append(False)

  if'href'in table[i] and'href'in table[i+1] and i > 0:
    print(i + 1, ' is duplicated')
    insertAt.append(True)

  else:
    insertAt.append(False)

insertAt = pd.Series(insertAt)
print(insertAt)

import numpy as np
array = np.insert(table.values, insertAt[insertAt].index, "<td class='test'>None</td>")
pd.Series(array) # back to series if necessary