Filter if String contain sub-string pyspark

Question

I have 2 datasets. In each one I have several columns. But I want to use only 2 columns from each dataset, without doing any join, merge or combination between the both of the datasets.

Example dataset 1:

column_dataset_1 <String>    |      column_dataset_1_normalized <String>
-----------------------------------------------------------------------
11882621-V021BRP161305-1     |      11882621V021BRP1613051
-----------------------------------------------------------------------
W-B.7120RP1605794            |      WB7120RP1605794
-----------------------------------------------------------------------
D/57RP.1534421               |      D57RP1534421
-----------------------------------------------------------------------
125858G_022BR/P070751        |      125858G022BRP070751
-----------------------------------------------------------------------
300B.5190C57/51507           |      300B5190C5751507
-----------------------------------------------------------------------

Example dataset 2

column_dataset_2 <String>                                                           |       column_dataset_2_normalized <String>
-------------------------------------------------------------------------------------------------------------------------------------------------------------
Por ejemplo, si W-B.7120RP1605794se trata de un archivo de texto,                   |  PorejemplosiWB7120RP1605794setratadeunarchivodetexto 
-------------------------------------------------------------------------------------------------------------------------------------------------------------     
se abrirá en un programa de procesamiento de texto.                                 |  seabrirenunprogramadeprocesamientodetexto
-------------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                    |
-------------------------------------------------------------------------------------------------------------------------------------------------------------
utilizados 125858G_022BR/P070751 frecuentemente (por ejemplo, un texto que describe |  utilizados125858G022BRP070751frecuentementeporejemplountextoquedescribe

--------------------------------------------------------------------------------------------------------------------------------------------------------------

column_dataset_1_normalized is the result of column_dataset_1 is normalized column_dataset_2_normalized is the resut of column_dataset_2 is normalized

I want to compare column_dataset_1_normalized if is exist in column_dataset_2_normalized. If yes I should extract it from column_dataset_2

Example:

WB7120RP1605794 is in the second line of column_dataset_1_normalized, is exist in the first line of column_dataset_2_normalized, so I should extract it's real value [W-B.7120RP1605794], from column_dataset_2 and store it in a new column in dataset 2.

And the same for 125858G022BRP070751 is in forth line in column_dataset_2_normalized, I should extract it from column_dataset_2 [125858G_022BR/P070751]. The comparaison should, take one by one value of column_dataset_1_normalized and search it in all the cell of column_dataset_2_normalized.

For normalization I used this code to kepp only number and letter:

df = df.withColumn(
        "column_normalized",
        F.regexp_replace(F.col("column_to_normalize"), "[^a-zA-Z0-9]+", ""))

Someone can propose me a suggestion how can I do it ? Thank you

Answer 1

There are various way to join two dataframes:

(1) find the location/position of string column_dataset_1_normalized in column_dataset_2_normalized by using SQL function locate, instr, position etc, return a position (1-based) if exists

    from pyspark.sql.functions import expr

    cond1 = expr('locate(column_dataset_1_normalized,column_dataset_2_normalized)>0')
    cond2 = expr('instr(column_dataset_2_normalized,column_dataset_1_normalized)>0')
    cond3 = expr('position(column_dataset_1_normalized IN column_dataset_2_normalized)>0')

(2) use regex rlike to find column_dataset_1_normalized from column_dataset_2_normalized, this is only valid when no regex meta-characters is shown in column_dataset_1_normalized

    cond4 = expr('column_dataset_2_normalized rlike column_dataset_1_normalized')

Run the following code and use one of the above conditions, for example:

df1.join(df2, cond1).select('column_dataset_1').show(truncate=False)
+---------------------+
|column_dataset_1     |
+---------------------+
|W-B.7120RP1605794    |
|125858G_022BR/P070751|
+---------------------+

Edit: Per comments, the matched sub-string might not be the same as df1.column_dataset_1, so we will need to reverse-engineer the sub-string from the normalized string. Based on how the normalization is conducted, the following udf might help (notice this will not cover any leading/trailing non-alnum that might be in the matched). Basically, we will iterate through the string by chars and find the start/end index of the normalized string in the original string, then take the sub-string:

from pyspark.sql.functions import udf

@udf('string')
def find_matched(orig, normalized):
  n, d = ([], [])
  for i in range(len(orig)):
    if orig[i].isalnum(): 
      n.append(orig[i])
      d.append(i)
  idx = ''.join(n).find(normalized)
  return orig[d[idx]:d[idx+len(normalized)]] if idx >= 0 else None

df1.join(df2, cond3) \
   .withColumn('matched', find_matched('column_dataset_2', 'column_dataset_1_normalized')) \
   .select('column_dataset_2', 'matched', 'column_dataset_1_normalized') \
   .show(truncate=False)

+------------------------------------------------------------------------------------+-----------------------+---------------------------+
|column_dataset_2                                                                    |matched                |column_dataset_1_normalized|
+------------------------------------------------------------------------------------+-----------------------+---------------------------+
|Por ejemplo, si W-B.7120RP-1605794se trata de un archivo de texto,                  |W-B.7120RP-1605794     |WB7120RP1605794            |
|utilizados 125858G_022BR/P-070751 frecuentemente (por ejemplo, un texto que describe|125858G_022BR/P-070751 |125858G022BRP070751        |
+------------------------------------------------------------------------------------+-----------------------+---------------------------+