Read .csv with different delimiters/separators

As I mentioned in the comments, this one is especially nasty:

1. It’s a regular formatted csv with comma delimiter, quote encapsulation, and double quotes as an escape character. But all the data is in the first column.
2. The data in the first column is itself delimited by a semi-colon and uses quote encapsulation (which are escaped properly by the outer csv) but its encapsulated double quote literals are not escaped by either the outer csv or the inner csv… it’s very odd. So commas inside the first column’s delimited data are treated as actual commas by the real csv in which it’s wrapped, malforming the data.

It is, essentially, a csv in a csv and because both layers of csv use quote encapsulation but the inner most layer isn’t escaping properly, it’s malformed.

One option is to just read each line in as an entire column and clean it up after it’s imported:

#read the entire line into a single dataframe column
df = pd.read_csv('file.csv', header=None, names=['data'], sep='|')
#replace commas and double quotes with a blank
data = df.data.str.replace('[,]*$|"','',regex=True)
#create a new data frame by splitting the single column above on a semicolon using lines 1:<end>, use line 0 as the header, splitting it using the same logic.
df_split = pd.DataFrame(data[1:].str.split(';').tolist(), columns = data[0].split(';'))
display(df_split)

+-----+---------+---------------------+----------+----------------------+----------+------------------------------------+----------+-----------------------------------------+-------------+---------------------------------------------------+-----------------------+--------+----+------+-----------+
| idx | Sección | Descripción Sección | Servicio | Descripción Servicio | Programa |        Descripción Programa        | Capítulo |          Descripción Capítulo           | Subconcepto |              Descripción Subconcepto              | Proyecto de Inversión | Nombre | CT | Isla |  Importe  |
+-----+---------+---------------------+----------+----------------------+----------+------------------------------------+----------+-----------------------------------------+-------------+---------------------------------------------------+-----------------------+--------+----+------+-----------+
|   0 |      01 | Parlamento          |     0101 | Servicios Generales  | 911A     | Actuación Legislativa y de Control |        1 | GASTOS DE PERSONAL                      |       10000 | Retrib.básic. y otras ret. del Gob.y altos Cargos |                       |        |    |      | 3.836.041 |
|   1 |      01 | Parlamento          |     0101 | Servicios Generales  | 911A     | Actuación Legislativa y de Control |        2 | GASTOS CORRIENTES EN BIENES Y SERVICIOS |       21900 | Otro inmovilizado material                        |                       |        |    |      |     1.500 |
|   2 |      01 | Parlamento          |     0101 | Servicios Generales  | 911A     | Actuación Legislativa y de Control |        2 | GASTOS CORRIENTES EN BIENES Y SERVICIOS |       22001 | Prensa, revistas, libros y otras publicaciones    |                       |        |    |      |   111.000 |
+-----+---------+---------------------+----------+----------------------+----------+------------------------------------+----------+-----------------------------------------+-------------+---------------------------------------------------+-----------------------+--------+----+------+-----------+

This is a little heavy handed since it’s just replacing double quotes with nothing, even though you may have double quotes in your data (though it’s very hard to tell if you do, and likely they are all malformed anyway).