I have a pandas dataframe that has a string column in it. The length of the frame is over 2 million rows and looping to extract the elements I need is a poor choice. My current code looks like the following
for i in range(len(table["series_id"])):
table["state_code"] = table["series_id"][i][2:4]
table["area_code"] = table["series_id"][i][5:9]
table["supersector_code"] = table["series_id"][i][11:12]
where "series_id" is the string containing multiple information fields I want to create an example data element:
columns:
[series_id, year, month, value, footnotes]
The data:
[['SMS01000000000000001' '2006' 'M01' 1966.5 '']
['SMS01000000000000001' '2006' 'M02' 1970.4 '']
['SMS01000000000000001' '2006' 'M03' 1976.6 '']
However series_id is column of interest that I am struggling with. I have looked at the str.FUNCTION for python and specifically pandas.
http://pandas.pydata.org/pandas-docs/stable/basics.html#testing-for-strings-that-match-or-contain-a-pattern
has a section describing each of the string functions i.e. specifically get & slice are the functions I would like to use. Ideally I could envision a solution like so:
table["state_code"] = table["series_id"].str.get(1:3)
or
table["state_code"] = table["series_id"].str.slice(1:3)
or
table["state_code"] = table["series_id"].str.slice([1:3])
When I have tried the following functions I get an invalid syntax for the ":".
but alas I cannot seem to figure out the proper way to perform the vector operation for taking a substring on a pandas data frame column.
Thank you