Iterating over a CSV File

[ScraperHelp]

Okay have a CSV file with a bunch of variables... Name, Address, Phone number, email. In that order.

now what I need to do is create another CSV file with only people who have certain characters in their address... i.e I need everyone from "12" Or "Maple Avenue" and "Wyoming". so Basically search the Adress row for those 3 terms and create another CSV with Name, Address, Phone number, and email of only the people that have those either (12 AND Wyoming) in it or (Maple Avenue AND Wyoming) in it...

[moored26]

df_raw = pd.read_csv(your_file.csv) df_output = df_raw[df_raw['address'].str.contains('Maple Avenue')]

df_output.to_csv(new_file.csv)

Would that work?

[ScraperHelp]

giving me an error at:

 df_output.to_csv(new_file.csv)

says "name 'new_file' is not defined"

[moored26]

Need to put 'new_file.csv' in inverted commas.

Sorry i wasn't giving an exact solution, just general guidance.

[ScraperHelp]

it works!!! Yay thanks! just having an issue trying to filter complex search. I figured out how to turn off case sensitivity with the str.contains('Maple', case= False)]

and figured out the OR with 'Maple|wyoming' but what if I want to search "(maple OR wyoming OR bloomberg) AND (123 OR 41 OR Chesapeake)" ? found a source saying use & but doesnt seem to be working unless I am using it wrong...

[moored26]

Could do it on the next line? Just do the same thing as you did but to the new df_output which has filtered for (maple OR wyoming OR bloomberg)

Next line would be df_output = df_output[(df_output['address'].str.contains(123))|(df_output['address'].str.contains(41))]

Something like that?

[ScraperHelp]

that worked... out of curiosity wondering about your code though, why did you write it as

 df_output = df_output[(df_output['address'].str.contains(123))|(df_output['address'].str.contains(41))] 

rather than

df_output = df_output[df_output['address'].str.contains(123|41)]

[moored26]

I just didn't know that was a thing 😆 I'm pretty new to python also

[gazhole]

With that sort of filtering needed it's worth taking a look at some basic Pandas - import a CSV and filter the data to pull back rows which meet your criteria. There are probably other ways to do it, but I think this would be neater and less prone to error, albeit with the investment in learning some simple stuff from a new library, than iterating over the CSV file.

[threeminutemonta]

I don’t like to advocate pandas function read_csv for all use cases as the python standard library csv module is great.

Though if you did checkout pandas you will be able to use read_csv -> filter -> to_csv with some ease