The difference between Wi-Fi calling and regular phone calls happens behind the scenes. You can use all of the same features you’re used to, like three-way calling and speakerphone, as you would with any other phone call. Note that the output file's character encoding will be "ANSI" (the encoding implied by the legacy code page associated with your system locale) in Windows PowerShell (in PowerShell Core it would be UTF-8 without BOM) use Set-Content's -Encoding parameter to change that.On Nextiva's Website What Is Wi-Fi Calling?įrom a user experience perspective, Wi-Fi calling behaves just like a regular phone call-you dial a phone number as you normally would. This code breaks the record down with a regex, creates a new object, then exports it to a CSV file. (IMO, Pandas makes it easy: pd.DataFrame(grouped).to_csv("records.csv", index=False)) You can store that as a CSV, using whatever I/O methods you prefer. Then, after we've performed a join on both location string sets (which are adjacent in the raw data), we can do another split on * to separate them. We can add a * marker to the chunk after the "comma chunk", like this: for i, elem in enumerate(line): 'Vegas,' or 'Reno,'), then the next chunk is going to be the state abbreviation, which is the last one in that location set. How to figure out which string chunk is the last one in a set of location strings? It seems safe to assume that if one string chunk ends in a comma (e.g. It's a little inelegant, but it will get the job done. Then we can just join all of the two location fields together initially, and split on the special character afterwards. One hack we can use here is to add a special character to the last string of a set of location strings. That means we can't just hard code the start and end index of each set of location strings to use in join. But "Las Vegas, NV" becomes after the split, with 3 elements. For example, "Reno, NV" becomes a 2-element list ( ) after our splitting operation above. The problematic fields are the location fields, as the number of string chunks may vary for each location. That makes it easy to hard code the number of elements that we should join() for those fields. I'm going to assume that three fields in each record always have the same number of elements: the date/time, the IP address, and the final number (call duration?). We can drop the empty string lines by checking for len(x) (which will evaluate to False if len = 0), and then split() the remaining lines by single spaces. ['Jan 6 12:30 PM Unavailable Las Vegas, NV Incoming, CL 2', Then data.split("\n") will yield a 7-element list: 4 lines with content and three empty string ( '') lines: data.split("\n") Let's assume the input data look exactly as posted, with lines of whitespace separating lines of content. The approach I'm taking is to split each content line into space-separated elements, and then combine the appropriate pieces. On the other hand, it's an opportunity to highlight some basic string parsing tactics in Python, so I'm going to treat this as a kind of annotated walkthrough that may be of some benefit to people who land on this post. I'm hesitant to just post an answer here, as the problem space isn't clearly defined, and there isn't any work included to show where you're getting stuck, or if you've tried anything at all.
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