Okay, so, “justin neil,” right? Sounds kinda cryptic, but let me break down what I did. It’s a deep dive into some data manipulation, nothing too fancy, but useful.
First off, I had this massive text file, you know, the kind that makes your text editor lag? Yeah, one of those. Inside, there were lines and lines of info I needed to parse. The goal? Extract specific data points and transform them into something usable. Think turning raw event logs into structured data for analysis.
So, I fired up Python. It’s my go-to for this kind of stuff. I started with the basics, reading the file line by line. The initial code looked something like this:
with open('justin_neil_raw_*', 'r') as f:
for line in f:
# Process each line here
print(line) #Just for test
Yep, super simple. Just wanted to make sure I could read the file correctly. The printing of each line was just for the sake of testing if the file was being read correctly.
Next, the real work began. Each line in the file had a specific format, kind of like this: “Timestamp: [DATE], Event: [EVENT_TYPE], User: [USER_ID], Value: [NUMERIC_VALUE]”. I needed to split each line based on these delimiters.
I used Python’s string manipulation to split the data. Here’s the snippet:
with open('justin_neil_raw_*', 'r') as f:
for line in f:
parts = *(', ')
timestamp = parts[0].split(': ')[1]
event = parts[1].split(': ')[1]
user = parts[2].split(': ')[1]
value = parts[3].split(': ')[1]
print(f"Timestamp: {timestamp}, Event: {event}, User: {user}, Value: {value}")#Just for test
It was a bit clunky. I split by commas first, then further split each part by the colon. This gave me the timestamp, event type, user ID, and numeric value for each entry. I printed them out just to make sure everything was being extracted correctly.
Now, this is where things got a bit interesting. The `value` field wasn’t always a number. Sometimes it was “N/A” or some other string. So, I added a check to handle these cases.
with open('justin_neil_raw_*', 'r') as f:
for line in f:
parts = *(', ')
timestamp = parts[0].split(': ')[1]
event = parts[1].split(': ')[1]
user = parts[2].split(': ')[1]
value = parts[3].split(': ')[1]
try:
value = float(value)
except ValueError:
value = None
print(f"Timestamp: {timestamp}, Event: {event}, User: {user}, Value: {value}") #Just for test
I wrapped the conversion to float in a `try…except` block. If it failed (because the value wasn’t a number), I set the value to `None`. This way, I wouldn’t get any errors and could handle the missing data later.
Finally, I stored all this processed data into a list of dictionaries. Each dictionary represented a row of data, with keys for timestamp, event, user, and value.
data = []
with open('justin_neil_raw_*', 'r') as f:
for line in f:
parts = *(', ')
timestamp = parts[0].split(': ')[1]
event = parts[1].split(': ')[1]
user = parts[2].split(': ')[1]
value = parts[3].split(': ')[1]
try:
value = float(value)
except ValueError:
value = None
'timestamp': timestamp,
'event': event,
'user': user,
'value': value
print(data) #Final output
And that was it! The `data` list now contained all the information I needed, neatly organized and ready for further analysis. I could then use Pandas, or any other tool, to analyze the data. Basically, I started with a messy text file and ended up with a structured dataset. Not groundbreaking, but a solid win in my book.
