Alright, let me tell you about my little “waylon mercy” adventure. It’s not as dramatic as some coding stories, but it was a solid learning experience.
So, I started with this idea, right? I wanted to mess around with some data manipulation using Python, specifically, pulling some info, cleaning it up, and then spitting it out in a usable format. I knew about `pandas` and figured this would be a good chance to really dig in.
First thing’s first, I grabbed the data. It was a CSV file, nothing fancy. I used `*_csv()` to load it into a DataFrame. Pretty straightforward.
import pandas as pd
df = *_csv("my_*")
print(*())
Okay, so I ran that, printed the first few rows using `.head()`, and BAM! Messy data. Missing values all over the place, inconsistent formatting, the whole shebang. This is where the “mercy” part comes in, because that data was not showing any mercy on my sanity.
Next up, cleaning time. I started with the missing values. Decided to fill them with something sensible. In some columns, I used the mean, in others, I used zero, depending on what the column represented. Here’s a snippet:
Then, I tackled the formatting. Some columns had strings with leading/trailing spaces. Ugh. `.strip()` to the rescue! And some date columns were in weird formats, so I used `*_datetime()` to get them into a consistent date format.
df['string_column'] = df['string_column'].*()
df['date_column'] = *_datetime(df['date_column'])
Now, this is where it got a bit tricky. I needed to create a new column based on the values in two existing columns. It was kind of like an “if this, then that” situation. I used `*()` with a lambda function to do this.
df['new_column'] = *(lambda row: 'Value1' if row['column1'] > 10 and row['column2'] == 'SomeValue' else 'Value2', axis=1)
That line was a bit of a head-scratcher at first. `axis=1` is crucial, tells pandas to apply the function row-wise, not column-wise. Got tripped up on that one for a bit. Remember to watch your axis!
After all the cleaning and manipulation, I had a DataFrame that looked… decent. I saved it to a new CSV file.
*_csv("cleaned_*", index=False)
The `index=False` bit is important. Don’t want to save the DataFrame index as a column in the CSV.
What I Learned
Pandas is Powerful: Seriously, it can handle a ton.
Data Cleaning is 80% of the Job: And it’s the least glamorous part.
Read the Docs: I spent a lot of time on the `pandas` documentation site. Worth it.
`apply()` Can Be Confusing: Especially with lambda functions. But powerful once you get the hang of it.
Axis Matters: Don’t forget `axis=0` vs `axis=1`. It’ll mess you up.
So, that’s my “waylon mercy” story. A simple data manipulation task, but it reinforced some fundamental `pandas` concepts and reminded me that clean data is a beautiful thing. Now, time for a beer!
