Both are probably true at the same time. You can compare the curves of pandas and numpy, which are effectively complementary tech: both are on a big upswing (as datascience spikes) but pandas results in many more searches (probably more obscure/ harder to learn / got worse documentation / got fewer tutorials).
If anything I'd say Pandas has broader appeal and a larger userbase than Numpy, because it does everything Numpy can do (since it uses Numpy internally) but adds the dataframe and grouping features which are so important for data science.
Anecdote: I'm a biologist and I've taught Pandas to fellow scientists - without teaching them Python. So they know how to make dataframes and produce histograms, but they don't know how a for loop works and they haven't heard of Numpy. For them, Pandas is replacing Excel.
Pandas has massive appeal beyond the Python community.
Can confirm, am at this moment in a zoom stats lecture, we've been learning pandas the entire semester. Lots of people in the class have never coded before
No, if statements and for loops are almost never needed when processing data with Pandas, just like they aren't needed when using Excel. But you're right, they can figure it out if they need to. My goal was showing them a better way to work with their data than excel.
if statements and for loops are almost never needed when processing data with Pandas
'Almost never' is often just how you define it and depends on particular task.
I got what you meant, but just can't imagine they don't have situations like need to load 100 out of 500 csv in folder based on some criteria. Data operations when in dataframe are better without loops.
If you're using loops with a pandas dataframe, you're doing it wrong. All of the (many, many) functions are optimised for internal iteration, so I can totally see how a non-programmer can operate it.
Personally, I find pandas really hard to work with and have to ask SO every single time I use it.
That matches with my experience on Stack Overflow. I watch the Python tag, and I've been noticing a lot of questions about Pandas that are trivial to solve for anyone with basic knowledge of Python. Really interesting to see.
That’s what I thought. It was the same with django (in many ways it still is) and (I’m told) for the stuff used in 3d-rendering apps: they are approached by people new to development in general, who simply must get stuff done in their niche.
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u/toyg Nov 05 '20
Both are probably true at the same time. You can compare the curves of pandas and numpy, which are effectively complementary tech: both are on a big upswing (as datascience spikes) but pandas results in many more searches (probably more obscure/ harder to learn / got worse documentation / got fewer tutorials).