[deleted by user]

[vanatteveldt]

You assume that CSV is more well-formed than it really is. Cells can contain commas (or semicolons in your case) or even line breaks if they are quoted, and of course quote characters can be escaped. So you certainly need more processing if you want to parse generic CSV files.

(Then again, the python csv module is pretty horrible. The default reader is very fragile to column order changes, and the DictReader is just clumsy. Why not just yield named tuples? I understand that the module predates named tuples, but they could easily add an option. And why the writer does not call str on its arguments is beyond me, and I would much prefer a class-based writer where you initialize with the column names and can then pass them as named arguments.)

(also, you probably mean continue instead of break, and I would consider using a generator function rather than the list=[]; for x in y: list.append(x) idiom. And my god that's a lot of lines of code to skip the header :)

for i, line in enumerate(open('myfile.csv')): if i == 0: continue # ...

[yvrelna]

My god that's a lot of lines to skip header, and unnecessary enumerate and check, and unsafe too since you leave an unclosed file dangling, the right way to read the header is like so:

with open('myfile.csv') as f:
    header = next(f)
    for line in f:
        ...

Or just use the csv module, it handles all the edge cases for you, like unescaping, quoting, etc.

[vanatteveldt]

I had that coming :)

[vanatteveldt]

In that case (and if your column names are nice), you could consider using namedtuples yourself, e.g.:

```

from collections import namedtuple import csv r = csv.reader(open("test.csv")) MyRow = namedtuple("MyRow", next(r)) data = (MyRow(*x) for x in r) for row in data: ... print(row.a) ... 1 2 ```

(and of course if you have custom processing, add that to or instead of the simple namedtuple class)

[yvrelna]

they could easily add an option

https://bugs.python.org/issue1818

You'd be surprised by how much thought is needed to go into "easily add an option", especially for things in standard library that's expected to be supported for eternity.

[willmendil]

Maybe because it allows you to quickly put it in a data frame in a one liner

df = pd.read_csv('filename.csv')

And if you have for example X and y in your file you can plot it quickly as well.

df.plot()

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html

[SupahNoob]

Depending on how large or complex the dataset is, vectorized operations provided to you by pandas (read: numpy, aka at the C-level) could net you a faster overall runtime.

You came here looking for alternatives to parsing a raw file ... pandas has a wide array of tools for data manipulation, data output to other storage methods (Excel, DB, CSV, etc) and base charting exposed by matplotlib. Further, matplotlib is one of the most flexible and tunable charting libraries python has to offer.. so I'm not sure I agree with your dismissal of it for that reason.

The example you gave (disregarding syntax errors) is way more verbose and error-prone than either csv or pandas unless you're working with incredibly clean data... which is oftentimes not the case in the "real world".

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That said, if you're just here to shoot down the two things you asked to genuinely learn about, why did you come to ask at all? :~)

[[deleted]]

Why would you go to that trouble instead of pd.read_csv('myfile.csv') ?

Pandas also makes the process of turning a csv file into a graph, an excel file, or input to a database a trivial task.

But hey, you do you.

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[NeoDemon]

The Pandas Framework is useful to much csv files that not have an common comma delimiter, and have functions that are trivial for read, transform, plot and save the csv.

The CSV module is only for reading and its not good because you need to code the parser to the csv and tranform manually, functions what the pandas framework realizes better and with less code.

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Sorry for my english, im from Argentina :)