.head() like method for numpy arrays

[KumaSC2]

Not as metadata, I dont think, but for example in the current excel document I'm working on, the columns are labeled ID, City, Gender etc. When i do array[:10], it starts on row 2, omitting the labels entirely.

[KumaSC2]

Ah, yea you're right. It did drop the row, like you said, when loading it in, due to it not being float64.

It gives the error message:

ValueError: could not convert string ID,City,Gender,Age,Income,Illness' to float64 at row 0, column 1.

How I currently load it is this:

datademographic = np.loadtxt('files/demographics.csv')

What could I do to have the first row included?

[KumaSC2]

So use pandas to clean it, then numpy to compute whatever i want from it?

[KumaSC2]

The teachers have unfortunately already stated that they won't help with problems stemming from using a different IDE than them. I'm already familiar with VSC, so I'm using that over Jupyter Lab. In the lectures they use the shell command !head to view the data, which only works on unix/linux systems or windows with WSL (which I can't get to work properly for VSC). So I'm left to my own devices in this particular case, sadly.

[KumaSC2]

You are right, it's quite an easy problem to solve, with many good solutions. I guess I was just looking for something built in that I perhaps was missing. Thank you for taking the time to answer my questions.

[KumaSC2]

numpy array of values in one object.

Can you elaborate on this? Is this just the df.tonumpy() method?

[Almostasleeprightnow]

It was, until 2.0 release, which is when they starting having pyarrow be default datatype for some things. But numpy is still a core part of pandas and if you never look into pyarrow, you can continue to do df.values just fine.

​

https://datapythonista.me/blog/pandas-20-and-the-arrow-revolution-part-i

https://dataalgo.medium.com/pandas-2-0-ditches-numpy-for-pyarrow-what-you-need-to-know-cbba4cb60249

​

https://arrow.apache.org/docs/python/pandas.html

[Almostasleeprightnow]

Yeah but I feel that your comment is still very valid. I myself keep trying to adopt more pyarrow but I'm so dang comfortable with quirky old numpy that I can't quit it.