On Fri, Feb 15, 2019 at 5:12 AM Mike C <tmrs...@gmail.com> wrote: > The original data was in CSV format. I read it in using pd.read_csv(). It > does have column names, but no row names. I don’t think numpy reads csv > files. >
If you read a file into a pandas structure, it will have row labels. The default labels are integers that correspond to the ordinal positions of the values. Numpy reads files. https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.loadtxt.html https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.genfromtxt.html I prefer file IO in pandas, so I don't know which function will better suite your needs. And also, when I do a[2:5]-b[:3], it does not throw any “index out of > range” error. I was able to catch that, but in both Matlab and R. You get > an error. This is frustrating!! > That's a feature of python in general, not numpy in particular. Every language has its own quirks. The more you immerse yourself in them, the quick you'll learn to adapt to them. -paul > > > ------------------------------ > *From:* NumPy-Discussion <numpy-discussion-bounces+tmrsg11= > gmail....@python.org> on behalf of Juan Nunez-Iglesias <jni.s...@gmail.com > > > *Sent:* Friday, February 15, 2019 4:15 AM > *To:* Discussion of Numerical Python > *Subject:* Re: [Numpy-discussion] [SciPy-User] Why slicing Pandas column > and then subtract gives NaN? > > > I don’t have index when I read in the data. I just want to slice two > series to the same length, and subtract. That’s it! > > I also don’t what numpy methods wrapped within methods. They work, but > hard do understand. > > How would you do it? In Matlab or R, it’s very simple, one line. > > > Why are you using pandas at all? If you want the Matlab equivalent, use > NumPy from the beginning (or as soon as possible). I personally agree with > you that pandas is too verbose, which is why I mostly use NumPy for this > kind of arithmetic, and reserve pandas for advanced data table type > functionality (like groupbys and joining on indices). > > As you saw yourself, a.values[1:4] - b.values[0:3] works great. If you > read in your data into NumPy from the beginning, it’ll be a[1:4] - b[0:3] > just like in Matlab. (Or even better: a[1:] - b[:-1]). > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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