Numpy vstack datetime6/7/2023 ![]() Numpy vstack, Numpy hstack, and Numpy concatenate are all somewhat similar. Numpy vstack is actually one of several Numpy tools for combining Numpy arrays. It’s essentially a data manipulation tool in NumPy. Previously, I had converted the datetime elements to floats and used vstack () without issue. NumPy vstack is a tool for combining together Numpy arrays. I have a Pandas dataframe with datetime elements ('date') and floats ('cups) and am wondering if there's anyway to use np.vstack () to stack the two. _133.to_numpy().reshape(-1,4).T gives the x_month_begin array directly. np.vstack () with datetime and float data types. ![]() That's a 'pure' pandas operation.Īrray([ True, True, True, True, True, True, True, True, True, Consider the example below for some examples: import numpy as np creating a date today np. The data type is called datetime64, so named because datetime is already taken by the datetime library included in Python. This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Working with datetime: Numpy has core array data types which natively support datetime functionality. ![]() Stack arrays in sequence vertically (row wise). The original `DatetimeIndex` can be tested against the timestamp. numpy.vstack(tup,, dtypeNone, casting'samekind') source. is the conversion that produces your error. If given a non-numpy object it will try, naively, to convert it, e.g. But none of the numpy code is pandas aware. The N-dimensional array ( ndarray) Scalars. For learning how to use NumPy, see the complete documentation. Pandas is built on numpy, or at least uses numpy arrays to store its data. This reference manual details functions, modules, and objects included in NumPy, describing what they are and what they do. Pandas/_libs/tslibs/c_timestamp.pyx in pandas._timestamp._Timestamp._richcmp_() TypeError Traceback (most recent call last) You manipulate that into a (n,3) array (I suspect this can be done more directly with a reshape and possible transpose): In : x_month_begin = np.vstack(np.split(x_month_begin, 3)) The same thing as a numpy array: In : x_month_begin.valuesĪrray(['T00:00:00.000000000', 'T00:00:00.000000000', Option 1 import matplotlib.dates as dates convertedDate dates.num2date (t) Error: can't multiply sequence by non-int of type 'float' Option 2 ( from here) from datetime import datetime convertedDate datetime.strptime (t, 'YYmmddHHMMss') Error: strptime () argument 1 must be str, not numpy. So you start off with a pandas structure: In : x_month_begin
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