Appending data in Python - arrays

Question

I have a time data with dimensions (95,). I wrote the following code to extract the year, month and day to create an array of dimension (95,3). However, the following code is able to create an array of dimension (285,). How can I create the new time array with dimension (95,3) where the first column represents year, second column - month and the last column the day.
newtime = np.array([])
for i in range(len(time)):
a = seconds_since_jan_1_1993_to_datetime(time[i])
time_year = float(a.strftime("%Y"))
time_mon = float(a.strftime("%m"))
time_day = float(a.strftime("%d"))
newtime = np.append(newtime, np.array([time_year, time_mon, time_day]))
For example, I have an input array with elements array([725696054.99044609, 725696056.99082708, 725696058.99119401, ...])
I want an output of the following form:
Col1 Col2 Col3
2015.0 12.0 31.0
2015.0 12.0 31.0
2015.0 12.0 31.0
Look forward to your suggestions or help.

Answer 1

My suggestion would be working with a dataframe format.
An easy fix to your code would be:
newtime = pd.DataFrame([], columns=['year','month','day'])
for i in range(len(time)):
a = seconds_since_jan_1_1993_to_datetime(time[i])
time_year = float(a.strftime("%Y"))
time_mon = float(a.strftime("%m"))
time_day = float(a.strftime("%d"))
newtime.loc[len(newtime)] = [time_year, time_mon, time_day]
hope that helps!

Answer 2

The dataframe is a good option. However, if you want to keep an array, you can simply use the reshape() function of numpy. Here is an example code :
import numpy as np
newtime = np.array([])
for i in range(12):
# Dummy data generated here, using floats like in the original post
time_year = float(2015.0)
time_mon = float(1.0*i)
time_day = float(31.0)
newtime = np.append(newtime,np.array([time_year, time_mon, time_day]))
newtime = newtime.reshape((-1,3))
Note the argument in the reshape function: (-1,3) will tell numpy to make the second dimension 3, computing automatically the first dimension. Now, if you print newtime, you should see:
[[ 2.01500000e+03 0.00000000e+00 3.10000000e+01]
[ 2.01500000e+03 1.00000000e+00 3.10000000e+01]
[ 2.01500000e+03 2.00000000e+00 3.10000000e+01]
[ 2.01500000e+03 3.00000000e+00 3.10000000e+01]
[ 2.01500000e+03 4.00000000e+00 3.10000000e+01]
[ 2.01500000e+03 5.00000000e+00 3.10000000e+01]
[ 2.01500000e+03 6.00000000e+00 3.10000000e+01]
[ 2.01500000e+03 7.00000000e+00 3.10000000e+01]
[ 2.01500000e+03 8.00000000e+00 3.10000000e+01]
[ 2.01500000e+03 9.00000000e+00 3.10000000e+01]
[ 2.01500000e+03 1.00000000e+01 3.10000000e+01]
[ 2.01500000e+03 1.10000000e+01 3.10000000e+01]]