In NumPy, you can reshape time series data using the numpy.reshape function. Reshaping can be useful for rearranging the data into a different format that is more suitable for analysis or visualization.
Here is an example of how to reshape a two-dimensional NumPy array of time series data:
import numpy as np# Create a datetime64 index for the time series dataindex = np.arange('2022-01-01', '2022-01-11', dtype='datetime64[D]')# Create a two-dimensional NumPy array to store the datadata = np.array([ [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1], [11.2, 12.3, 13.4, 14.5, 15.6, 16.7, 17.8, 18.9, 19.0, 20.1]])# Reshape the data into a three-dimensional arrayreshaped_data = data.reshape((2, 5, 2))# Print the original and reshaped dataprint("Original data:")print(data)print("Reshaped data:")print(reshaped_data) |
In this example, we first create a numpy.datetime64 index for the time series data, and a two-dimensional NumPy array to store the data. We then reshape the data into a three-dimensional NumPy array with dimensions (2, 5, 2). This reshapes the data into a format where the first dimension corresponds to the column names, the second dimension corresponds to the dates, and the third dimension corresponds to the data values.
You can also use the numpy.transpose function to transpose the time series data. Transposing can be useful for rearranging the data so that it is organized differently. For example, you might want to transpose the data so that each row corresponds to a different date, and each column corresponds to a different variable:
# Transpose the datatransposed_data = np.transpose(data)# Print the original and transposed dataprint("Original data:")print(data)print("Transposed data:")print(transposed_data) |
This will transpose the data so that it has ten rows (one for each date) and two columns (one for each variable).