Finally, we use the-operator to remove rows where the gender column is “M” fromfiltered_df. We do this by first using thewhich()function to find the indices of the rows where gender is “M”, and then using th
You can also use the numpy.lexsort() method if you need to remove the duplicate rows from a NumPy array. main.py import numpy as np arr = np.array([[3, 3, 5, 6, 7], [3, 3, 5, 6, 7], [7, 7, 8, 9, 10]]) print(arr) print('-' * 50) sorted_indices = np.lexsort...
"""Convert indices to time. Parameters --- index : list-like | int List of ints or int representing points in time. use_first_samp : boolean If True, the time returned is relative to the session onset, else relative to the recording onset. Returns --- times : ndarray Times correspond...
import time from zeus.monitor.power import PowerMonitor if __name__ == "__main__": gpu_indices = [0] monitor = PowerMonitor(gpu_indices) st = time.time() time.sleep(5) end = time.time() while st < end: power = monitor.get_power(st) st += 0.1 print(f"{st}, {power[0]...
Remove specific elements based on the given indices # Import numpyimportnumpyasnp# Creating a numpy arrayarr=np.array([10,20,30,40,50,60,70])# Display original arrayprint("Orignal array:\n",arr,"\n")# Defining the indices of the elements# to deletemultiples=[0,2,3]# Deleting these...
The function's name implies that it will provide a tuple containing the unique rows and their corresponding indices, in that specific order. Another option available is to utilizepandas. import pandas as pd pd.DataFrame(data).drop_duplicates().values ...
mask = tokenizer.mask_id # placeholder to calculate indices here batch_size=1, device='gpu', single_res_domain=False, chain='A'): mask = tokenizer.mask_id # placeholder to calculate indices here start = tokenizer.start_id stop = tokenizer.stop_id@...
Let's create a `DataFrame` with bonus points for each person from October to December:" ] }, @@ -5413,7 +5414,7 @@ "source": [ "Looks like the addition worked in some cases but way too many elements are now empty. That's because when aligning the `DataFrame`s, some columns ...
return XBS[:, indices] def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags._xfail_checks = { "check_estimators_pickle": ( "Current Scipy implementation of _bsplines does not" "support const memory views." ), } return tags 7 changes: 0 additions & 7 deletions ...