Remove the Outliers From the DataFrame in Python We will use the dataframe.drop function to drop the outlier points. Click here to more information about the function. For this, we will have to pass a list containing the indices of the outliers to the function. We can do this as follows...
You can also use thenumpy.lexsort()method if you need to remove the duplicate rows from a NumPy array. main.py importnumpyasnp 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(arr.T)print(sorted_indices)print('-'*...
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 the-operator to remove those rows fromfiltered_df. This creates a final da...
fun main() { val list = mutableListOf(1, 2, 3, 4, 5, 6, 7, 8, 9) for (i in list.indices step 2) { if (i + 1 < list.size) { list.removeAt(i + 1) } } println(list) // 输出: [1, 3, 5, 7, 9] } 在这个示例中,我们使用了 indices 属性来获取列表的索引范围,并通...
"""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...
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 ...