import multiprocessing as mp
pool = mp.Pool(processes=mp.cpu_count())
def func( arg ):
idx,row = arg
# Edit df
return row
new_rows = pool.map( func, [(idx,row) for idx,row in data_all.iterrows()])
data_all_new = pd.concat( new_rows )
Here is what the above code is Doing:
1. Create a pool of workers.
2. Create a list of arguments to pass to the function.
3. Map the function to the list of arguments.
4. Concatenate the results.
It was moved to tf.random.normal (along with all the other tf.random_* functions) Here is what the above code is Doing: 1. Creating a random normal distribution with mean = 0 and std = 1 2. Creating a random uniform distribution with low = -1 and high = 1 3. Creating a random uniform distribution with…
In [55]: import pandas as pd equiv = {7001:1, 8001:2, 9001:3} df = pd.DataFrame( {“A”: [7001, 8001, 9001]} ) df[“B”] = df[“A”].map(equiv) print(df) A B 0 7001 1 1 8001 2 2 9001 3 [3 rows x 2 columns] Here is what the above code is Doing: 1. Create a dictionary that maps the values…
# Basic syntax: # Using list comprehension: selected_elements = [your_list[index] for index in indices] # Example usage: # Say you want to return the elements at indices 1, 4, 6 your_list = [5, 7, 23, 42, 11, 17, 27] indices = [1, 4, 6] # Running: [your_list[index] for index in indices] # Would return: –>…
from tkinter import * #creating tkinter window root=Tk() #setting window’s dimensions root.geometry(‘520×520′) root.reziable(0,0) #Don’t allow resizing in the x or y direction root.mainloop() Here is what the above code is Doing: 1. We’re creating a Tkinter window called root. 2. We’re setting the dimensions of the window to 520×520. 3. We’re making the window non-resizable….
o=[‘[‘,'{‘,'(‘] c=[‘]’,’}’,’)’] def check(s): a=[] for i in s: if i in o: a.append(i) elif i in c: pos=c.index(i) if((len(a)>0)and(o[pos]==a[len(a)-1])): a.pop() else: return “Unbalaced” if len(a)==0: return “bal” else: return “Unbal” s='{{[])}}’ string=check(s) print(string) Here is what the above code is Doing: 1. We have a list of opening and closing brackets. 2. We iterate…
df = pd.DataFrame({‘A’:[61.75, 10.25], ‘B’:[0.62, 0.45]}) print (df) A B 0 61.75 0.62 1 10.25 0.45 df[‘A’] = df[‘A’].div(100).round(2) #same as #df[‘A’] = (df[‘A’] / 100).round(2) print (df) A B 0 0.62 0.62 1 0.10 0.45 Here is what the above code is Doing: 1. Create a dataframe with two columns, A and B. 2….