>>> import random >>> thelist = ['a', 'b', 'c', 'd'] >>> random.shuffle(thelist) >>> thelist ['d', 'a', 'c', 'b']
Here is what the above code is Doing:
1. We import the random module.
2. We create a list of four letters.
3. We use the shuffle() function to shuffle the list.
4. We print the list to see the result.
import numpy as np randi_arr = np.random.randint(start, end, dimensions) #random integers will be sampled from [start, end) (end not inclusive) #end is optional; if end is not specified, random integers will be sampled from [0, start) (start not inclusive) #dimensions can be specified as shown here; (m,n) #2D array with size ‘m x n’ Here…
emb = discord.Embed(title=’Команды Админов’) emb.add_field(name = settings[‘prefix’]+’clear [count]’, value=’Очистка чата.\n’, inline=False) emb.add_field(name = settings[‘prefix’]+’ban [name]’, value=’Бан пользователя.\n’, inline=False) emb.add_field(name = settings[‘prefix’]+’muted [name]’, value=’Замютить пользователя.\n’, inline=False) emb.add_field(name = settings[‘prefix’]+’setprefix [prefix]’, value=’Сменить префикс бота.\n’, inline=False) emb.add_field(name = settings[‘prefix’]+’restart’, value=’Перезапуск бота.\n’, inline=False) Here is what the above code is Doing: 1. We’re creating a new Embed object, and…
with open(“file.txt”) as f: out = [] for line in f: for word in line.split(): out.append(word) if word.endswith((‘.’, ‘!’)): print(‘ ‘.join(out)+’\n’) out.clear() Here is what the above code is Doing: 1. It opens the file and reads it line by line. 2. For each line, it splits the line into words. 3. For each word,…
It is not possible to pair it with a phone Here is what the above code is Doing: 1. It is importing the BluetoothSocket class from the Python sockets module. 2. It is creating a socket object called btSocket. 3. It is binding the socket to the localhost address and port 0. 4. It is…
df[‘C’] = df.apply(lambda row: row[‘A’] + row[‘B’], axis=1) Here is what the above code is Doing: 1. Create a new column called ‘C’ 2. For each row in the dataframe, apply the function lambda row: row[‘A’] + row[‘B’] 3. The function is applied to each row, and the result is assigned to the column ‘C’
annotations.groupby([‘bookid’,’conceptid’], sort=False)[‘weight’].max() Here is what the above code is Doing: 1. We’re grouping the dataframe by bookid and conceptid. 2. We’re sorting the dataframe by bookid and conceptid. 3. We’re taking the max weight for each group.