x = {2, 3, 5, 6}
y = {1, 2, 3, 4}
z = x.intersection(y)
Here is what the above code is Doing:
1. We create two sets, x and y.
2. We use the intersection method to find the common elements between x and y.
3. We store the result in a new set, z.
import random return 1 if random.random() < 0.5 else -1 Here is what the above code is Doing: 1. It's creating a random number between 0 and 1. 2. If that number is less than 0.5, it returns 1. 3. Otherwise, it returns -1. This is a pretty simple function, but it's not very efficient....
import pandas as pd pd.DataFrame(np_array).to_csv(“path/to/file.csv”) Here is what the above code is Doing: 1. Creating a DataFrame from the NumPy array. 2. Writing the DataFrame to a CSV file. The CSV file will have the same number of rows as the NumPy array and the same number of columns. You can also read a CSV…
today = ‘Sunday’ if not today==’Sunday’: print(‘Go to work.’) else: print(‘Take rest.’) Here is what the above code is Doing: 1. It’s creating a variable called today and assigning it the value ‘Sunday’. 2. It’s checking if today is not equal to ‘Sunday’. 3. If today is not equal to ‘Sunday’, it prints ‘Go to…
import pandas as pd data = pd.read_csv(filename, encoding= ‘unicode_escape’) Here is what the above code is Doing: 1. Importing the pandas library 2. Reading the CSV file into a variable called data 3. Setting the encoding to unicode_escape The encoding is important because it tells Python how to read the data. The unicode_escape encoding is…
logger = fmeobjects.FMELogFile() logger.logMessageString(“{}”.format(‘Error’), fmeobjects.FME_ERROR) Here is what the above code is Doing: 1. Importing the FME Objects module 2. Creating a logger object 3. Logging a message to the FME log file
df.loc[df[‘column_name’] == some_value] Here is what the above code is Doing: 1. We’re using the .loc method to select all of the rows from our dataframe where the value in the ‘column_name’ column is equal to ‘some_value’. 2. We’re storing the result of this selection in a new dataframe called ‘new_df’.