Please convert below code to execute parallel, Here I'm trying to map nested dictionary with pandas column values. The below code works perfectly but consumes lot of time. Hence looking to parallelize the for loop(Note: df.replace(Source_Dictionary) also did the job but takes triple the time of below code).

df = pd.DataFrame({'one':['bab'],'two':['abb'],'three':['bb']})
Source_Dictionary = {'one':{'dadd':1,'bab':1.5},'two':{'ab':2},'three':{'cc':1,'bb':3}}
required_columns = ['one','two','three']
def Feature_Map(x):df[x] = df[x].map(Source_Dictionary[x]).fillna(0)for i in required_columns:Feature_Map(i)
print(df)one  two  three
0  1.5  0.0      3

To speed up your execution you can use multi processing. Number of processes and its performance depends on the resource provided. Let's suppose you can afford 4 processes to be running in parallel.

Your function:

def Feature_Map(x):
df[x] = df[x].map(Source_Dictionary[x]).fillna(0)

Multi processing:

from multiprocessing.pool import ThreadPool
pool = ThreadPool(processes=4)
for i in required_columns:pool.apply_async(Feature_Map, (i))

You can also implement code for waiting till the process has finished execution before exiting.

You can refer to https://docs.python.org/2/library/multiprocessing.html for detailed usage.