We have a dataset which has approx 1.5MM rows. I would like to process that in parallel. The main function of that code is to lookup master information and enrich the 1.5MM rows. The master is a two column dataset with roughly 25000 rows. However i am unable to make the multi-process work and test its scalability properly. Can some one please help. The cut-down version of the code is as follows
import pandas
from multiprocessing import Pool
def work(data):
mylist =[]
#Business Logic
return mylist.append(data)
if __name__ == '__main__':
data_df = pandas.read_csv('D:\\retail\\customer_sales_parallel.csv',header='infer')
print('Source Data :', data_df)
agents = 2
chunksize = 2
with Pool(processes=agents) as pool:
result = pool.map(func=work, iterable= data_df, chunksize=20)
pool.close()
pool.join()
print('Result :', result)
Method work will have the business logic and i would like to pass partitioned data_df into work to enable parallel processing. The sample data is as follows
CUSTOMER_ID,PRODUCT_ID,SALE_QTY
641996,115089,2
1078894,78144,1
1078894,121664,1
1078894,26467,1
457347,59359,2
1006860,36329,2
1006860,65237,2
1006860,121189,2
825486,78151,2
825486,78151,2
123445,115089,4
Ideally i would like to process 6 rows in each partition.
Please help.
Thanks and Regards
Bala
First, work is returning the output of mylist.append(data), which is None. I assume (and if not, I suggest) you want to return a processed Dataframe.
To distribute the load, you could use numpy.array_split to split the large Dataframe into a list of 6-row Dataframes, which are then processed by work.
import pandas
import math
import numpy as np
from multiprocessing import Pool
def work(data):
#Business Logic
return data # Return it as a Dataframe
if __name__ == '__main__':
data_df = pandas.read_csv('D:\\retail\\customer_sales_parallel.csv',header='infer')
print('Source Data :', data_df)
agents = 2
rows_per_workload = 6
num_loads = math.ceil(data_df.shape[0]/float(rows_per_workload))
split_df = np.array_split(data_df, num_loads) # A list of Dataframes
with Pool(processes=agents) as pool:
result = pool.map(func=work, iterable=split_df)
result = pandas.concat(result) # Stitch them back together
pool.close()
pool.join()pool = Pool(processes=agents)
print('Result :', result)
My best recommendation is for you to use the chunksize parameter in read_csv (Docs) and iterate over. This way you wont crash your ram trying to load everything plus if you want you can for example use threads to speed up the process.
for i,chunk in enumerate(pd.read_csv('bigfile.csv', chunksize=500000)):
Im not sure if this answer your specific question but i hope it helps.
Related
I have this part of code in my application.
What I want is to iterate over each row in my data frame (pandas) and modify column to function result.
I tried to implement it with multiprocessing, but I'm to see if there is any faster and easier to implement way to do it.
Is there any simple way to run this part in parallel?
def _format(data: pd.DataFrame, context: pd.DataFrame)
data['context'] = data.apply(lambda row: get_context_value(context, row), axis=1)
The data frame I work with is not to large (10,000 - 100,000) and the function to evaluate the value to assign to the column take around 250ms - 500ms for one row. But the whole process for the size of the data frame takes to much.
Thanks
I have a project which it is done there: https://github.com/mjafari98/dm-classification/blob/main/inference.py
import pandas as pd
from functools import partial
from multiprocessing import Pool
import numpy as np
def parallelize(data, func, num_of_processes=8):
data_split = np.array_split(data, num_of_processes)
pool = Pool(num_of_processes)
data = pd.concat(pool.map(func, data_split))
pool.close()
pool.join()
return data
def run_on_subset(func, data_subset):
return data_subset.apply(func, axis=1)
def parallelize_on_rows(data, func, num_of_processes=8):
return parallelize(data, partial(run_on_subset, func), num_of_processes)
def a_function(row):
...do something ...
return row
df = ...somedf...
new_df = parallelize_on_rows(df, a_function)
I'm relatively new to python and very new to multithreading and multiprocessing. I've been trying to send out thousands of values (Approx. 70,000) into chunks through a web-based API and want it to return me data associated with all those values. The API can take on 50 values a batch at a time so for now as a test I have 100 values I'd like to send in 2 chunks of 50 values. Without multithreading, it would've taken me hours to finish the job so I've tried to use multithreading to improve performance.
The Issue: The code is getting stuck after performing only one task(first row, that even the header, not even the main values) on pool.map() part, I had to restart the notebook kernel. I've heard not to use multiprocessing on a notebook, so I've coded the whole thing on Spyder and ran it, but still the same. Code is below:
#create df data frame with
#some codes to get df of 100 values in
#2 chunks, each chunk contains 50 values.
output:
df = VAL
0 1166835704;1352357565;544477351;159345951;22...
1 354236462063;54666246046;13452466248...
def get_val(df):
data = []
v_list = df
s = requests.Session()
url = 'https://website/'
post_fields = {'format': 'json', 'data':v_list}
r = s.post(url, data=post_fields)
d = json.loads(r.text)
sort = pd.json_normalize(d, ['Results'])
return sort
if __name__ == "__main__":
pool = ThreadPool(4) # Make the Pool of workers
results = pool.map(get_val, df) #Open the df in their own threads
pool.close() #close the pool and wait for the work to finish
pool.join()
Any suggestions would be helpful. Thanks!
Can you check once with following
with ThreadPool(4) as pool:
results= pool.map(get_val, df) #df should be iterable.
print(results)
Also, pls.check if chunksize can be passed to threadpool as that can affect performance.
datasets = {}
datasets['df1'] = df1
datasets['df2'] = df2
datasets['df3'] = df3
datasets['df4'] = df4
def prepare_dataframe(dataframe):
return dataframe.apply(lambda x: x.astype(str).str.lower().str.replace('[^\w\s]', ''))
for key, value in datasets.items():
datasets[key] = prepare_dataframe(value)
I need to prepare the data in some dataframes for further analysis. I would like to parallelize the for loop that updates the dictionary with a prepared dataframe. This code will eventually run on a machine with dozens of cores and thousands of dataframes. On my local machine I do not appear to be using more than a single core in the prepare_dataframe function.
I have looked at Numba and Joblib but I cannot find a way to work with dictionary values in either library.
Any insight would be very much appreciated!
You can use the multiprocessing library. You can read about its basics here.
Here is the code that does what you need:
from multiprocessing import Pool
def prepare_dataframe(dataframe):
# do whatever you want here
# changes made here are *not* global
# return a modified version of what you want
return dataframe
def worker(dict_item):
key,value = dict_item
return (key,prepare_dataframe(value))
def parallelize(data, func):
data_list = list(data.items())
pool = Pool()
data = dict(pool.map(func, data_list))
pool.close()
pool.join()
return data
datasets = parallelize(datasets,worker)
Manager Code..
import pandas as pd
import multiprocessing
import time
import MyDF
import WORKER
class Manager():
'Common base class for all Manager'
def __init__(self,Name):
print('Hello Manager..')
self.MDF=MyDF.MYDF(Name);
self.Arg=self.MDF.display();
self.WK=WORKER.Worker(self.Arg); MGR=Manager('event_wise_count') if __name__ == '__main__':
jobs = []
x=5;
for i in range(5):
x=10*i
print('Manager : ',i)
p = multiprocessing.Process(target=MGR.WK.DISPLAY)
jobs.append(p)
p.start()
time.sleep(x);
worker code...
import pandas as pd
import time
class Worker():
'Common base class for all Workers'
empCount = 0
def __init__(self,DF):
self.DF=DF;
print('Hello worker..',self.DF.count())
def DISPLAY(self):
self.DF=self.DF.head(10);
return self.DF
Hi I am trying to do multiprocessing. and i want to share a Data Frame address with all sub-processes.
So in above from Manager Class I am spawning 5 process , where each sub-process required to use Data Frame of worker class , expecting that each sub process will share reference of worker Data Frame. But unfortunately It is not happening..
Any Answer welcome..
Thanks In Advance,,.. please :)..
This answer suggests using Namespaces to share large objects between processes by reference.
Here's an example of an application where 4 different processes can read from the same DataFrame. (Note: you can't run this on an interactive console -- save this as a program.py and run it.)
import pandas as pd
from multiprocessing import Manager, Pool
def get_slice(namespace, column, rows):
'''Return the first `rows` rows from column `column in namespace.data'''
return namespace.data[column].head(rows)
if __name__ == '__main__':
# Create a namespace to place our DataFrame in it
manager = Manager()
namespace = manager.Namespace()
namespace.data = pd.DataFrame(pd.np.random.rand(1000, 10))
# Create 4 processes
pool = Pool(processes=2)
for column in namespace.data.columns:
# Each pool can access the same DataFrame object
result = pool.apply_async(get_slice, [namespace, column, 5])
print result._job, column, result.get().tolist()
While reading from the DataFrame is perfectly fine, it gets a little tricky if you want to write back to it. It's better to just stick to immutable objects unless you really need large write-able objects.
Sorry about the necromancy.
The issue is that the workers must have unique DataFrame instances. Almost all attempts to slice, or chunk, a Pandas DataFrame will result in aliases to the original DataFrame. These aliases will still result in resource contention between workers.
There a two things that should improve performance. The first would be to make sure that you are working with Pandas. Iterating row by row, with iloc or iterrows, fights against the design of DataFrames. Using a new-style class object and the apply a method is one option.
def get_example_df():
return pd.DataFrame(pd.np.random.randint(10, 100, size=(5,5)))
class Math(object):
def __init__(self):
self.summation = 0
def operation(self, row):
row_result = 0
for elem in row:
if elem % 2:
row_result += elem
else:
row_result += 1
self.summation += row_result
if row_result % 2:
return row_result
else:
return 1
def get_summation(self):
return self.summation
Custom = Math()
df = get_example_df()
df['new_col'] = df.apply(Custom.operation)
print Custom.get_summation()
The second option would be to read in, or generate, each DataFrame for each worker. Then recombine if desired.
workers = 5
df_list = [ get_example_df() ]*workers
...
# worker code
...
aggregated = pd.concat(df_list, axis=0)
However, multiprocessing will not be necessary in most cases. I've processed more than 6 million rows of data without multiprocessing in a reasonable amount of time (on a laptop).
Note: I did not time the above code and there is probably room for improvement.
I am reading in hundreds of HDF files and processing the data of each HDF seperately. However, this takes an awful amount of time, since it is working on one HDF file at a time. I just stumbled upon http://docs.python.org/library/multiprocessing.html and am now wondering how I can speed things up using multiprocessing.
So far, I came up with this:
import numpy as np
from multiprocessing import Pool
def myhdf(date):
ii = dates.index(date)
year = date[0:4]
month = date[4:6]
day = date[6:8]
rootdir = 'data/mydata/'
filename = 'no2track'+year+month+day
records = read_my_hdf(rootdir,filename)
if records.size:
results[ii] = np.mean(records)
dates = ['20080105','20080106','20080107','20080108','20080109']
results = np.zeros(len(dates))
pool = Pool(len(dates))
pool.map(myhdf,dates)
However, this is obviously not correct. Can you follow my chain of thought what I want to do? What do I need to change?
Try joblib for a friendlier multiprocessing wrapper:
from joblib import Parallel, delayed
def myhdf(date):
# do work
return np.mean(records)
results = Parallel(n_jobs=-1)(delayed(myhdf)(d) for d in dates)
The Pool classes map function is like the standard python libraries map function, you're guaranteed to get your results back in the order that you put them in. Knowing that, the only other trick is that you need to return results in a consistant manner, and the filter them afterwards.
import numpy as np
from multiprocessing import Pool
def myhdf(date):
year = date[0:4]
month = date[4:6]
day = date[6:8]
rootdir = 'data/mydata/'
filename = 'no2track'+year+month+day
records = read_my_hdf(rootdir,filename)
if records.size:
return np.mean(records)
dates = ['20080105','20080106','20080107','20080108','20080109']
pool = Pool(len(dates))
results = pool.map(myhdf,dates)
results = [ result for result in results if result ]
results = np.array(results)
If you really do want results as soon as they are available you can use imap_unordered