Skip to main content

Adding count information to mapreduce output

I have data that came from sys.stdout mapper.py program as follows:

input from stdout of previous mapper.py

chevy, {mod: spark | col: brown}
chevy, {mod: equinox | col: red}
honda, {mod:civic | col:black}
honda, {mod:accord | col:white} 
honda, {mod:crv | col:pink} 
honda, {mod:hrv | col:gray} 
toyota, {mod:corola | col:white}

I would like to write a reducer.py or maybe even another mapper that takes this information and produces an output such as:

Expected output

chevy, {mod: spark | col: brown | total:2}
chevy, {mod: equinox | col: red | total:2}
honda, {mod:civic | col:black | total:4}
honda, {mod:accord | col:white | total:4} 
honda, {mod:crv | col:pink | total:4} 
honda, {mod:hrv | col:gray | total:4} 
toyota, {mod:corola | col:white | total:1}

the total is only for the keys (car brand), so chevy appears twice, honda appears 4 times, and toyota 1.

I have tried a reducer.py program and it did not work. The program I wrote looks like this:

curr_k = None
curr_v = None
k = None
curr_count = 0

for car in sys.stdin:
    car_split = car.split('|')
    k = car_split[0]
    v = car_split[1]

    if curr_k == k:
        print(curr_k, curr_v, 'total:',curr_count)
        curr_count += 1

    else:
        if curr_k:
            print(curr_k, curr_v, 'total:',curr_count)
        curr_k = k
        curr_count = 1
    
if curr_k == k:
    print(curr_k, curr_v, 'total:',curr_count)

The above code gave me the following answer:

chevy, {mod: spark | col: brown | total:1}
chevy, {mod: equinox | col: red | total:2}
honda, {mod:civic | col:black | total:1}
honda, {mod:accord | col:white | total:2} 
honda, {mod:crv | col:pink | total:3} 
honda, {mod:hrv | col:gray | total:4} 
toyota, {mod:corola | col:white | total:1}

But that is not what I am looking for.



source https://stackoverflow.com/questions/74173010/adding-count-information-to-mapreduce-output

Comments

Popular posts from this blog

Why is my reports service not connecting?

I am trying to pull some data from a Postgres database using Node.js and node-postures but I can't figure out why my service isn't connecting. my routes/index.js file: const express = require('express'); const router = express.Router(); const ordersCountController = require('../controllers/ordersCountController'); const ordersController = require('../controllers/ordersController'); const weeklyReportsController = require('../controllers/weeklyReportsController'); router.get('/orders_count', ordersCountController); router.get('/orders', ordersController); router.get('/weekly_reports', weeklyReportsController); module.exports = router; My controllers/weeklyReportsController.js file: const weeklyReportsService = require('../services/weeklyReportsService'); const weeklyReportsController = async (req, res) => { try { const data = await weeklyReportsService; res.json({data}) console...

How to show number of registered users in Laravel based on usertype?

i'm trying to display data from the database in the admin dashboard i used this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count(); echo $users; ?> and i have successfully get the correct data from the database but what if i want to display a specific data for example in this user table there is "usertype" that specify if the user is normal user or admin i want to user the same code above but to display a specific usertype i tried this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count()->WHERE usertype =admin; echo $users; ?> but it didn't work, what am i doing wrong? source https://stackoverflow.com/questions/68199726/how-to-show-number-of-registered-users-in-laravel-based-on-usertype

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...