__main__ imposition in code

[karunakar2]

Make sure if a novice tries to access the module, he should be aware of encapsulating code within main execution context, pretty much similar to that of requirements of multiprocessing in general. This will reduce wasting of time and delving into multiporcessing more.

[till-m]

I'm not sure why this is necessary. Could you explain?

[karunakar2]

I'm not sure why this is necessary. Could you explain?

Hi Till,
Wondering if you could run a simple script on windows, without notebook, plain python with out the 'main' part. doesn't work for me.

import pandas as pd
from pandarallel import pandarallel
pandarallel.initialize()

def testFn(x):
    return (x.sepal_length*x.sepal_width)/(x.petal_length*x.petal_width)

#internet based iris data
myDf = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
print(myDf.head())

res = myDf.parallel_applymap(testFn,axis=1)
print(res)

[karunakar2]

I'm not sure why this is necessary. Could you explain?

Hi Till, Wondering if you could run a simple script on windows, without notebook, plain python with out the 'main' part. doesn't work for me. import pandas as pd from pandarallel import pandarallel pandarallel.initialize()

def testFn(x): return (x.sepal_length_x.sepal_width)/(x.petal_length_x.petal_width)

#internet based iris data myDf = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv') print(myDf.head())

res = myDf.parallel_applymap(testFn,axis=1) print(res)

Please ignore if the intentions are only to use on jupyter notebooks. Thanks

[till-m]

Apologies for getting back to you so late.
I think the problem here is using applymap instead of apply. Try this code:

import pandas as pd
from pandarallel import pandarallel
pandarallel.initialize()

def testFn(x):
    return (x.sepal_length*x.sepal_width)/(x.petal_length*x.petal_width)

#internet based iris data
myDf = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
print(myDf.head())

res = myDf.parallel_apply(testFn,axis=1)
print(res)