Dataframe with 64000 rows is being processed twice

[reinzler]

Dataframe with 64000 rows is being processed twice

[shermansiu]

The example doesn't work if you don't have access to the Newton Raphson CSVs.

[reinzler]

The example doesn't work if you don't have access to the Newton Raphson CSVs.

data_300.csv

[shermansiu]

I reduced your code sample to the following:

import pandas as pd
from pandarallel import pandarallel
from spatialmath import SE3
# from Master10 import Master10DH


def ik(row):
    # robot = Master10DH()
    TEP = (SE3([row['x'], row['y'], row['z']]) * SE3.RPY([row['r'], row['p'], row['w']], unit="deg"))
    return True, "Reason"
    # sol1 = robot.ikine_NR(Tep)
    # return (sol1.success, sol1.reason)

pandarallel.initialize(progress_bar=True, nb_workers=8)

df = pd.read_csv("data_300.csv")
try:
    df[['solution', 'reason']] = df.parallel_apply(ik, axis=1)
except ValueError:
    print("ValueError")
    df['solution'] = df.parallel_apply(ik, axis=1)

Master10 seems to be a custom package for operating a robot, so I've commented that code out.

Upon inspection, it seems that if we DON'T use pandarallel, the code still runs twice.

It seems like you can fix your code by replacing df[['solution', 'reason']] = df.parallel_apply(ik, axis=1) with df[['solution', 'reason']] = df.parallel_apply(ik, axis=1).tolist().

This is not a problem with pandarallel. Unless there is other information that suggests otherwise, this issue can be closed.