Mixing
Pandas groupby with numpy array_split take to much time
I'm working on to get a lots of rows out around 2.000.000 rows where i'm group it on a single columen.
Its working fine so long, but when i'm in Python3 try to make a Numpy array_split its take for ever to load in, its take so long time i can't keep waiting on it when i testing.
Normal if i'm not using groupby in Pandas its take few sec, but now when i'm using groupby('columen') i can't easy split my dataframe enymore.
def parallelize(data, func):
data_split = np.array_split(data, partitions)
pool = Pool(cores)
data = pd.concat(pool.map(func, data_split))
pool.close()
pool.join()
return data
You can se i'm trying to make multithread to handle my functions even faster.
when i uncomment my groupby function in Pandas its working smooth, so its my groupby and then array_split there make troblese.
python pandas numpy dataframe
asked Nov 21 '18 at 12:08
ParisNakitaKejserParisNakitaKejser
2,50983654
migrated from datascience.stackexchange.com Nov 21 '18 at 19:17
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
add a comment |
I'm working on to get a lots of rows out around 2.000.000 rows where i'm group it on a single columen.
Its working fine so long, but when i'm in Python3 try to make a Numpy array_split its take for ever to load in, its take so long time i can't keep waiting on it when i testing.
Normal if i'm not using groupby in Pandas its take few sec, but now when i'm using groupby('columen') i can't easy split my dataframe enymore.
def parallelize(data, func):
data_split = np.array_split(data, partitions)
pool = Pool(cores)
data = pd.concat(pool.map(func, data_split))
pool.close()
pool.join()
return data
You can se i'm trying to make multithread to handle my functions even faster.
when i uncomment my groupby function in Pandas its working smooth, so its my groupby and then array_split there make troblese.
python pandas numpy dataframe
asked Nov 21 '18 at 12:08
ParisNakitaKejserParisNakitaKejser
2,50983654
migrated from datascience.stackexchange.com Nov 21 '18 at 19:17
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
add a comment |
I'm working on to get a lots of rows out around 2.000.000 rows where i'm group it on a single columen.
Its working fine so long, but when i'm in Python3 try to make a Numpy array_split its take for ever to load in, its take so long time i can't keep waiting on it when i testing.
Normal if i'm not using groupby in Pandas its take few sec, but now when i'm using groupby('columen') i can't easy split my dataframe enymore.
def parallelize(data, func):
data_split = np.array_split(data, partitions)
pool = Pool(cores)
data = pd.concat(pool.map(func, data_split))
pool.close()
pool.join()
return data
You can se i'm trying to make multithread to handle my functions even faster.
when i uncomment my groupby function in Pandas its working smooth, so its my groupby and then array_split there make troblese.
python pandas numpy dataframe
asked Nov 21 '18 at 12:08
ParisNakitaKejserParisNakitaKejser
2,50983654
I'm working on to get a lots of rows out around 2.000.000 rows where i'm group it on a single columen.
Its working fine so long, but when i'm in Python3 try to make a Numpy array_split its take for ever to load in, its take so long time i can't keep waiting on it when i testing.
Normal if i'm not using groupby in Pandas its take few sec, but now when i'm using groupby('columen') i can't easy split my dataframe enymore.
def parallelize(data, func):
data_split = np.array_split(data, partitions)
pool = Pool(cores)
data = pd.concat(pool.map(func, data_split))
pool.close()
pool.join()
return data
You can se i'm trying to make multithread to handle my functions even faster.
when i uncomment my groupby function in Pandas its working smooth, so its my groupby and then array_split there make troblese.
python pandas numpy dataframe
python pandas numpy dataframe
asked Nov 21 '18 at 12:08
ParisNakitaKejserParisNakitaKejser
2,50983654
asked Nov 21 '18 at 12:08
ParisNakitaKejserParisNakitaKejser
2,50983654
asked Nov 21 '18 at 12:08
ParisNakitaKejserParisNakitaKejser
2,50983654
asked Nov 21 '18 at 12:08
ParisNakitaKejserParisNakitaKejser
2,50983654
asked Nov 21 '18 at 12:08
ParisNakitaKejserParisNakitaKejser
2,50983654
2,50983654
migrated from datascience.stackexchange.com Nov 21 '18 at 19:17
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
migrated from datascience.stackexchange.com Nov 21 '18 at 19:17
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
add a comment |
add a comment |
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