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June 24, 2018 18:51
Data aggregation elapsed time in Python and Q. See article https://www.linkedin.com/pulse/python-data-analysis-really-simple-ferenc-bodon-ph-d-/
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rowCount: 100*1000 | |
t: ([] bucket: string rowCount?`2; qty: rowCount?100; risk: rowCount?10; weight: rowCount?2.) | |
\t do[100; select NR: count i, TOTAL_QTY: sum qty, AVG_QTY: avg qty, TOTAL_RISK: sum risk, AVG_QTY: avg risk, W_AVG_QTY: weight wavg qty, W_AVG_RISK: weight wavg risk by bucket from t] |
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rowCount: 100*1000 | |
t: ([] bucket: rowCount?`2; qty: rowCount?100; risk: rowCount?10; weight: rowCount?2.) | |
\t do[100; select NR: count i, TOTAL_QTY: sum qty, AVG_QTY: avg qty, TOTAL_RISK: sum risk, AVG_QTY: avg risk, W_AVG_QTY: weight wavg qty, W_AVG_RISK: weight wavg risk by bucket from t] |
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import timeit | |
createTable = ''' | |
import pandas as pd | |
import numpy as np | |
import random | |
import string | |
rowCount = 100*1000 | |
t = pd.DataFrame({'bucket': [''.join(random.choices(string.ascii_lowercase, k=2)) for _ in range(rowCount)], | |
'weight': [random.uniform(0, 2) for _ in range(rowCount)], | |
'qty': [random.randint(0, 100) for _ in range(rowCount)], | |
'risk': [random.randint(0, 10) for _ in range(rowCount)]}) | |
''' | |
aggrSingleExpression = ''' | |
def my_agg(x): | |
data = {'NR': x.bucket.count(), | |
'TOTAL_QTY': x.qty.sum(), | |
'AVG_QTY': x.qty.mean(), | |
'TOTAL_RISK': x.risk.sum(), | |
'AVG_RISK': x.risk.mean(), | |
'W_AVG_QTY': np.average(x.qty, weights=x.weight), | |
'W_AVG_RISK': np.average(x.risk, weights=x.weight) | |
} | |
return pd.Series(data, index=['NR', 'TOTAL_QTY', 'AVG_QTY', 'TOTAL_RISK', | |
'AVG_RISK', 'W_AVG_QTY', 'W_AVG_RISK']) | |
t.groupby('bucket').apply(my_agg) | |
''' | |
print("Python single expression:", timeit.timeit(setup=createTable, stmt=aggrSingleExpression, number = 100)) |
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import timeit | |
createTable = ''' | |
import pandas as pd | |
import numpy as np | |
import random | |
import string | |
rowCount = 100*1000 | |
t = pd.DataFrame({'bucket': [''.join(random.choices(string.ascii_lowercase, k=2)) for _ in range(rowCount)], | |
'weight': [random.uniform(0, 2) for _ in range(rowCount)], | |
'qty': [random.randint(0, 100) for _ in range(rowCount)], | |
'risk': [random.randint(0, 10) for _ in range(rowCount)]}) | |
''' | |
aggrWithJoin = ''' | |
res = t.groupby('bucket').agg({'bucket': len, 'qty': [sum, np.mean], 'risk': [sum, np.mean]}) | |
res.columns = res.columns.map('_'.join) | |
res.rename(columns={'bucket_len': 'NR', 'qty_sum': 'TOTAL_QTY', 'qty_mean': 'AVG_QTY', | |
'risk_sum': 'TOTAL_RISK', 'risk_mean': 'AVG_RISK'}).join( | |
t.groupby('bucket').apply(lambda g: np.average(g.qty, weights=g.weight)).to_frame('W_AVG_QTY')).join( | |
t.groupby('bucket').apply(lambda g: np.average(g.risk, weights=g.weight)).to_frame('W_AVG_RISK') | |
) | |
''' | |
print("Python with Join 1:", timeit.timeit(setup=createTable, stmt=aggrWithJoin, number = 100)) | |
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