Created
June 12, 2017 16:50
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import pandas as pd | |
import numpy as np | |
from pyarrow.parquet import read_pandas, write_table | |
from pyarrow import Table | |
import timeit | |
N = 25000 | |
s = np.array(['AAAAAAAA', 'BBBBBBB', 'CCCCCCCC'], dtype='O') | |
np.random.seed(42) | |
df = pd.DataFrame({'f1': np.linspace(0, 100, num=N), | |
'f2': np.linspace(0, 100, num=N), | |
'f3': np.linspace(0, 100, num=N), | |
'f4': np.linspace(0, 100, num=N), | |
'd1': pd.date_range('1900-01-01', periods=N), | |
's1': np.random.choice(s, N), | |
's2': np.random.choice(s, N)}) | |
write_cases = [ | |
('Excel - XlsxWriter', 'df.to_excel("tmp.xlsx", index=False, engine="xlsxwriter")'), | |
('Excel - Openpyxl', 'df.to_excel("tmp.xlsx", index=False, engine="openpyxl")'), | |
('CSV', 'df.to_csv("tmp.csv", index=False)'), | |
('Parquet', 'write_table(Table.from_pandas(df, timestamps_to_ms=True), "tmp.pq")') | |
] | |
n_runs = 3 | |
results = {} | |
for label, stmt in write_cases: | |
t = timeit.timeit(stmt, globals=globals(), number=n_runs) / n_runs * 1000 | |
results[label] = t | |
writes = pd.Series(results, name='Write Time(ms)') | |
read_cases = [ | |
('Excel - Xlrd', 'pd.read_excel("tmp.xlsx", engine="xlrd")'), | |
('CSV', 'pd.read_csv("tmp.csv")'), | |
('Parquet', 'read_pandas("tmp.pq")') | |
] | |
results = {} | |
for label, stmt in read_cases: | |
t = timeit.timeit(stmt, globals=globals(), number=n_runs) / n_runs * 1000 | |
results[label] = t | |
reads = pd.Series(results, name='Read Time(ms)') | |
df = pd.concat([writes, reads], axis=1) | |
with pd.option_context('precision', 2): | |
print(df) | |
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