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November 10, 2022 12:52
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| import pandas as pd | |
| import time | |
| from contextlib import contextmanager | |
| import dask.dataframe | |
| import redis | |
| import struct | |
| import numpy as np | |
| import pickle | |
| @contextmanager | |
| def timer(message: str) -> None: | |
| try: | |
| start = time.perf_counter() | |
| yield {} | |
| finally: | |
| time_taken = round(time.perf_counter() - start, 3) | |
| print(f"'{message}' --> {time_taken} sec.") | |
| """ | |
| Loading 2Go file CSV with 100 000 000 rows and 3 columns | |
| """ | |
| """ | |
| Redis | |
| Output: Error 104 while writing to socket. Connection reset by peer | |
| """ | |
| r = redis.StrictRedis(host='panda_redis', port=6379, db=0) | |
| df = pd.read_csv("test.csv") | |
| data = df.to_json() | |
| r.set("redis-key", data) # Fail, too large... | |
| results = r.get('redis-key') | |
| print(pd.read_json(results)) | |
| """ | |
| Loading CSV: | |
| Output: 'Panda loading CSV' --> 5.022 sec. | |
| """ | |
| with timer("Panda loading CSV"): | |
| df = pd.read_csv("test.csv") | |
| """ | |
| Loading CSV with dtypes: | |
| Output: 'Panda loading CSV with dtypes' --> 4.603 sec. | |
| """ | |
| with timer("Panda loading CSV with dtypes"): | |
| df = pd.read_csv("test.csv", dtype={"Titre": "object", "Taille": "float64", "Age": "int64"}) | |
| """ | |
| Loading CSV with Pyarrow: | |
| Output: Crashes Python...... | |
| """ | |
| with timer("Panda loading CSV with Pyarrow"): | |
| df = pd.read_csv("test.csv", engine="pyarrow") | |
| """ | |
| Loading CSV with dtypes and Parquet: | |
| Output: Erreur de segmentation (core dumped) | |
| Note: file seems to big to be saved as parquet... | |
| """ | |
| with timer("Panda loading CSV with parquet"): | |
| df = pd.read_csv("test.csv", dtype={"Titre": "object", "Taille": "float64", "Age": "int64"}) | |
| df.to_parquet("test.parquet", engine="fastparquet") | |
| """ | |
| Loading CSV with Dask: | |
| Output: Crashes | |
| """ | |
| with timer("Panda loading CSV with Dask"): | |
| data = dask.dataframe.read_csv("test.csv", dtype={"Titre": "object", "Taille": "float64", "Age": "int64"}) |
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