st.cache_data是缓存返回数据的计算的推荐方法：从CSV加载DataFrame，转换NumPy数组，查询API或任何其他返回可序列化数据对象（str，int，float，DataFrame，array，list，...）的函数。
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Pandas can read many formats such as CSV, parquet, pickle, JSON, Excel, etc. We recommended using the parquet format, a compressed, efficient columnar data representation. We'll also explain what can slow your pandas down and share a few bonus tips surrounding caching and parallelization. Keep...
改为to_csv
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spss = ['pyreadstat>=1.2.0'] postgresql = ['SQLAlchemy>=2.0.0', 'psycopg2>=2.9.6', 'adbc-driver-postgresql>=0.10.0'] mysql = ['SQLAlchemy>=2.0.0', 'pymysql>=1.0.2'] sql-other = ['SQLAlchemy>=2.0.0', 'adbc-driver-postgresql>=0.10.0', 'adbc-driver-sqlite>=0.8.0...
realize it’s pretty large. The data set for this case is a565MBCSVfile with 24 columns and 2.3M rows. This is not big data by any means but it is big enough that it can make Excel crawl. It’s also big enough that some of the pandas approaches will be relatively slow on your ...
When I first started using pandas, I was advised that, while it was a great tool for dissecting data, pandas was too slow to use as a statistical modeling tool. Starting out, this proved true. I spent more than a few minutes twiddling my thumbs, waiting for pandas to churn through data...
importmodin.pandasaspd df = pd.read_csv("my_dataset.csv") Modin can handle the datasets that pandas can't Often data scientists have to switch between different tools for operating on datasets of different sizes. Processing large dataframes with pandas is slow, and pandas does not support wor...
pd.io.sql.read_sql_table("books",cnx,index_col='id') author copiesid1Alice2 It wrote the first row! Not much of a difference with this toy example, but once you were writing a few thousand rows and the error didn't pop up until the 3000th, this would make a pretty noticeable di...