To load the dataframe, you'll need to install fastparquet and python-snappy. In : import datashader as ds , datashader.transfer_functions as tf , numpy as np from datashader import spatial
C:\Python\temp\iris_read.csv <class 'pandas.core.frame.DataFrame'> RangeIndex: 150 entries, 0 to 149 Data columns (total 5 columns): sepal_length 150 non-null float64 sepal_width 150 non-null float64 petal_length 150 non-null float64 petal_width 150 non-null float64 species 150 non-null object dtypes: float64(4), object(1) memory usage: 5.9+ KB
Overall, Parquet_pyarrow is the fastest reading format for the given tables. The Parquet_pyarrow format is about 3 times as fast as the CSV one. Also, regarding the Microsoft SQL storage, it is interesting to see that turbobdc performs slightly better than the two other drivers (pyodbc and pymssql).
fastparquet is a newer Parquet file reader/writer implementation for Python users created for use in the Dask project. It is implemented in Python and uses the Numba Python-to-LLVM compiler to accelerate the Parquet decoding routines. I also installed that to compare with alternative implementations.
Type Size Name Uploaded Uploader Downloads Labels; conda: 5.2 MB | win-64/fastparquet-0.4.1-py36h7725771_0.tar.bz2
The default io.parquet.engine behavior is to try 'pyarrow', falling back to 'fastparquet' if 'pyarrow' is unavailable. columns list, default=None. If not None, only these columns will be read from the file. use_nullable_dtypes bool, default False.
Engine: pyarrow or fastparquet engine. Compression: allowing to choose various compression methods. Index: saves dataframe index. Partition_cols: specify the order of the column partitioning. Excel: Exporting the data in the form of Excel has its own advantages because of the easy manipulation offered in excel. It also allows custom formatting ...
Jul 30, 2019 · Overall, Parquet_pyarrow is the fastest reading format for the given tables. The Parquet_pyarrow format is about 3 times as fast as the CSV one. Also, regarding the Microsoft SQL storage, it is interesting to see that turbobdc performs slightly better than the two other drivers (pyodbc and pymssql).
Oct 22, 2019 · Quick note: I tried building pandas from source (Fedora 30). During 21211596095fe62b9076143_000000 I ran into 21211596095fe62b9076143_000001 A little more context: 21211596095fe62b9076143_000002 Th…