 pandas: powerful Python data analysis toolkit - 0.12. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 18 IO Tools (Text, CSV, HDF5, ...) 357 18.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • Highlites include a consistent I/O API naming scheme, routines to read html, write multi-indexes to csv files, read & write STATA data files, read & write JSON format files, Python 3 support for HDFStore0 码力 | 657 页 | 3.58 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.12. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 18 IO Tools (Text, CSV, HDF5, ...) 357 18.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • Highlites include a consistent I/O API naming scheme, routines to read html, write multi-indexes to csv files, read & write STATA data files, read & write JSON format files, Python 3 support for HDFStore0 码力 | 657 页 | 3.58 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.25values (GH28204). • Regression in to_csv() where writing a Series or DataFrame indexed by an IntervalIndex would incorrectly raise a TypeError (GH28210) • Fix to_csv() with ExtensionArray with list-like axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • Release 0.25.3 3.2.12 Getting data in/out CSV Writing to a csv file. In [143]: df.to_csv('foo.csv') Reading from a csv file. In [144]: pd.read_csv('foo.csv') Out[144]: Unnamed: 0 A B C D 0 2000-01-010 码力 | 698 页 | 4.91 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.25values (GH28204). • Regression in to_csv() where writing a Series or DataFrame indexed by an IntervalIndex would incorrectly raise a TypeError (GH28210) • Fix to_csv() with ExtensionArray with list-like axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • Release 0.25.3 3.2.12 Getting data in/out CSV Writing to a csv file. In [143]: df.to_csv('foo.csv') Reading from a csv file. In [144]: pd.read_csv('foo.csv') Out[144]: Unnamed: 0 A B C D 0 2000-01-010 码力 | 698 页 | 4.91 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.20.3. . . . . . . . . . . . . . . . . . . . . 12 1.3.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 13 1.3.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . . 56 1.6.1.3 read_csv has improved support for duplicate column names . . . . . . . . . . . 58 1.6.1.4 read_csv supports parsing Categorical directly . . . . . . . 2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes 77 1.6.2.12 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . 79 1.6.2.13 Sparse Changes0 码力 | 2045 页 | 9.18 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.20.3. . . . . . . . . . . . . . . . . . . . . 12 1.3.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 13 1.3.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . . 56 1.6.1.3 read_csv has improved support for duplicate column names . . . . . . . . . . . 58 1.6.1.4 read_csv supports parsing Categorical directly . . . . . . . 2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes 77 1.6.2.12 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . 79 1.6.2.13 Sparse Changes0 码力 | 2045 页 | 9.18 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.20.2. . . . . . . . . . . . . . . . . . . . . 10 1.2.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 11 1.2.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . . 54 1.5.1.3 read_csv has improved support for duplicate column names . . . . . . . . . . . 56 1.5.1.4 read_csv supports parsing Categorical directly . . . . . . . 2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes 75 1.5.2.12 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . 77 1.5.2.13 Sparse Changes0 码力 | 1907 页 | 7.83 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.20.2. . . . . . . . . . . . . . . . . . . . . 10 1.2.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 11 1.2.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . . 54 1.5.1.3 read_csv has improved support for duplicate column names . . . . . . . . . . . 56 1.5.1.4 read_csv supports parsing Categorical directly . . . . . . . 2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes 75 1.5.2.12 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . 77 1.5.2.13 Sparse Changes0 码力 | 1907 页 | 7.83 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.21.1. . . . . . . . . . . . . . . . . . . . . 41 1.5.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 42 1.5.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . . 85 1.8.1.3 read_csv has improved support for duplicate column names . . . . . . . . . . . 87 1.8.1.4 read_csv supports parsing Categorical directly . . . . . . . 2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes106 1.8.2.12 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . 108 1.8.2.13 Sparse Changes0 码力 | 2207 页 | 8.59 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.21.1. . . . . . . . . . . . . . . . . . . . . 41 1.5.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 42 1.5.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . . 85 1.8.1.3 read_csv has improved support for duplicate column names . . . . . . . . . . . 87 1.8.1.4 read_csv supports parsing Categorical directly . . . . . . . 2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes106 1.8.2.12 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . 108 1.8.2.13 Sparse Changes0 码力 | 2207 页 | 8.59 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.13.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 19 IO Tools (Text, CSV, HDF5, ...) 455 19.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • users upgrade to this version. Highlights include: • Added infer_datetime_format keyword to read_csv/to_datetime to allow speedups for homo- geneously formatted datetimes. • Will intelligently limit0 码力 | 1219 页 | 4.81 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.13.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 19 IO Tools (Text, CSV, HDF5, ...) 455 19.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • users upgrade to this version. Highlights include: • Added infer_datetime_format keyword to read_csv/to_datetime to allow speedups for homo- geneously formatted datetimes. • Will intelligently limit0 码力 | 1219 页 | 4.81 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.14.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 19 IO Tools (Text, CSV, HDF5, ...) 511 19.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • ftypes now return a series with dtype=object on empty containers (GH5740) • df.to_csv will now return a string of the CSV data if neither a target path nor a buffer is provided (GH6061) • pd.infer_freq()0 码力 | 1349 页 | 7.67 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.14.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 19 IO Tools (Text, CSV, HDF5, ...) 511 19.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • ftypes now return a series with dtype=object on empty containers (GH5740) • df.to_csv will now return a string of the CSV data if neither a target path nor a buffer is provided (GH6061) • pd.infer_freq()0 码力 | 1349 页 | 7.67 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.19.1. . . . . . . . . . . . . . . . . . . . . 8 read_csv has improved support for duplicate column names . . . . . . . . . . . . . . . . . 10 read_csv supports parsing Categorical directly . . . . . . . 29 MultiIndex constructors, groupby and set_index preserve categorical dtypes . . . . 30 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . . . . . . . 31 Sparse Changes on groupby resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Changes in read_csv exceptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 to_datetime error changes0 码力 | 1943 页 | 12.06 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.19.1. . . . . . . . . . . . . . . . . . . . . 8 read_csv has improved support for duplicate column names . . . . . . . . . . . . . . . . . 10 read_csv supports parsing Categorical directly . . . . . . . 29 MultiIndex constructors, groupby and set_index preserve categorical dtypes . . . . 30 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . . . . . . . 31 Sparse Changes on groupby resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Changes in read_csv exceptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 to_datetime error changes0 码力 | 1943 页 | 12.06 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.19.0. . . . . . . . . . . . . . . . . . . . . 7 read_csv has improved support for duplicate column names . . . . . . . . . . . . . . . . . 9 read_csv supports parsing Categorical directly . . . . . . . . 28 MultiIndex constructors, groupby and set_index preserve categorical dtypes . . . . 28 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . . . . . . . 30 Sparse Changes on groupby resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Changes in read_csv exceptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 to_datetime error changes0 码力 | 1937 页 | 12.03 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.19.0. . . . . . . . . . . . . . . . . . . . . 7 read_csv has improved support for duplicate column names . . . . . . . . . . . . . . . . . 9 read_csv supports parsing Categorical directly . . . . . . . . 28 MultiIndex constructors, groupby and set_index preserve categorical dtypes . . . . 28 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . . . . . . . 30 Sparse Changes on groupby resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Changes in read_csv exceptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 to_datetime error changes0 码力 | 1937 页 | 12.03 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.17.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 740 24 IO Tools (Text, CSV, HDF5, ...) 755 24.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • objects (GH9157) • pd.read_csv can now read bz2-compressed files incrementally, and the C parser can read bz2-compressed files from AWS S3 (GH11070, GH11072). • In pd.read_csv, recognize s3n:// and s3a://0 码力 | 1787 页 | 10.76 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.17.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 740 24 IO Tools (Text, CSV, HDF5, ...) 755 24.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • objects (GH9157) • pd.read_csv can now read bz2-compressed files incrementally, and the C parser can read bz2-compressed files from AWS S3 (GH11070, GH11072). • In pd.read_csv, recognize s3n:// and s3a://0 码力 | 1787 页 | 10.76 MB | 1 年前3
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