pandas: powerful Python data analysis toolkit - 1.3.3Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2757 4.13.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2758 4 Failed Failed Access data in the cloud Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.12.0 Google “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of0 码力 | 3603 页 | 14.65 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.3.4Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2757 4.13.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2758 4 Failed Failed Access data in the cloud Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.12.0 Google “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of0 码力 | 3605 页 | 14.68 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.3.2Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2678 4.13.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2678 4 Release 1.3.2 Access data in the cloud Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.12.0 Google “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of0 码力 | 3509 页 | 14.01 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.4.2Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2850 4.13.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2851 4 Failed Failed Access data in the cloud Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.14.0 Google “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of0 码力 | 3739 页 | 15.24 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.4.4Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2852 4.13.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2853 4 Failed Failed Access data in the cloud Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.14.0 Google “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of0 码力 | 3743 页 | 15.26 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.5.0rc0Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3020 4.11.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3021 4 Failed Failed Access data in the cloud Dependency Minimum Version Notes fsspec 2021.5.0 Handling files aside from simple local and HTTP gcsfs 2021.5.0 Google Cloud Storage access pandas-gbq 0.15.0 Google “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of0 码力 | 3943 页 | 15.73 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.2.3Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2536 4.10.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2536 4 files aside from local and HTTP fastparquet 0.4.0 Parquet reading / writing gcsfs 0.6.0 Google Cloud Storage access html5lib 1.0.1 HTML parser for read_html (see note) lxml 4.3.0 HTML parser for read_html “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of0 码力 | 3323 页 | 12.74 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.2.0Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2530 4.10.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2530 4 files aside from local and HTTP fastparquet 0.3.2 Parquet reading / writing gcsfs 0.6.0 Google Cloud Storage access html5lib 1.0.1 HTML parser for read_html (see note) lxml 4.3.0 HTML parser for read_html “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of0 码力 | 3313 页 | 10.91 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.25.1frames. This feature requires version 0.10.0 of the pandas-gbq library as well as the google-cloud-bigquery-storage and fastavro libraries. (GH26104) • Fixed memory leak in DataFrame.to_json() when dealing blosc Compression for msgpack fastparquet 0.2.1 Parquet reading / writing gcsfs 0.2.2 Google Cloud Storage access html5lib HTML parser for read_html (see note) lxml 3.8.0 HTML parser for read_html (see “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of0 码力 | 2833 页 | 9.65 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.25.0frames. This feature requires version 0.10.0 of the pandas-gbq library as well as the google-cloud-bigquery-storage and fastavro libraries. (GH26104) • Fixed memory leak in DataFrame.to_json() when dealing blosc Compression for msgpack fastparquet 0.2.1 Parquet reading / writing gcsfs 0.2.2 Google Cloud Storage access html5lib HTML parser for read_html (see note) lxml 3.8.0 HTML parser for read_html (see “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of0 码力 | 2827 页 | 9.62 MB | 1 年前3
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