GroupBy on Categoricals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.3.1.9 Table Schema Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.3.1.10 SciPy sparse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581 11.8 Table Schema Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1033 24.2.5 Table Schema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1034 24.3 HTML

Categoricals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 ii 1.5.1.9 Table Schema Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 1.5.1.10 SciPy sparse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 11.8 Table Schema Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 610 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1069 24.2.5 Table Schema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1069 24.3 HTML

GroupBy on Categoricals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.2.1.9 Table Schema Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.2.1.10 SciPy sparse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 11.8 Table Schema Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1029 xix 24.2.5 Table Schema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1029 24.3 HTML

'{"name":"D","index":["x","y","z"],"data":[15,16,17]}' Table oriented serializes to the JSON Table Schema, allowing for the preservation of metadata including but not limited to dtypes and index names. like {column -> {index -> value}} values just the values array table adhering to the JSON Table Schema • dtype : if True, infer dtypes, if a dict of column to dtype, then use those, if False, then dont print(chunk) .....: Empty DataFrame Columns: [] Index: [] a b 0 1 2 a b 1 3 4 Table schema New in version 0.20.0. Table Schema is a spec for describing tabular datasets as a JSON object. The JSON includes

supported starting with pyarrow >= 0.16 (GH20612). • to_parquet() now appropriately handles the schema argument for user defined schemas in the pyarrow engine. (GH30270) • DataFrame.to_json() now accepts '{"name":"D","index":["x","y","z"],"data":[15,16,17]}' Table oriented serializes to the JSON Table Schema, allowing for the preservation of metadata including but not limited to dtypes and index names. like {column -> {index -> value}} values just the values array table adhering to the JSON Table Schema • dtype : if True, infer dtypes, if a dict of column to dtype, then use those, if False, then don’t

JSON schema (GH21345) • Bug in read_json() for orient='table' and float index, as it infers index dtype by default, which is not applicable because index dtype is already defined in the JSON schema (GH25433) conversion to Timestamp, which is not applicable because column names are already defined in the JSON schema (GH25435) • Bug in json_normalize() for errors='ignore' where missing values in the input data, powerful Python data analysis toolkit, Release 0.25.0 Table oriented serializes to the JSON Table Schema, allowing for the preservation of metadata including but not limited to dtypes and index names.

JSON schema (GH21345) • Bug in read_json() for orient='table' and float index, as it infers index dtype by default, which is not applicable because index dtype is already defined in the JSON schema (GH25433) conversion to Timestamp, which is not applicable because column names are already defined in the JSON schema (GH25435) • Bug in json_normalize() for errors='ignore' where missing values in the input data, powerful Python data analysis toolkit, Release 0.25.1 Table oriented serializes to the JSON Table Schema, allowing for the preservation of metadata including but not limited to dtypes and index names.

˓→'{"name":"D","index":["x","y","z"],"data":[15,16,17]}' Table oriented serializes to the JSON Table Schema, allowing for the preservation of metadata including but not limited to dtypes and index names. like {column -> {index -> value}} values just the values array table adhering to the JSON Table Schema 284 Chapter 4. User Guide pandas: powerful Python data analysis toolkit, Release 0.24.0 • dtype \\\\\\\\\Empty DataFrame Columns: [] Index: [] a b 0 1 2 a b 1 3 4 Table Schema New in version 0.20.0. Table Schema is a spec for describing tabular datasets as a JSON object. The JSON includes

formatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 817 2.17.8 Table schema display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 818 2.18 Enhancing '{"name":"D","index":["x","y","z"],"data":[15,16,17]}' Table oriented serializes to the JSON Table Schema, allowing for the preservation of metadata including but not limited to dtypes and index names. like {column -> {index -> value}} values just the values array table adhering to the JSON Table Schema • dtype : if True, infer dtypes, if a dict of column to dtype, then use those, if False, then don’t

formatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 820 2.17.8 Table schema display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 821 2.18 Enhancing '{"name":"D","index":["x","y","z"],"data":[15,16,17]}' Table oriented serializes to the JSON Table Schema, allowing for the preservation of metadata including but not limited to dtypes and index names. like {column -> {index -> value}} values just the values array table adhering to the JSON Table Schema • dtype : if True, infer dtypes, if a dict of column to dtype, then use those, if False, then don’t