see the docs • json_normalize() is a new method to allow you to create a flat table from semi-structured JSON data. See the docs (GH1067) • Added PySide support for the qtpandas DataFrameModel and DataFrameWidget different kinds of input: • Dict of 1D ndarrays, lists, dicts, or Series • 2-D numpy.ndarray • Structured or record ndarray • A Series • Another DataFrame Along with the data, you can optionally pass ’b’, ’c’, ’d’]) Out[40]: one two a 1 4 b 2 3 c 3 2 d 4 1 [4 rows x 2 columns] 8.2.3 From structured or record array This case is handled identically to a dict of arrays. In [41]: data = np.zeros((2

different kinds of input: • Dict of 1D ndarrays, lists, dicts, or Series • 2-D numpy.ndarray • Structured or record ndarray • A Series • Another DataFrame Along with the data, you can optionally pass DataFrame(d, index=['a', 'b', 'c', 'd']) Out[46]: one two a 1.0 4.0 b 2.0 3.0 c 3.0 2.0 d 4.0 1.0 From structured or record array This case is handled identically to a dict of arrays. In [47]: data = np.zeros((2 tuples or an ndarray with structured dtype. It works analogously to the normal DataFrame constructor, except that the resulting DataFrame index may be a specific field of the structured dtype. For example:

see the docs • json_normalize() is a new method to allow you to create a flat table from semi-structured JSON data. See the docs (GH1067) • Added PySide support for the qtpandas DataFrameModel and DataFrameWidget different kinds of input: • Dict of 1D ndarrays, lists, dicts, or Series • 2-D numpy.ndarray • Structured or record ndarray • A Series • Another DataFrame Along with the data, you can optionally pass DataFrame(d, index=[’a’, ’b’, ’c’, ’d’]) Out[40]: one two a 1 4 b 2 3 c 3 2 d 4 1 8.2.3 From structured or record array This case is handled identically to a dict of arrays. In [41]: data = np.zeros((2

see the docs • json_normalize() is a new method to allow you to create a flat table from semi-structured JSON data. See the docs (GH1067) • Added PySide support for the qtpandas DataFrameModel and DataFrameWidget different kinds of input: • Dict of 1D ndarrays, lists, dicts, or Series • 2-D numpy.ndarray • Structured or record ndarray • A Series • Another DataFrame Along with the data, you can optionally pass DataFrame(d, index=[’a’, ’b’, ’c’, ’d’]) Out[40]: one two a 1 4 b 2 3 c 3 2 d 4 1 8.2.3 From structured or record array This case is handled identically to a dict of arrays. In [41]: data = np.zeros((2

see the docs • json_normalize() is a new method to allow you to create a flat table from semi-structured JSON data. See the docs (GH1067) • Added PySide support for the qtpandas DataFrameModel and DataFrameWidget different kinds of input: • Dict of 1D ndarrays, lists, dicts, or Series • 2-D numpy.ndarray • Structured or record ndarray • A Series • Another DataFrame Along with the data, you can optionally pass DataFrame(d, index=[’a’, ’b’, ’c’, ’d’]) Out[40]: one two a 1 4 b 2 3 c 3 2 d 4 1 8.2.3 From structured or record array This case is handled identically to a dict of arrays. In [41]: data = np.zeros((2

see the docs • json_normalize() is a new method to allow you to create a flat table from semi-structured JSON data. See the docs (GH1067) • Added PySide support for the qtpandas DataFrameModel and DataFrameWidget Performance improvement • CLN: Code cleanup The following defines how a commit message should be structured. Please reference the relevant GitHub issues in your commit message using GH1234 or #1234. Either different kinds of input: • Dict of 1D ndarrays, lists, dicts, or Series • 2-D numpy.ndarray • Structured or record ndarray • A Series 9.2. DataFrame 311 pandas: powerful Python data analysis toolkit

ndarrays / lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 9.2.3 From structured or record array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 9.2.4 From see the docs • json_normalize() is a new method to allow you to create a flat table from semi-structured JSON data. See the docs (GH1067) • Added PySide support for the qtpandas DataFrameModel and DataFrameWidget Performance improvement • CLN: Code cleanup The following defines how a commit message should be structured. Please reference the relevant GitHub issues in your commit message using GH1234 or #1234. Either

ndarrays / lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 9.2.3 From structured or record array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 9.2.4 From see the docs • json_normalize() is a new method to allow you to create a flat table from semi-structured JSON data. See the docs (GH1067) • Added PySide support for the qtpandas DataFrameModel and DataFrameWidget Performance improvement • CLN: Code cleanup The following defines how a commit message should be structured. Please reference the relevant GitHub issues in your commit message using GH1234 or #1234. Either

ndarrays / lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 8.2.3 From structured or record array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 8.2.4 From see the docs • json_normalize() is a new method to allow you to create a flat table from semi-structured JSON data. See the docs (GH1067) • Added PySide support for the qtpandas DataFrameModel and DataFrameWidget Performance improvement • CLN: Code cleanup The following defines how a commit message should be structured. Please reference the relevant GitHub issues in your commit message using GH1234 or #1234. Either

ndarrays / lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 8.2.3 From structured or record array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 8.2.4 From see the docs • json_normalize() is a new method to allow you to create a flat table from semi-structured JSON data. See the docs (GH1067) • Added PySide support for the qtpandas DataFrameModel and DataFrameWidget Performance improvement • CLN: Code cleanup The following defines how a commit message should be structured. Please reference the relevant GitHub issues in your commit message using GH1234 or #1234. Either