Python data analysis toolkit, Release 0.19.0 • Bug in .describe() resets categorical columns information (GH11558) • Bug where loffset argument was not applied when calling resample().count() on a timeseries However, when wrapped in a Series whose dtype is datetime64[ns] or timedelta64[ns], the dtype information is respected. In [1]: pd.Series([pd.NaT], dtype='information, by specifying which columns/rows make up the MultiIndex in the header and index_col parameters

name attribute can be a hashable type (GH12610) • Bug in .describe() resets categorical columns information (GH11558) • Bug where loffset argument was not applied when calling resample().count() on a timeseries However, when wrapped in a Series whose dtype is datetime64[ns] or timedelta64[ns], the dtype information is respected. In [1]: pd.Series([pd.NaT], dtype='information, by specifying which columns/rows make up the MultiIndex in the header and index_col parameters

keyword argument for specifying the types of specific columns (GH14295). See the io docs for more information. 1.3. v0.20.1 (May 5, 2017) 11 pandas: powerful Python data analysis toolkit, Release 0.20.3 000Z"},{"idx":2, ˓→"A":3,"B":"c","C":"2016-01-03T00:00:00.000Z"}]}' See IO: Table Schema for more information. Additionally, the repr for DataFrame and Series can now publish this JSON Table schema representation notebook and nteract more flexiblity in how they display pandas objects, since they have more information about the data. You must enable this by setting the display.html.table_schema option to True.

keyword argument for specifying the types of specific columns (GH14295). See the io docs for more information. In [11]: data = "a b\n1 2\n3 4" In [12]: pd.read_fwf(StringIO(data)).dtypes Out[12]: a int64 000Z"},{"idx":2, ˓→"A":3,"B":"c","C":"2016-01-03T00:00:00.000Z"}]}' See IO: Table Schema for more information. Additionally, the repr for DataFrame and Series can now publish this JSON Table schema representation notebook and nteract more flexiblity in how they display pandas objects, since they have more information about the data. You must enable this by setting the display.html.table_schema option to True.

keyword argument for specifying the types of specific columns (GH14295). See the io docs for more information. In [11]: data = "a b\n1 2\n3 4" In [12]: pd.read_fwf(StringIO(data)).dtypes Out[12]: a int64 000Z"},{"idx":2, ˓→"A":3,"B":"c","C":"2016-01-03T00:00:00.000Z"}]}' See IO: Table Schema for more information. Additionally, the repr for DataFrame and Series can now publish this JSON Table schema representation notebook and nteract more flexiblity in how they display pandas objects, since they have more information about the data. You must enable this by setting the display.html.table_schema option to True.

transform list-likes to individual rows. See section on Exploding list-like column in docs for more information (GH16538, GH10511) Here is a typical usecase. You have comma separated string in a column. In strings with mixed UTC offsets (GH25978) • Bug in to_datetime() with unit='ns' would drop timezone information from the parsed argument (GH26168) 26 Chapter 1. What’s new in 0.25.0 (July 18, 2019) pandas: in pandas.core.groupby.GroupBy.first() and pandas.core.groupby.GroupBy. last() where timezone information would be dropped (GH21603) • Bug in pandas.core.groupby.GroupBy.size() when grouping only NA values

transform list-likes to individual rows. See section on Exploding list-like column in docs for more information (GH16538, GH10511) Here is a typical usecase. You have comma separated string in a column. In strings with mixed UTC offsets (GH25978) • Bug in to_datetime() with unit='ns' would drop timezone information from the parsed argument (GH26168) 26 Chapter 1. What’s new in 0.25.0 (July 18, 2019) pandas: in pandas.core.groupby.GroupBy.first() and pandas.core.groupby.GroupBy. last() where timezone information would be dropped (GH21603) • Bug in pandas.core.groupby.GroupBy.size() when grouping only NA values

that rely on explicitly excluding certain columns. See Splitting an object into groups for more information (GH15475, GH15506). • DataFrame.to_parquet() now accepts index as an argument, allowing the user DatetimeIndex has gained the DatetimeIndex.timetz attribute. This returns the local time with timezone information. (GH21358) 12 Chapter 1. What’s New in 0.24.0 (January 25, 2019) pandas: powerful Python data (GH19891) • DatetimeIndex.to_period() and Timestamp.to_period() will issue a warning when timezone information will be lost (GH21333) • PeriodIndex.tz_convert() and PeriodIndex.tz_localize() have been removed

All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection minor_axis=None, **kwargs) to conform with other NDFrame objects. See Internal Refactoring for more information. • Series.argmin and Series.argmax are now aliased to Series.idxmin and Series.idxmax. These return The new methods re- quire scipy. Consult the Scipy reference guide and documentation for more information about when the various methods are appropriate. See the docs. Interpolate now also accepts a limit

getting started guides User guide The user guide provides in-depth information on the key concepts of pandas with useful background information and explanation. To the user guide API reference The reference numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it. To introduction tutorial To user guide 1.3 Coming from... Are you familiar with other xpassed, 197 warnings, 10␣ ˓→errors in 1090.16s (0:18:10) = This is just an example of what information is shown. You might see a slightly different result as what is shown above. Dependencies Package