From 0fc655f84c6b51552a7c70f2b92c846b4d24e381 Mon Sep 17 00:00:00 2001 From: MeeseeksMachine <39504233+meeseeksmachine@users.noreply.github.com> Date: Fri, 11 Feb 2022 01:36:10 -0800 Subject: [PATCH] Backport PR #45920: DOC: fix URLs, formatting and typos (#45930) Co-authored-by: partev --- pandas/core/generic.py | 2 +- pandas/plotting/_core.py | 2 +- web/pandas/about/index.md | 2 +- web/pandas/community/ecosystem.md | 29 +++++++++++++++-------------- 4 files changed, 18 insertions(+), 17 deletions(-) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index cca8ed9789518..fee048a7d98cc 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -11440,7 +11440,7 @@ def _find_valid_index(self, *, how: str) -> Hashable | None: @doc(position="first", klass=_shared_doc_kwargs["klass"]) def first_valid_index(self) -> Hashable | None: """ - Return index for {position} non-NA value or None, if no NA value is found. + Return index for {position} non-NA value or None, if no non-NA value is found. Returns ------- diff --git a/pandas/plotting/_core.py b/pandas/plotting/_core.py index 5ad3e404b94a9..216ccddcf05cb 100644 --- a/pandas/plotting/_core.py +++ b/pandas/plotting/_core.py @@ -1371,7 +1371,7 @@ def kde(self, bw_method=None, ind=None, **kwargs): `ind` number of equally spaced points are used. **kwargs Additional keyword arguments are documented in - :meth:`pandas.%(this-datatype)s.plot`. + :meth:`DataFrame.plot`. Returns ------- diff --git a/web/pandas/about/index.md b/web/pandas/about/index.md index 02caaa3b8c53c..4c809a148b328 100644 --- a/web/pandas/about/index.md +++ b/web/pandas/about/index.md @@ -54,7 +54,7 @@ This will help ensure the success of development of _pandas_ as a world-class op series without losing data; - Highly **optimized for performance**, with critical code paths written in - [Cython](http://www.cython.org/) or C. + [Cython](https://cython.org) or C. - Python with *pandas* is in use in a wide variety of **academic and commercial** domains, including Finance, Neuroscience, Economics, diff --git a/web/pandas/community/ecosystem.md b/web/pandas/community/ecosystem.md index e744889070d8e..3e35b3ac4ea30 100644 --- a/web/pandas/community/ecosystem.md +++ b/web/pandas/community/ecosystem.md @@ -29,7 +29,7 @@ modeling functionality that is out of pandas' scope. Statsmodels leverages pandas objects as the underlying data container for computation. -### [sklearn-pandas](https://github.com/paulgb/sklearn-pandas) +### [sklearn-pandas](https://github.com/scikit-learn-contrib/sklearn-pandas) Use pandas DataFrames in your [scikit-learn](https://scikit-learn.org/) ML pipeline. @@ -60,7 +60,7 @@ top of the powerful Vega-Lite JSON specification. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. Altair works with Pandas DataFrames. -### [Bokeh](https://bokeh.pydata.org) +### [Bokeh](https://docs.bokeh.org) Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide @@ -172,7 +172,7 @@ inspection and rich visualization capabilities of a scientific environment like MATLAB or Rstudio. Its [Variable -Explorer](https://docs.spyder-ide.org/variableexplorer.html) allows +Explorer](https://docs.spyder-ide.org/current/panes/variableexplorer.html) allows users to view, manipulate and edit pandas `Index`, `Series`, and `DataFrame` objects like a "spreadsheet", including copying and modifying values, sorting, displaying a "heatmap", converting data @@ -183,9 +183,9 @@ of plain text and binary files or the clipboard into a new pandas DataFrame via a sophisticated import wizard. Most pandas classes, methods and data attributes can be autocompleted in -Spyder's [Editor](https://docs.spyder-ide.org/editor.html) and [IPython -Console](https://docs.spyder-ide.org/ipythonconsole.html), and Spyder's -[Help pane](https://docs.spyder-ide.org/help.html) can retrieve and +Spyder's [Editor](https://docs.spyder-ide.org/current/panes/editor.html) and [IPython +Console](https://docs.spyder-ide.org/current/panes/ipythonconsole.html), and Spyder's +[Help pane](https://docs.spyder-ide.org/current/panes/help.html) can retrieve and render Numpydoc documentation on pandas objects in rich text with Sphinx both automatically and on-demand. @@ -233,7 +233,7 @@ package requires valid credentials for this API (non free). ### [pandaSDMX](https://pandasdmx.readthedocs.io) pandaSDMX is a library to retrieve and acquire statistical data and -metadata disseminated in [SDMX](https://www.sdmx.org) 2.1, an +metadata disseminated in [SDMX](https://sdmx.org) 2.1, an ISO-standard widely used by institutions such as statistics offices, central banks, and international organisations. pandaSDMX can expose datasets and related structural metadata including data flows, @@ -254,7 +254,7 @@ you can obtain for free on the FRED website. ## Domain specific -### [Geopandas](https://github.com/kjordahl/geopandas) +### [Geopandas](https://github.com/geopandas/geopandas) Geopandas extends pandas data objects to include geographic information which support geometric operations. If your work entails maps and @@ -277,13 +277,13 @@ Blaze provides a standard API for doing computations with various in-memory and on-disk backends: NumPy, Pandas, SQLAlchemy, MongoDB, PyTables, PySpark. -### [Dask](https://dask.readthedocs.io/en/latest/) +### [Dask](https://docs.dask.org) Dask is a flexible parallel computing library for analytics. Dask provides a familiar `DataFrame` interface for out-of-core, parallel and distributed computing. -### [Dask-ML](https://dask-ml.readthedocs.io/en/latest/) +### [Dask-ML](https://ml.dask.org) Dask-ML enables parallel and distributed machine learning using Dask alongside existing machine learning libraries like Scikit-Learn, @@ -303,7 +303,7 @@ packages such as PyTables, h5py, and pymongo to move data between non pandas formats. Its graph based approach is also extensible by end users for custom formats that may be too specific for the core of odo. -### [Ray](https://ray.readthedocs.io/en/latest/pandas_on_ray.html) +### [Ray](https://docs.ray.io/en/latest/data/modin/index.html) Pandas on Ray is an early stage DataFrame library that wraps Pandas and transparently distributes the data and computation. The user does not @@ -320,14 +320,14 @@ Ray just like you would Pandas. import ray.dataframe as pd ``` -### [Vaex](https://docs.vaex.io/) +### [Vaex](https://vaex.io/docs/) Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. Vaex is a python library for Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional -grid up to a billion (10^9^) objects/rows per second. Visualization is +grid up to a billion (10^9) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory @@ -338,7 +338,7 @@ wasted). ## Data cleaning and validation -### [pyjanitor](https://github.com/ericmjl/pyjanitor/) +### [pyjanitor](https://github.com/pyjanitor-devs/pyjanitor) Pyjanitor provides a clean API for cleaning data, using method chaining. @@ -388,6 +388,7 @@ authors to coordinate on the namespace. | [pint-pandas](https://github.com/hgrecco/pint-pandas) | `pint` | `Series`, `DataFrame` | | [composeml](https://github.com/alteryx/compose) | `slice` | `DataFrame` | | [woodwork](https://github.com/alteryx/woodwork) | `slice` | `Series`, `DataFrame` | + ## Development tools ### [pandas-stubs](https://github.com/VirtusLab/pandas-stubs)