From c1bed8279578b2ef2312190426072589424641bb Mon Sep 17 00:00:00 2001 From: Bhard27 Date: Sun, 30 Aug 2020 03:43:22 +0530 Subject: [PATCH 1/5] improved the documentation --- doc/source/getting_started/overview.rst | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/doc/source/getting_started/overview.rst b/doc/source/getting_started/overview.rst index d8a40c5406dee..3554e5babfe1b 100644 --- a/doc/source/getting_started/overview.rst +++ b/doc/source/getting_started/overview.rst @@ -9,9 +9,9 @@ Package overview **pandas** is a `Python `__ package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the -fundamental high-level building block for doing practical, **real world** data +fundamental high-level building block for doing practical, **real-world** data analysis in Python. Additionally, it has the broader goal of becoming **the -most powerful and flexible open source data analysis / manipulation tool +most powerful and flexible open source data analysis/manipulation tool available in any language**. It is already well on its way toward this goal. pandas is well suited for many different kinds of data: @@ -21,11 +21,11 @@ pandas is well suited for many different kinds of data: - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - - Any other form of observational / statistical data sets. The data actually + - Any other form of observational / statistical data sets. The data need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, :class:`Series` (1-dimensional) -and :class:`DataFrame` (2-dimensional), handle the vast majority of typical use +and :class:`DataFrame` (2-dimensional) handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, :class:`DataFrame` provides everything that R's ``data.frame`` provides and much more. pandas is built on top of `NumPy @@ -57,7 +57,7 @@ Here are just a few of the things that pandas does well: Excel files, databases, and saving / loading data from the ultrafast **HDF5 format** - **Time series**-specific functionality: date range generation and frequency - conversion, moving window statistics, date shifting and lagging. + conversion, moving window statistics, date shifting, and lagging. Many of these principles are here to address the shortcomings frequently experienced using other languages / scientific research environments. For data @@ -72,7 +72,7 @@ Some other notes extensively tweaked in `Cython `__ code. However, as with anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster - specialized tool. + -specialized tool. - pandas is a dependency of `statsmodels `__, making it an important part of the @@ -101,12 +101,12 @@ fashion. Also, we would like sensible default behaviors for the common API functions which take into account the typical orientation of time series and -cross-sectional data sets. When using ndarrays to store 2- and 3-dimensional +cross-sectional data sets. When using the N-dimensional array(ndarrays) to store 2- and 3-dimensional data, a burden is placed on the user to consider the orientation of the data set when writing functions; axes are considered more or less equivalent (except when C- or Fortran-contiguousness matters for performance). In pandas, the axes are intended to lend more semantic meaning to the data; i.e., for a particular -data set there is likely to be a "right" way to orient the data. The goal, +data set, there is likely to be a "right" way to orient the data. The goal, then, is to reduce the amount of mental effort required to code up data transformations in downstream functions. @@ -148,8 +148,8 @@ pandas possible. Thanks to `all of our contributors `. pandas is a `NumFOCUS `__ sponsored project. -This will help ensure the success of development of pandas as a world-class open-source -project, and makes it possible to `donate `__ to the project. +This will help ensure the success of the development of pandas as a world-class open-source +project and makes it possible to `donate `__ to the project. Project governance ------------------ @@ -174,4 +174,3 @@ License ------- .. literalinclude:: ../../../LICENSE - From 8414086e4ee934b64b9bc8d9ac5aa903173057f7 Mon Sep 17 00:00:00 2001 From: Pranjal Bhardwaj <50989807+Bhard27@users.noreply.github.com> Date: Sun, 30 Aug 2020 14:37:44 +0530 Subject: [PATCH 2/5] Update doc/source/getting_started/overview.rst Co-authored-by: Marco Gorelli --- doc/source/getting_started/overview.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/getting_started/overview.rst b/doc/source/getting_started/overview.rst index 3554e5babfe1b..a5acefb36b97b 100644 --- a/doc/source/getting_started/overview.rst +++ b/doc/source/getting_started/overview.rst @@ -101,7 +101,7 @@ fashion. Also, we would like sensible default behaviors for the common API functions which take into account the typical orientation of time series and -cross-sectional data sets. When using the N-dimensional array(ndarrays) to store 2- and 3-dimensional +cross-sectional data sets. When using the N-dimensional array (ndarrays) to store 2- and 3-dimensional data, a burden is placed on the user to consider the orientation of the data set when writing functions; axes are considered more or less equivalent (except when C- or Fortran-contiguousness matters for performance). In pandas, the axes From b2c1f4cff1d37fc407bb5a9e20d029b92b36790d Mon Sep 17 00:00:00 2001 From: Bhard27 Date: Sun, 30 Aug 2020 14:40:23 +0530 Subject: [PATCH 3/5] new commit --- doc/source/getting_started/overview.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/getting_started/overview.rst b/doc/source/getting_started/overview.rst index a5acefb36b97b..40cba56b79fc8 100644 --- a/doc/source/getting_started/overview.rst +++ b/doc/source/getting_started/overview.rst @@ -25,7 +25,7 @@ pandas is well suited for many different kinds of data: need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, :class:`Series` (1-dimensional) -and :class:`DataFrame` (2-dimensional) handle the vast majority of typical use +and :class:`DataFrame` (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, :class:`DataFrame` provides everything that R's ``data.frame`` provides and much more. pandas is built on top of `NumPy From ba8790c45476ebc680831ecf3d51dc50f52b480e Mon Sep 17 00:00:00 2001 From: Bhard27 Date: Sun, 30 Aug 2020 14:50:20 +0530 Subject: [PATCH 4/5] content changed --- doc/source/getting_started/overview.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/source/getting_started/overview.rst b/doc/source/getting_started/overview.rst index 40cba56b79fc8..5cc0b049f9e59 100644 --- a/doc/source/getting_started/overview.rst +++ b/doc/source/getting_started/overview.rst @@ -174,3 +174,4 @@ License ------- .. literalinclude:: ../../../LICENSE + From 361ebeec855b8a289a268b5aedeec9117b84d261 Mon Sep 17 00:00:00 2001 From: Bhard27 Date: Sun, 30 Aug 2020 14:59:53 +0530 Subject: [PATCH 5/5] new commit --- doc/source/getting_started/overview.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/getting_started/overview.rst b/doc/source/getting_started/overview.rst index 5cc0b049f9e59..032ba73a7293d 100644 --- a/doc/source/getting_started/overview.rst +++ b/doc/source/getting_started/overview.rst @@ -72,7 +72,7 @@ Some other notes extensively tweaked in `Cython `__ code. However, as with anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster - -specialized tool. + specialized tool. - pandas is a dependency of `statsmodels `__, making it an important part of the