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pandas-dev__pandas-24759

You will be provided with a partial code base and an issue statement explaining a problem to resolve. <issue> BUG: DataFrame with tz-aware data and max(axis=1) returns NaN I have a dataframe looks like this, and its column 2 is missing:  When I try to select the max date in each row, I got all NaN in return:  However, If the dataframe's type is float64, the selection work as expected. </issue> <code> [start of README.md] 1 <div align="center"> 2 <img src="https://github.com/pandas-dev/pandas/blob/master/doc/logo/pandas_logo.png"><br> 3 </div> 4 5 ----------------- 6 7 # pandas: powerful Python data analysis toolkit 8 9 <table> 10 <tr> 11 <td>Latest Release</td> 12 <td> 13 <a href="https://pypi.org/project/pandas/"> 14 <img src="https://img.shields.io/pypi/v/pandas.svg" alt="latest release" /> 15 </a> 16 </td> 17 </tr> 18 <td></td> 19 <td> 20 <a href="https://anaconda.org/anaconda/pandas/"> 21 <img src="https://anaconda.org/conda-forge/pandas/badges/version.svg" alt="latest release" /> 22 </a> 23 </td> 24 </tr> 25 <tr> 26 <td>Package Status</td> 27 <td> 28 <a href="https://pypi.org/project/pandas/"> 29 <img src="https://img.shields.io/pypi/status/pandas.svg" alt="status" /></td> 30 </a> 31 </tr> 32 <tr> 33 <td>License</td> 34 <td> 35 <a href="https://github.com/pandas-dev/pandas/blob/master/LICENSE"> 36 <img src="https://img.shields.io/pypi/l/pandas.svg" alt="license" /> 37 </a> 38 </td> 39 </tr> 40 <tr> 41 <td>Build Status</td> 42 <td> 43 <a href="https://travis-ci.org/pandas-dev/pandas"> 44 <img src="https://travis-ci.org/pandas-dev/pandas.svg?branch=master" alt="travis build status" /> 45 </a> 46 </td> 47 </tr> 48 <tr> 49 <td></td> 50 <td> 51 <a href="https://dev.azure.com/pandas-dev/pandas/_build/latest?definitionId=1&branch=master"> 52 <img src="https://dev.azure.com/pandas-dev/pandas/_apis/build/status/pandas-dev.pandas?branch=master" alt="Azure Pipelines build status" /> 53 </a> 54 </td> 55 </tr> 56 <tr> 57 <td>Coverage</td> 58  <td> 59 <a href="https://codecov.io/gh/pandas-dev/pandas"> 60 <img src="https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=master" alt="coverage" /> 61 </a> 62 </td> 63 </tr> 64 <tr> 65 <td>Downloads</td> 66 <td> 67 <a href="https://pandas.pydata.org"> 68 <img src="https://anaconda.org/conda-forge/pandas/badges/downloads.svg" alt="conda-forge downloads" /> 69 </a> 70 </td> 71 </tr> 72 <tr> 73 <td>Gitter</td> 74 <td> 75 <a href="https://gitter.im/pydata/pandas"> 76 <img src="https://badges.gitter.im/Join%20Chat.svg" 77 </a> 78 </td> 79 </tr> 80 </table> 81 82 83 84 ## What is it? 85 86 **pandas** is a Python package providing fast, flexible, and expressive data 87 structures designed to make working with "relational" or "labeled" data both 88 easy and intuitive. It aims to be the fundamental high-level building block for 89 doing practical, **real world** data analysis in Python. Additionally, it has 90 the broader goal of becoming **the most powerful and flexible open source data 91 analysis / manipulation tool available in any language**. It is already well on 92 its way towards this goal. 93 94 ## Main Features 95 Here are just a few of the things that pandas does well: 96 97 - Easy handling of [**missing data**][missing-data] (represented as 98 `NaN`) in floating point as well as non-floating point data 99 - Size mutability: columns can be [**inserted and 100 deleted**][insertion-deletion] from DataFrame and higher dimensional 101 objects 102 - Automatic and explicit [**data alignment**][alignment]: objects can 103 be explicitly aligned to a set of labels, or the user can simply 104 ignore the labels and let `Series`, `DataFrame`, etc. automatically 105 align the data for you in computations 106 - Powerful, flexible [**group by**][groupby] functionality to perform 107 split-apply-combine operations on data sets, for both aggregating 108 and transforming data 109 - Make it [**easy to convert**][conversion] ragged, 110 differently-indexed data in other Python and NumPy data structures 111 into DataFrame objects 112 - Intelligent label-based [**slicing**][slicing], [**fancy 113 indexing**][fancy-indexing], and [**subsetting**][subsetting] of 114 large data sets 115 - Intuitive [**merging**][merging] and [**joining**][joining] data 116 sets 117 - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of 118 data sets 119 - [**Hierarchical**][mi] labeling of axes (possible to have multiple 120 labels per tick) 121 - Robust IO tools for loading data from [**flat files**][flat-files] 122 (CSV and delimited), [**Excel files**][excel], [**databases**][db], 123 and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] 124 - [**Time series**][timeseries]-specific functionality: date range 125 generation and frequency conversion, moving window statistics, 126 moving window linear regressions, date shifting and lagging, etc. 127 128 129 [missing-data]: https://pandas.pydata.org/pandas-docs/stable/missing_data.html#working-with-missing-data 130 [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion 131 [alignment]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html?highlight=alignment#intro-to-data-structures 132 [groupby]: https://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine 133 [conversion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe 134 [slicing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges 135 [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix 136 [subsetting]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing 137 [merging]: https://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging 138 [joining]: https://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index 139 [reshape]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-and-pivot-tables 140 [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations 141 [mi]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindex 142 [flat-files]: https://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files 143 [excel]: https://pandas.pydata.org/pandas-docs/stable/io.html#excel-files 144 [db]: https://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries 145 [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/io.html#hdf5-pytables 146 [timeseries]: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-series-date-functionality 147 148 ## Where to get it 149 The source code is currently hosted on GitHub at: 150 https://github.com/pandas-dev/pandas 151 152 Binary installers for the latest released version are available at the [Python 153 package index](https://pypi.org/project/pandas) and on conda. 154 155 ```sh 156 # conda 157 conda install pandas 158 ``` 159 160 ```sh 161 # or PyPI 162 pip install pandas 163 ``` 164 165 ## Dependencies 166 - [NumPy](https://www.numpy.org): 1.12.0 or higher 167 - [python-dateutil](https://labix.org/python-dateutil): 2.5.0 or higher 168 - [pytz](https://pythonhosted.org/pytz): 2011k or higher 169 170 See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) 171 for recommended and optional dependencies. 172 173 ## Installation from sources 174 To install pandas from source you need Cython in addition to the normal 175 dependencies above. Cython can be installed from pypi: 176 177 ```sh 178 pip install cython 179 ``` 180 181 In the `pandas` directory (same one where you found this file after 182 cloning the git repo), execute: 183 184 ```sh 185 python setup.py install 186 ``` 187 188 or for installing in [development mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs): 189 190 ```sh 191 python setup.py develop 192 ``` 193 194 Alternatively, you can use `pip` if you want all the dependencies pulled 195 in automatically (the `-e` option is for installing it in [development 196 mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs)): 197 198 ```sh 199 pip install -e . 200 ``` 201 202 See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/install.html#installing-from-source). 203 204 ## License 205 [BSD 3](LICENSE) 206 207 ## Documentation 208 The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable 209 210 ## Background 211 Work on ``pandas`` started at AQR (a quantitative hedge fund) in 2008 and 212 has been under active development since then. 213 214 ## Getting Help 215 216 For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas). 217 Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata). 218 219 ## Discussion and Development 220 Most development discussion is taking place on github in this repo. Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Gitter channel](https://gitter.im/pydata/pandas) is available for quick development related questions. 221 222 ## Contributing to pandas [](https://www.codetriage.com/pandas-dev/pandas) 223 224 All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. 225 226 A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas-docs.github.io/pandas-docs-travis/contributing.html)**. There is also an [overview](.github/CONTRIBUTING.md) on GitHub. 227 228 If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) where you could start out. 229 230 You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to [subscribe to pandas on CodeTriage](https://www.codetriage.com/pandas-dev/pandas). 231 232 Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it! 233 234 Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Gitter](https://gitter.im/pydata/pandas). 235 [end of README.md] </code> I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format. <patch> --- a/file.py +++ b/file.py @@ -1,27 +1,35 @@ def euclidean(a, b): - while b: - a, b = b, a % b - return a + if b == 0: + return a + return euclidean(b, a % b) def bresenham(x0, y0, x1, y1): points = [] dx = abs(x1 - x0) dy = abs(y1 - y0) - sx = 1 if x0 < x1 else -1 - sy = 1 if y0 < y1 else -1 - err = dx - dy + x, y = x0, y0 + sx = -1 if x0 > x1 else 1 + sy = -1 if y0 > y1 else 1 - while True: - points.append((x0, y0)) - if x0 == x1 and y0 == y1: - break - e2 = 2 * err - if e2 > -dy: + if dx > dy: + err = dx / 2.0 + while x != x1: + points.append((x, y)) err -= dy - x0 += sx - if e2 < dx: - err += dx - y0 += sy + if err < 0: + y += sy + err += dx + x += sx + else: + err = dy / 2.0 + while y != y1: + points.append((x, y)) + err -= dx + if err < 0: + x += sx + err += dy + y += sy + points.append((x, y)) return points </patch>

pandas-dev/pandas

e2bf1ff491dbdabce1590506d2b1dafca34db82e

BUG: DataFrame with tz-aware data and max(axis=1) returns NaN I have a dataframe looks like this, and its column 2 is missing:  When I try to select the max date in each row, I got all NaN in return:  However, If the dataframe's type is float64, the selection work as expected.

pls show pd.show_versions() and df_datetime64.info() Hello! Hijacking this issue as I've also verified this behaviour (actually, it took a while to discover after upgrading to 0.19.0 and discovering some odd dropping of timezones - see #14524, which is a duplication of #13905). This behaviour was masked to my program previously as Pandas 0.18.1 was dropping the timezones from all relevant columns before I tried to perform this step. Once upgrading to 0.19.0 half the operations I was performing stopped dropping timezones, leading to mismatch between tz-aware and tz-naive timestamps which I've been chasing down the rabbit hole for a couple of days now. I've verified that this is present in pandas 0.18.1 and 0.19.0. From some stepping through of the code, this looks like a potential problem with the numpy implementations of `.max(axis=1)`, but I haven't yet found the culprit! This issue has meant that I've been forced to roll back to 0.18.1 to use the drop timezone bug in order to make the `df.max(axis=1)` work, which is frustrating! I have also tried a `df.T.max()` to work around the issue, but this infuriatingly returns an empty series (see below). #### A small, complete example of the issue ``` python import pandas as pd df = pd.DataFrame(pd.date_range(start=pd.Timestamp('2016-01-01 00:00:00+00'), end=pd.Timestamp('2016-01-01 23:59:59+00'), freq='H')) df.columns = ['a'] df['b'] = df.a.subtract(pd.Timedelta(seconds=60*60)) # if using pandas 0.19.0 to test, ensure that this is a series of timedeltas instead of a single - we want b and c to be tz-naive. df[['a', 'b']].max() # This is fine, produces two numbers df[['a', 'b']].max(axis=1) # This is not fine, produces a correctly sized series of NaN df['c'] = df.a.subtract(pd.Timedelta(seconds=60)) # if using pandas 0.19.0 to test, ensure that this is a series of timedeltas instead of a single - we want b and c to be tz-naive. df[['b', 'c']].max(axis=1) # This is fine, produces correctly sized series of valid timestamps without timezone df[['a', 'b']].T.max() # produces an empty series. ``` #### Expected Output Calling `df.max(axis=1)` on a dataframe with timezone-aware timestamps should return valid timestamps, not NaN. #### Output of `pd.show_versions()` (I have tested in two virtualenvs, the only difference between the two being the pandas version) <details> # Paste the output here ## INSTALLED VERSIONS commit: None python: 2.7.10.final.0 python-bits: 64 OS: Darwin OS-release: 14.5.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: None.None pandas: 0.18.1 nose: 1.3.7 pip: 8.1.2 setuptools: 28.6.0 Cython: None numpy: 1.11.2 scipy: None statsmodels: None xarray: None IPython: None sphinx: None patsy: None dateutil: 2.5.3 pytz: 2016.7 blosc: None bottleneck: None tables: None numexpr: None matplotlib: None openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: None boto: None pandas_datareader: None </details>

2019-01-14T00:28:56Z

<patch> diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst --- a/doc/source/whatsnew/v0.24.0.rst +++ b/doc/source/whatsnew/v0.24.0.rst @@ -1647,6 +1647,7 @@ Timezones - Bug in :meth:`DataFrame.any` returns wrong value when ``axis=1`` and the data is of datetimelike type (:issue:`23070`) - Bug in :meth:`DatetimeIndex.to_period` where a timezone aware index was converted to UTC first before creating :class:`PeriodIndex` (:issue:`22905`) - Bug in :meth:`DataFrame.tz_localize`, :meth:`DataFrame.tz_convert`, :meth:`Series.tz_localize`, and :meth:`Series.tz_convert` where ``copy=False`` would mutate the original argument inplace (:issue:`6326`) +- Bug in :meth:`DataFrame.max` and :meth:`DataFrame.min` with ``axis=1`` where a :class:`Series` with ``NaN`` would be returned when all columns contained the same timezone (:issue:`10390`) Offsets ^^^^^^^ diff --git a/pandas/core/frame.py b/pandas/core/frame.py --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -49,6 +49,7 @@ find_common_type) from pandas.core.dtypes.common import ( is_dict_like, + is_datetime64tz_dtype, is_object_dtype, is_extension_type, is_extension_array_dtype, @@ -7390,7 +7391,9 @@ def f(x): return op(x, axis=axis, skipna=skipna, **kwds) # exclude timedelta/datetime unless we are uniform types - if axis == 1 and self._is_mixed_type and self._is_datelike_mixed_type: + if (axis == 1 and self._is_datelike_mixed_type + and (not self._is_homogeneous_type + and not is_datetime64tz_dtype(self.dtypes[0]))): numeric_only = True if numeric_only is None: </patch>

[]

[]

pandas-dev__pandas-37834

You will be provided with a partial code base and an issue statement explaining a problem to resolve. <issue> read_json with dtype=False infers Missing Values as None Run against master: ```python In [13]: pd.read_json("[null]", dtype=True) Out[13]: 0 0 NaN In [14]: pd.read_json("[null]", dtype=False) Out[14]: 0 0 None ``` I think the second above is an issue - should probably return `np.nan` instead of `None` </issue> <code> [start of README.md] 1 <div align="center"> 2 <img src="https://dev.pandas.io/static/img/pandas.svg"><br> 3 </div> 4 5 ----------------- 6 7 # pandas: powerful Python data analysis toolkit 8 [](https://pypi.org/project/pandas/) 9 [](https://anaconda.org/anaconda/pandas/) 10 [](https://doi.org/10.5281/zenodo.3509134) 11 [](https://pypi.org/project/pandas/) 12 [](https://github.com/pandas-dev/pandas/blob/master/LICENSE) 13 [](https://travis-ci.org/pandas-dev/pandas) 14 [](https://dev.azure.com/pandas-dev/pandas/_build/latest?definitionId=1&branch=master) 15 [](https://codecov.io/gh/pandas-dev/pandas) 16 [](https://pandas.pydata.org) 17 [](https://gitter.im/pydata/pandas) 18 [](https://numfocus.org) 19 [](https://github.com/psf/black) 20 21 ## What is it? 22 23 **pandas** is a Python package that provides fast, flexible, and expressive data 24 structures designed to make working with "relational" or "labeled" data both 25 easy and intuitive. It aims to be the fundamental high-level building block for 26 doing practical, **real world** data analysis in Python. Additionally, it has 27 the broader goal of becoming **the most powerful and flexible open source data 28 analysis / manipulation tool available in any language**. It is already well on 29 its way towards this goal. 30 31 ## Main Features 32 Here are just a few of the things that pandas does well: 33 34 - Easy handling of [**missing data**][missing-data] (represented as 35 `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data 36 - Size mutability: columns can be [**inserted and 37 deleted**][insertion-deletion] from DataFrame and higher dimensional 38 objects 39 - Automatic and explicit [**data alignment**][alignment]: objects can 40 be explicitly aligned to a set of labels, or the user can simply 41 ignore the labels and let `Series`, `DataFrame`, etc. automatically 42 align the data for you in computations 43 - Powerful, flexible [**group by**][groupby] functionality to perform 44 split-apply-combine operations on data sets, for both aggregating 45 and transforming data 46 - Make it [**easy to convert**][conversion] ragged, 47 differently-indexed data in other Python and NumPy data structures 48 into DataFrame objects 49 - Intelligent label-based [**slicing**][slicing], [**fancy 50 indexing**][fancy-indexing], and [**subsetting**][subsetting] of 51 large data sets 52 - Intuitive [**merging**][merging] and [**joining**][joining] data 53 sets 54 - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of 55 data sets 56 - [**Hierarchical**][mi] labeling of axes (possible to have multiple 57 labels per tick) 58 - Robust IO tools for loading data from [**flat files**][flat-files] 59 (CSV and delimited), [**Excel files**][excel], [**databases**][db], 60 and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] 61 - [**Time series**][timeseries]-specific functionality: date range 62 generation and frequency conversion, moving window statistics, 63 date shifting and lagging. 64 65 66 [missing-data]: https://pandas.pydata.org/pandas-docs/stable/missing_data.html#working-with-missing-data 67 [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion 68 [alignment]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html?highlight=alignment#intro-to-data-structures 69 [groupby]: https://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine 70 [conversion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe 71 [slicing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges 72 [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix 73 [subsetting]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing 74 [merging]: https://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging 75 [joining]: https://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index 76 [reshape]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-and-pivot-tables 77 [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations 78 [mi]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindex 79 [flat-files]: https://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files 80 [excel]: https://pandas.pydata.org/pandas-docs/stable/io.html#excel-files 81 [db]: https://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries 82 [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/io.html#hdf5-pytables 83 [timeseries]: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-series-date-functionality 84 85 ## Where to get it 86 The source code is currently hosted on GitHub at: 87 https://github.com/pandas-dev/pandas 88 89 Binary installers for the latest released version are available at the [Python 90 package index](https://pypi.org/project/pandas) and on conda. 91 92 ```sh 93 # conda 94 conda install pandas 95 ``` 96 97 ```sh 98 # or PyPI 99 pip install pandas 100 ``` 101 102 ## Dependencies 103 - [NumPy](https://www.numpy.org) 104 - [python-dateutil](https://labix.org/python-dateutil) 105 - [pytz](https://pythonhosted.org/pytz) 106 107 See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies. 108 109 ## Installation from sources 110 To install pandas from source you need Cython in addition to the normal 111 dependencies above. Cython can be installed from pypi: 112 113 ```sh 114 pip install cython 115 ``` 116 117 In the `pandas` directory (same one where you found this file after 118 cloning the git repo), execute: 119 120 ```sh 121 python setup.py install 122 ``` 123 124 or for installing in [development mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs): 125 126 127 ```sh 128 python -m pip install -e . --no-build-isolation --no-use-pep517 129 ``` 130 131 If you have `make`, you can also use `make develop` to run the same command. 132 133 or alternatively 134 135 ```sh 136 python setup.py develop 137 ``` 138 139 See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/install.html#installing-from-source). 140 141 ## License 142 [BSD 3](LICENSE) 143 144 ## Documentation 145 The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable 146 147 ## Background 148 Work on ``pandas`` started at AQR (a quantitative hedge fund) in 2008 and 149 has been under active development since then. 150 151 ## Getting Help 152 153 For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas). 154 Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata). 155 156 ## Discussion and Development 157 Most development discussions take place on github in this repo. Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Gitter channel](https://gitter.im/pydata/pandas) is available for quick development related questions. 158 159 ## Contributing to pandas [](https://www.codetriage.com/pandas-dev/pandas) 160 161 All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. 162 163 A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**. There is also an [overview](.github/CONTRIBUTING.md) on GitHub. 164 165 If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) where you could start out. 166 167 You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to [subscribe to pandas on CodeTriage](https://www.codetriage.com/pandas-dev/pandas). 168 169 Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it! 170 171 Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Gitter](https://gitter.im/pydata/pandas). 172 173 As contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: [Contributor Code of Conduct](https://github.com/pandas-dev/pandas/blob/master/.github/CODE_OF_CONDUCT.md) 174 [end of README.md] </code> I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format. <patch> --- a/file.py +++ b/file.py @@ -1,27 +1,35 @@ def euclidean(a, b): - while b: - a, b = b, a % b - return a + if b == 0: + return a + return euclidean(b, a % b) def bresenham(x0, y0, x1, y1): points = [] dx = abs(x1 - x0) dy = abs(y1 - y0) - sx = 1 if x0 < x1 else -1 - sy = 1 if y0 < y1 else -1 - err = dx - dy + x, y = x0, y0 + sx = -1 if x0 > x1 else 1 + sy = -1 if y0 > y1 else 1 - while True: - points.append((x0, y0)) - if x0 == x1 and y0 == y1: - break - e2 = 2 * err - if e2 > -dy: + if dx > dy: + err = dx / 2.0 + while x != x1: + points.append((x, y)) err -= dy - x0 += sx - if e2 < dx: - err += dx - y0 += sy + if err < 0: + y += sy + err += dx + x += sx + else: + err = dy / 2.0 + while y != y1: + points.append((x, y)) + err -= dx + if err < 0: + x += sx + err += dy + y += sy + points.append((x, y)) return points </patch>

pandas-dev/pandas

793b6351f59b42ec6a0dc42ccce679a5cf232a1a

read_json with dtype=False infers Missing Values as None Run against master: ```python In [13]: pd.read_json("[null]", dtype=True) Out[13]: 0 0 NaN In [14]: pd.read_json("[null]", dtype=False) Out[14]: 0 0 None ``` I think the second above is an issue - should probably return `np.nan` instead of `None`

I _think_ that this isn't a bug. NaN is a numerical value. If `dtype=False` we shouldn't infer it to be a numerical value. But if it it is an issue I can do this one. Yea there is certainly some ambiguity here and @jorisvandenbossche might have some thoughts but I don't necessarily think the JSON `null` would map better to `None` than it would to `np.nan`. read_csv would also convert this to NaN: ```python >>> pd.read_csv(io.StringIO("1\nnull")) 1 0 NaN ``` Complementing what @chrisstpierre said, the null type in json can be numerical, string, list, ..., as seen in https://stackoverflow.com/questions/21120999/representing-null-in-json. So setting up as `np.nan` even though `dtype` is set explicitly to `False` may be beneficial when working with numerical data only but not with strings for example. I think a `np.nan` in a column of strings should not be desirable. Hi all - Just curious whether stringifying `None` in the following example is expected behaviour? ```python df = pd.read_json('[null]', dtype={0: str}) print(df.values) # [['None']] ``` If yes, how can I avoid this while at the same time specifying the the column to be of `str` type? Thanks for your help!

2020-11-14T16:52:13Z

<patch> diff --git a/doc/source/whatsnew/v1.2.0.rst b/doc/source/whatsnew/v1.2.0.rst --- a/doc/source/whatsnew/v1.2.0.rst +++ b/doc/source/whatsnew/v1.2.0.rst @@ -653,6 +653,7 @@ I/O - Bug in :func:`read_html` was raising a ``TypeError`` when supplying a ``pathlib.Path`` argument to the ``io`` parameter (:issue:`37705`) - :meth:`to_excel` and :meth:`to_markdown` support writing to fsspec URLs such as S3 and Google Cloud Storage (:issue:`33987`) - Bug in :meth:`read_fw` was not skipping blank lines (even with ``skip_blank_lines=True``) (:issue:`37758`) +- Parse missing values using :func:`read_json` with ``dtype=False`` to ``NaN`` instead of ``None`` (:issue:`28501`) - :meth:`read_fwf` was inferring compression with ``compression=None`` which was not consistent with the other :meth:``read_*`` functions (:issue:`37909`) Period diff --git a/pandas/io/json/_json.py b/pandas/io/json/_json.py --- a/pandas/io/json/_json.py +++ b/pandas/io/json/_json.py @@ -20,7 +20,7 @@ from pandas.core.dtypes.common import ensure_str, is_period_dtype -from pandas import DataFrame, MultiIndex, Series, isna, to_datetime +from pandas import DataFrame, MultiIndex, Series, isna, notna, to_datetime from pandas.core import generic from pandas.core.construction import create_series_with_explicit_dtype from pandas.core.generic import NDFrame @@ -858,7 +858,10 @@ def _try_convert_data(self, name, data, use_dtypes=True, convert_dates=True): # don't try to coerce, unless a force conversion if use_dtypes: if not self.dtype: - return data, False + if all(notna(data)): + return data, False + return data.fillna(np.nan), True + elif self.dtype is True: pass else: </patch>

[]

[]

pandas-dev__pandas-24725

You will be provided with a partial code base and an issue statement explaining a problem to resolve. <issue> DataFrame creation incorrect error message The problem was already mentioned as part of other issues, but still persists in 0.22 https://github.com/pandas-dev/pandas/issues/8020 https://github.com/blaze/blaze/issues/466 Reported both expected shape and input data shape are both transposed which causes a lot of confusion. In my opinion, the reference value should be ` DataFrame.shape`. ```python my_arr = np.array([1, 2, 3]) print("my_arr.shape: {}".format(my_arr.shape)) df = pd.DataFrame(index=[0], columns=range(0, 4), data=my_arr) ``` ```python my_arr.shape: (3,) Traceback (most recent call last): ... ValueError: Shape of passed values is (1, 3), indices imply (4, 1) ``` Below are shapes which are expected to be reported: ```python my_arr = np.array([[0, 1, 2, 3]]) print("my_arr.shape: {}".format(my_arr.shape)) df = pd.DataFrame(index=[0], columns=range(0, 4), data=my_arr) print(df.shape)` ``` ```python my_arr.shape: (1, 4) (1, 4) ``` I'm not sure, whether this is another issue, but in the first example, the error cause is 1-dimensional data while constructor expects 2-dimensional data. The user gets no hint about this from the error message. </issue> <code> [start of README.md] 1 <div align="center"> 2 <img src="https://github.com/pandas-dev/pandas/blob/master/doc/logo/pandas_logo.png"><br> 3 </div> 4 5 ----------------- 6 7 # pandas: powerful Python data analysis toolkit 8 9 <table> 10 <tr> 11 <td>Latest Release</td> 12 <td> 13 <a href="https://pypi.org/project/pandas/"> 14 <img src="https://img.shields.io/pypi/v/pandas.svg" alt="latest release" /> 15 </a> 16 </td> 17 </tr> 18 <td></td> 19 <td> 20 <a href="https://anaconda.org/anaconda/pandas/"> 21 <img src="https://anaconda.org/conda-forge/pandas/badges/version.svg" alt="latest release" /> 22 </a> 23 </td> 24 </tr> 25 <tr> 26 <td>Package Status</td> 27 <td> 28 <a href="https://pypi.org/project/pandas/"> 29 <img src="https://img.shields.io/pypi/status/pandas.svg" alt="status" /></td> 30 </a> 31 </tr> 32 <tr> 33 <td>License</td> 34 <td> 35 <a href="https://github.com/pandas-dev/pandas/blob/master/LICENSE"> 36 <img src="https://img.shields.io/pypi/l/pandas.svg" alt="license" /> 37 </a> 38 </td> 39 </tr> 40 <tr> 41 <td>Build Status</td> 42 <td> 43 <a href="https://travis-ci.org/pandas-dev/pandas"> 44 <img src="https://travis-ci.org/pandas-dev/pandas.svg?branch=master" alt="travis build status" /> 45 </a> 46 </td> 47 </tr> 48 <tr> 49 <td></td> 50 <td> 51 <a href="https://dev.azure.com/pandas-dev/pandas/_build/latest?definitionId=1&branch=master"> 52 <img src="https://dev.azure.com/pandas-dev/pandas/_apis/build/status/pandas-dev.pandas?branch=master" alt="Azure Pipelines build status" /> 53 </a> 54 </td> 55 </tr> 56 <tr> 57 <td>Coverage</td> 58  <td> 59 <a href="https://codecov.io/gh/pandas-dev/pandas"> 60 <img src="https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=master" alt="coverage" /> 61 </a> 62 </td> 63 </tr> 64 <tr> 65 <td>Downloads</td> 66 <td> 67 <a href="https://pandas.pydata.org"> 68 <img src="https://anaconda.org/conda-forge/pandas/badges/downloads.svg" alt="conda-forge downloads" /> 69 </a> 70 </td> 71 </tr> 72 <tr> 73 <td>Gitter</td> 74 <td> 75 <a href="https://gitter.im/pydata/pandas"> 76 <img src="https://badges.gitter.im/Join%20Chat.svg" 77 </a> 78 </td> 79 </tr> 80 </table> 81 82 83 84 ## What is it? 85 86 **pandas** is a Python package providing fast, flexible, and expressive data 87 structures designed to make working with "relational" or "labeled" data both 88 easy and intuitive. It aims to be the fundamental high-level building block for 89 doing practical, **real world** data analysis in Python. Additionally, it has 90 the broader goal of becoming **the most powerful and flexible open source data 91 analysis / manipulation tool available in any language**. It is already well on 92 its way towards this goal. 93 94 ## Main Features 95 Here are just a few of the things that pandas does well: 96 97 - Easy handling of [**missing data**][missing-data] (represented as 98 `NaN`) in floating point as well as non-floating point data 99 - Size mutability: columns can be [**inserted and 100 deleted**][insertion-deletion] from DataFrame and higher dimensional 101 objects 102 - Automatic and explicit [**data alignment**][alignment]: objects can 103 be explicitly aligned to a set of labels, or the user can simply 104 ignore the labels and let `Series`, `DataFrame`, etc. automatically 105 align the data for you in computations 106 - Powerful, flexible [**group by**][groupby] functionality to perform 107 split-apply-combine operations on data sets, for both aggregating 108 and transforming data 109 - Make it [**easy to convert**][conversion] ragged, 110 differently-indexed data in other Python and NumPy data structures 111 into DataFrame objects 112 - Intelligent label-based [**slicing**][slicing], [**fancy 113 indexing**][fancy-indexing], and [**subsetting**][subsetting] of 114 large data sets 115 - Intuitive [**merging**][merging] and [**joining**][joining] data 116 sets 117 - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of 118 data sets 119 - [**Hierarchical**][mi] labeling of axes (possible to have multiple 120 labels per tick) 121 - Robust IO tools for loading data from [**flat files**][flat-files] 122 (CSV and delimited), [**Excel files**][excel], [**databases**][db], 123 and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] 124 - [**Time series**][timeseries]-specific functionality: date range 125 generation and frequency conversion, moving window statistics, 126 moving window linear regressions, date shifting and lagging, etc. 127 128 129 [missing-data]: https://pandas.pydata.org/pandas-docs/stable/missing_data.html#working-with-missing-data 130 [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion 131 [alignment]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html?highlight=alignment#intro-to-data-structures 132 [groupby]: https://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine 133 [conversion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe 134 [slicing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges 135 [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix 136 [subsetting]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing 137 [merging]: https://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging 138 [joining]: https://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index 139 [reshape]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-and-pivot-tables 140 [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations 141 [mi]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindex 142 [flat-files]: https://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files 143 [excel]: https://pandas.pydata.org/pandas-docs/stable/io.html#excel-files 144 [db]: https://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries 145 [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/io.html#hdf5-pytables 146 [timeseries]: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-series-date-functionality 147 148 ## Where to get it 149 The source code is currently hosted on GitHub at: 150 https://github.com/pandas-dev/pandas 151 152 Binary installers for the latest released version are available at the [Python 153 package index](https://pypi.org/project/pandas) and on conda. 154 155 ```sh 156 # conda 157 conda install pandas 158 ``` 159 160 ```sh 161 # or PyPI 162 pip install pandas 163 ``` 164 165 ## Dependencies 166 - [NumPy](https://www.numpy.org): 1.12.0 or higher 167 - [python-dateutil](https://labix.org/python-dateutil): 2.5.0 or higher 168 - [pytz](https://pythonhosted.org/pytz): 2011k or higher 169 170 See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) 171 for recommended and optional dependencies. 172 173 ## Installation from sources 174 To install pandas from source you need Cython in addition to the normal 175 dependencies above. Cython can be installed from pypi: 176 177 ```sh 178 pip install cython 179 ``` 180 181 In the `pandas` directory (same one where you found this file after 182 cloning the git repo), execute: 183 184 ```sh 185 python setup.py install 186 ``` 187 188 or for installing in [development mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs): 189 190 ```sh 191 python setup.py develop 192 ``` 193 194 Alternatively, you can use `pip` if you want all the dependencies pulled 195 in automatically (the `-e` option is for installing it in [development 196 mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs)): 197 198 ```sh 199 pip install -e . 200 ``` 201 202 See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/install.html#installing-from-source). 203 204 ## License 205 [BSD 3](LICENSE) 206 207 ## Documentation 208 The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable 209 210 ## Background 211 Work on ``pandas`` started at AQR (a quantitative hedge fund) in 2008 and 212 has been under active development since then. 213 214 ## Getting Help 215 216 For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas). 217 Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata). 218 219 ## Discussion and Development 220 Most development discussion is taking place on github in this repo. Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Gitter channel](https://gitter.im/pydata/pandas) is available for quick development related questions. 221 222 ## Contributing to pandas [](https://www.codetriage.com/pandas-dev/pandas) 223 224 All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. 225 226 A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas-docs.github.io/pandas-docs-travis/contributing.html)**. There is also an [overview](.github/CONTRIBUTING.md) on GitHub. 227 228 If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) where you could start out. 229 230 You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to [subscribe to pandas on CodeTriage](https://www.codetriage.com/pandas-dev/pandas). 231 232 Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it! 233 234 Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Gitter](https://gitter.im/pydata/pandas). 235 [end of README.md] </code> I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format. <patch> --- a/file.py +++ b/file.py @@ -1,27 +1,35 @@ def euclidean(a, b): - while b: - a, b = b, a % b - return a + if b == 0: + return a + return euclidean(b, a % b) def bresenham(x0, y0, x1, y1): points = [] dx = abs(x1 - x0) dy = abs(y1 - y0) - sx = 1 if x0 < x1 else -1 - sy = 1 if y0 < y1 else -1 - err = dx - dy + x, y = x0, y0 + sx = -1 if x0 > x1 else 1 + sy = -1 if y0 > y1 else 1 - while True: - points.append((x0, y0)) - if x0 == x1 and y0 == y1: - break - e2 = 2 * err - if e2 > -dy: + if dx > dy: + err = dx / 2.0 + while x != x1: + points.append((x, y)) err -= dy - x0 += sx - if e2 < dx: - err += dx - y0 += sy + if err < 0: + y += sy + err += dx + x += sx + else: + err = dy / 2.0 + while y != y1: + points.append((x, y)) + err -= dx + if err < 0: + x += sx + err += dy + y += sy + points.append((x, y)) return points </patch>

pandas-dev/pandas

17a6bc56e5ab6ad3dab12d3a8b20ed69a5830b6f

DataFrame creation incorrect error message The problem was already mentioned as part of other issues, but still persists in 0.22 https://github.com/pandas-dev/pandas/issues/8020 https://github.com/blaze/blaze/issues/466 Reported both expected shape and input data shape are both transposed which causes a lot of confusion. In my opinion, the reference value should be ` DataFrame.shape`. ```python my_arr = np.array([1, 2, 3]) print("my_arr.shape: {}".format(my_arr.shape)) df = pd.DataFrame(index=[0], columns=range(0, 4), data=my_arr) ``` ```python my_arr.shape: (3,) Traceback (most recent call last): ... ValueError: Shape of passed values is (1, 3), indices imply (4, 1) ``` Below are shapes which are expected to be reported: ```python my_arr = np.array([[0, 1, 2, 3]]) print("my_arr.shape: {}".format(my_arr.shape)) df = pd.DataFrame(index=[0], columns=range(0, 4), data=my_arr) print(df.shape)` ``` ```python my_arr.shape: (1, 4) (1, 4) ``` I'm not sure, whether this is another issue, but in the first example, the error cause is 1-dimensional data while constructor expects 2-dimensional data. The user gets no hint about this from the error message.

the first example is wrong. The block manager reports this, but doesn't flip the dim (like we do for everything else), so would welcome a PR to correct that. I don't see a problem with the 2nd. You gave a 1, 4 array. That's the same as the dim of the frame, so it constructs.

2019-01-11T15:13:07Z

<patch> diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst --- a/doc/source/whatsnew/v0.24.0.rst +++ b/doc/source/whatsnew/v0.24.0.rst @@ -1816,6 +1816,7 @@ Reshaping - Bug in :func:`DataFrame.unstack` where a ``ValueError`` was raised when unstacking timezone aware values (:issue:`18338`) - Bug in :func:`DataFrame.stack` where timezone aware values were converted to timezone naive values (:issue:`19420`) - Bug in :func:`merge_asof` where a ``TypeError`` was raised when ``by_col`` were timezone aware values (:issue:`21184`) +- Bug showing an incorrect shape when throwing error during ``DataFrame`` construction. (:issue:`20742`) .. _whatsnew_0240.bug_fixes.sparse: @@ -1853,6 +1854,7 @@ Other - Bug where C variables were declared with external linkage causing import errors if certain other C libraries were imported before Pandas. (:issue:`24113`) + .. _whatsnew_0.24.0.contributors: Contributors diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py --- a/pandas/core/internals/managers.py +++ b/pandas/core/internals/managers.py @@ -1674,7 +1674,15 @@ def create_block_manager_from_arrays(arrays, names, axes): def construction_error(tot_items, block_shape, axes, e=None): """ raise a helpful message about our construction """ passed = tuple(map(int, [tot_items] + list(block_shape))) - implied = tuple(map(int, [len(ax) for ax in axes])) + # Correcting the user facing error message during dataframe construction + if len(passed) <= 2: + passed = passed[::-1] + + implied = tuple(len(ax) for ax in axes) + # Correcting the user facing error message during dataframe construction + if len(implied) <= 2: + implied = implied[::-1] + if passed == implied and e is not None: raise e if block_shape[0] == 0: </patch>

[]

[]

pandas-dev__pandas-25419

You will be provided with a partial code base and an issue statement explaining a problem to resolve. <issue> Series.str.casefold `Series.str.lower` implements `str.lower`, as expected. There are also corresponding `Series` methods for the other Python 2 string casing methods. However, Python 3's `str.casefold` is missing. [Casefold](https://docs.python.org/3/library/stdtypes.html#str.casefold) improves string equality and other comparisons, because it handles a greater variety of characters, as per the Unicode Standard. It'd be nice to have `Series.str.casefold` in Pandas. The current alternative is more verbose and is slower than `Series.str.lower`. pd.Series(s.casefold() if isinstance(s, str) else s for s in series) Further, this alternative encourages a frustrating mistake -- forgetting to keep the original Series' index, which causes trouble if the new Series needs to be inserted into the same DataFrame as the original. Apologies for double-posting. I used the wrong account for #25404 . </issue> <code> [start of README.md] 1 <div align="center"> 2 <img src="https://github.com/pandas-dev/pandas/blob/master/doc/logo/pandas_logo.png"><br> 3 </div> 4 5 ----------------- 6 7 # pandas: powerful Python data analysis toolkit 8 9 <table> 10 <tr> 11 <td>Latest Release</td> 12 <td> 13 <a href="https://pypi.org/project/pandas/"> 14 <img src="https://img.shields.io/pypi/v/pandas.svg" alt="latest release" /> 15 </a> 16 </td> 17 </tr> 18 <td></td> 19 <td> 20 <a href="https://anaconda.org/anaconda/pandas/"> 21 <img src="https://anaconda.org/conda-forge/pandas/badges/version.svg" alt="latest release" /> 22 </a> 23 </td> 24 </tr> 25 <tr> 26 <td>Package Status</td> 27 <td> 28 <a href="https://pypi.org/project/pandas/"> 29 <img src="https://img.shields.io/pypi/status/pandas.svg" alt="status" /></td> 30 </a> 31 </tr> 32 <tr> 33 <td>License</td> 34 <td> 35 <a href="https://github.com/pandas-dev/pandas/blob/master/LICENSE"> 36 <img src="https://img.shields.io/pypi/l/pandas.svg" alt="license" /> 37 </a> 38 </td> 39 </tr> 40 <tr> 41 <td>Build Status</td> 42 <td> 43 <a href="https://travis-ci.org/pandas-dev/pandas"> 44 <img src="https://travis-ci.org/pandas-dev/pandas.svg?branch=master" alt="travis build status" /> 45 </a> 46 </td> 47 </tr> 48 <tr> 49 <td></td> 50 <td> 51 <a href="https://dev.azure.com/pandas-dev/pandas/_build/latest?definitionId=1&branch=master"> 52 <img src="https://dev.azure.com/pandas-dev/pandas/_apis/build/status/pandas-dev.pandas?branch=master" alt="Azure Pipelines build status" /> 53 </a> 54 </td> 55 </tr> 56 <tr> 57 <td>Coverage</td> 58  <td> 59 <a href="https://codecov.io/gh/pandas-dev/pandas"> 60 <img src="https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=master" alt="coverage" /> 61 </a> 62 </td> 63 </tr> 64 <tr> 65 <td>Downloads</td> 66 <td> 67 <a href="https://pandas.pydata.org"> 68 <img src="https://anaconda.org/conda-forge/pandas/badges/downloads.svg" alt="conda-forge downloads" /> 69 </a> 70 </td> 71 </tr> 72 <tr> 73 <td>Gitter</td> 74 <td> 75 <a href="https://gitter.im/pydata/pandas"> 76 <img src="https://badges.gitter.im/Join%20Chat.svg" 77 </a> 78 </td> 79 </tr> 80 </table> 81 82 83 84 ## What is it? 85 86 **pandas** is a Python package providing fast, flexible, and expressive data 87 structures designed to make working with "relational" or "labeled" data both 88 easy and intuitive. It aims to be the fundamental high-level building block for 89 doing practical, **real world** data analysis in Python. Additionally, it has 90 the broader goal of becoming **the most powerful and flexible open source data 91 analysis / manipulation tool available in any language**. It is already well on 92 its way towards this goal. 93 94 ## Main Features 95 Here are just a few of the things that pandas does well: 96 97 - Easy handling of [**missing data**][missing-data] (represented as 98 `NaN`) in floating point as well as non-floating point data 99 - Size mutability: columns can be [**inserted and 100 deleted**][insertion-deletion] from DataFrame and higher dimensional 101 objects 102 - Automatic and explicit [**data alignment**][alignment]: objects can 103 be explicitly aligned to a set of labels, or the user can simply 104 ignore the labels and let `Series`, `DataFrame`, etc. automatically 105 align the data for you in computations 106 - Powerful, flexible [**group by**][groupby] functionality to perform 107 split-apply-combine operations on data sets, for both aggregating 108 and transforming data 109 - Make it [**easy to convert**][conversion] ragged, 110 differently-indexed data in other Python and NumPy data structures 111 into DataFrame objects 112 - Intelligent label-based [**slicing**][slicing], [**fancy 113 indexing**][fancy-indexing], and [**subsetting**][subsetting] of 114 large data sets 115 - Intuitive [**merging**][merging] and [**joining**][joining] data 116 sets 117 - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of 118 data sets 119 - [**Hierarchical**][mi] labeling of axes (possible to have multiple 120 labels per tick) 121 - Robust IO tools for loading data from [**flat files**][flat-files] 122 (CSV and delimited), [**Excel files**][excel], [**databases**][db], 123 and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] 124 - [**Time series**][timeseries]-specific functionality: date range 125 generation and frequency conversion, moving window statistics, 126 moving window linear regressions, date shifting and lagging, etc. 127 128 129 [missing-data]: https://pandas.pydata.org/pandas-docs/stable/missing_data.html#working-with-missing-data 130 [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion 131 [alignment]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html?highlight=alignment#intro-to-data-structures 132 [groupby]: https://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine 133 [conversion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe 134 [slicing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges 135 [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix 136 [subsetting]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing 137 [merging]: https://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging 138 [joining]: https://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index 139 [reshape]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-and-pivot-tables 140 [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations 141 [mi]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindex 142 [flat-files]: https://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files 143 [excel]: https://pandas.pydata.org/pandas-docs/stable/io.html#excel-files 144 [db]: https://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries 145 [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/io.html#hdf5-pytables 146 [timeseries]: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-series-date-functionality 147 148 ## Where to get it 149 The source code is currently hosted on GitHub at: 150 https://github.com/pandas-dev/pandas 151 152 Binary installers for the latest released version are available at the [Python 153 package index](https://pypi.org/project/pandas) and on conda. 154 155 ```sh 156 # conda 157 conda install pandas 158 ``` 159 160 ```sh 161 # or PyPI 162 pip install pandas 163 ``` 164 165 ## Dependencies 166 - [NumPy](https://www.numpy.org): 1.12.0 or higher 167 - [python-dateutil](https://labix.org/python-dateutil): 2.5.0 or higher 168 - [pytz](https://pythonhosted.org/pytz): 2011k or higher 169 170 See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) 171 for recommended and optional dependencies. 172 173 ## Installation from sources 174 To install pandas from source you need Cython in addition to the normal 175 dependencies above. Cython can be installed from pypi: 176 177 ```sh 178 pip install cython 179 ``` 180 181 In the `pandas` directory (same one where you found this file after 182 cloning the git repo), execute: 183 184 ```sh 185 python setup.py install 186 ``` 187 188 or for installing in [development mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs): 189 190 ```sh 191 python setup.py develop 192 ``` 193 194 Alternatively, you can use `pip` if you want all the dependencies pulled 195 in automatically (the `-e` option is for installing it in [development 196 mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs)): 197 198 ```sh 199 pip install -e . 200 ``` 201 202 See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/install.html#installing-from-source). 203 204 ## License 205 [BSD 3](LICENSE) 206 207 ## Documentation 208 The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable 209 210 ## Background 211 Work on ``pandas`` started at AQR (a quantitative hedge fund) in 2008 and 212 has been under active development since then. 213 214 ## Getting Help 215 216 For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas). 217 Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata). 218 219 ## Discussion and Development 220 Most development discussion is taking place on github in this repo. Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Gitter channel](https://gitter.im/pydata/pandas) is available for quick development related questions. 221 222 ## Contributing to pandas [](https://www.codetriage.com/pandas-dev/pandas) 223 224 All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. 225 226 A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas-docs.github.io/pandas-docs-travis/contributing.html)**. There is also an [overview](.github/CONTRIBUTING.md) on GitHub. 227 228 If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) where you could start out. 229 230 You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to [subscribe to pandas on CodeTriage](https://www.codetriage.com/pandas-dev/pandas). 231 232 Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it! 233 234 Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Gitter](https://gitter.im/pydata/pandas). 235 [end of README.md] </code> I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format. <patch> --- a/file.py +++ b/file.py @@ -1,27 +1,35 @@ def euclidean(a, b): - while b: - a, b = b, a % b - return a + if b == 0: + return a + return euclidean(b, a % b) def bresenham(x0, y0, x1, y1): points = [] dx = abs(x1 - x0) dy = abs(y1 - y0) - sx = 1 if x0 < x1 else -1 - sy = 1 if y0 < y1 else -1 - err = dx - dy + x, y = x0, y0 + sx = -1 if x0 > x1 else 1 + sy = -1 if y0 > y1 else 1 - while True: - points.append((x0, y0)) - if x0 == x1 and y0 == y1: - break - e2 = 2 * err - if e2 > -dy: + if dx > dy: + err = dx / 2.0 + while x != x1: + points.append((x, y)) err -= dy - x0 += sx - if e2 < dx: - err += dx - y0 += sy + if err < 0: + y += sy + err += dx + x += sx + else: + err = dy / 2.0 + while y != y1: + points.append((x, y)) + err -= dx + if err < 0: + x += sx + err += dy + y += sy + points.append((x, y)) return points </patch>

pandas-dev/pandas

85572de5e7bb188cfecc575ee56786406e79dc79

Series.str.casefold `Series.str.lower` implements `str.lower`, as expected. There are also corresponding `Series` methods for the other Python 2 string casing methods. However, Python 3's `str.casefold` is missing. [Casefold](https://docs.python.org/3/library/stdtypes.html#str.casefold) improves string equality and other comparisons, because it handles a greater variety of characters, as per the Unicode Standard. It'd be nice to have `Series.str.casefold` in Pandas. The current alternative is more verbose and is slower than `Series.str.lower`. pd.Series(s.casefold() if isinstance(s, str) else s for s in series) Further, this alternative encourages a frustrating mistake -- forgetting to keep the original Series' index, which causes trouble if the new Series needs to be inserted into the same DataFrame as the original. Apologies for double-posting. I used the wrong account for #25404 .

can u update with the doc reference for casefold in python docs certainly would take as an enhancement PR

2019-02-23T19:41:19Z

<patch> diff --git a/doc/source/reference/series.rst b/doc/source/reference/series.rst --- a/doc/source/reference/series.rst +++ b/doc/source/reference/series.rst @@ -409,6 +409,7 @@ strings and apply several methods to it. These can be accessed like :template: autosummary/accessor_method.rst Series.str.capitalize + Series.str.casefold Series.str.cat Series.str.center Series.str.contains diff --git a/doc/source/user_guide/text.rst b/doc/source/user_guide/text.rst --- a/doc/source/user_guide/text.rst +++ b/doc/source/user_guide/text.rst @@ -600,6 +600,7 @@ Method Summary :meth:`~Series.str.partition`;Equivalent to ``str.partition`` :meth:`~Series.str.rpartition`;Equivalent to ``str.rpartition`` :meth:`~Series.str.lower`;Equivalent to ``str.lower`` + :meth:`~Series.str.casefold`;Equivalent to ``str.casefold`` :meth:`~Series.str.upper`;Equivalent to ``str.upper`` :meth:`~Series.str.find`;Equivalent to ``str.find`` :meth:`~Series.str.rfind`;Equivalent to ``str.rfind`` diff --git a/doc/source/whatsnew/v0.25.0.rst b/doc/source/whatsnew/v0.25.0.rst --- a/doc/source/whatsnew/v0.25.0.rst +++ b/doc/source/whatsnew/v0.25.0.rst @@ -22,6 +22,7 @@ Other Enhancements - Indexing of ``DataFrame`` and ``Series`` now accepts zerodim ``np.ndarray`` (:issue:`24919`) - :meth:`Timestamp.replace` now supports the ``fold`` argument to disambiguate DST transition times (:issue:`25017`) - :meth:`DataFrame.at_time` and :meth:`Series.at_time` now support :meth:`datetime.time` objects with timezones (:issue:`24043`) +- ``Series.str`` has gained :meth:`Series.str.casefold` method to removes all case distinctions present in a string (:issue:`25405`) - :meth:`DataFrame.set_index` now works for instances of ``abc.Iterator``, provided their output is of the same length as the calling frame (:issue:`22484`, :issue:`24984`) - :meth:`DatetimeIndex.union` now supports the ``sort`` argument. The behaviour of the sort parameter matches that of :meth:`Index.union` (:issue:`24994`) - diff --git a/pandas/core/strings.py b/pandas/core/strings.py --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -2926,7 +2926,7 @@ def rindex(self, sub, start=0, end=None): _shared_docs['casemethods'] = (""" Convert strings in the Series/Index to %(type)s. - + %(version)s Equivalent to :meth:`str.%(method)s`. Returns @@ -2943,6 +2943,7 @@ def rindex(self, sub, start=0, end=None): remaining to lowercase. Series.str.swapcase : Converts uppercase to lowercase and lowercase to uppercase. + Series.str.casefold: Removes all case distinctions in the string. Examples -------- @@ -2989,12 +2990,15 @@ def rindex(self, sub, start=0, end=None): 3 sWaPcAsE dtype: object """) - _shared_docs['lower'] = dict(type='lowercase', method='lower') - _shared_docs['upper'] = dict(type='uppercase', method='upper') - _shared_docs['title'] = dict(type='titlecase', method='title') + _shared_docs['lower'] = dict(type='lowercase', method='lower', version='') + _shared_docs['upper'] = dict(type='uppercase', method='upper', version='') + _shared_docs['title'] = dict(type='titlecase', method='title', version='') _shared_docs['capitalize'] = dict(type='be capitalized', - method='capitalize') - _shared_docs['swapcase'] = dict(type='be swapcased', method='swapcase') + method='capitalize', version='') + _shared_docs['swapcase'] = dict(type='be swapcased', method='swapcase', + version='') + _shared_docs['casefold'] = dict(type='be casefolded', method='casefold', + version='\n .. versionadded:: 0.25.0\n') lower = _noarg_wrapper(lambda x: x.lower(), docstring=_shared_docs['casemethods'] % _shared_docs['lower']) @@ -3010,6 +3014,9 @@ def rindex(self, sub, start=0, end=None): swapcase = _noarg_wrapper(lambda x: x.swapcase(), docstring=_shared_docs['casemethods'] % _shared_docs['swapcase']) + casefold = _noarg_wrapper(lambda x: x.casefold(), + docstring=_shared_docs['casemethods'] % + _shared_docs['casefold']) _shared_docs['ismethods'] = (""" Check whether all characters in each string are %(type)s. </patch>

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