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1 Metadata-Version: 2.1
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2 Name: pandas
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3 Version: 2.0.3
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4 Summary: Powerful data structures for data analysis, time series, and statistics
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5 Author-email: The Pandas Development Team <pandas-dev@python.org>
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6 License: BSD 3-Clause License
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7
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8 Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
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9 All rights reserved.
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10
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11 Copyright (c) 2011-2023, Open source contributors.
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12
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13 Redistribution and use in source and binary forms, with or without
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14 modification, are permitted provided that the following conditions are met:
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15
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16 * Redistributions of source code must retain the above copyright notice, this
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17 list of conditions and the following disclaimer.
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18
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19 * Redistributions in binary form must reproduce the above copyright notice,
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20 this list of conditions and the following disclaimer in the documentation
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21 and/or other materials provided with the distribution.
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22
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23 * Neither the name of the copyright holder nor the names of its
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24 contributors may be used to endorse or promote products derived from
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25 this software without specific prior written permission.
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26
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27 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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28 AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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29 IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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30 DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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31 FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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32 DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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33 SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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34 CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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35 OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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36 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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37
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38 Project-URL: homepage, https://pandas.pydata.org
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39 Project-URL: documentation, https://pandas.pydata.org/docs/
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40 Project-URL: repository, https://github.com/pandas-dev/pandas
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41 Classifier: Development Status :: 5 - Production/Stable
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42 Classifier: Environment :: Console
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43 Classifier: Intended Audience :: Science/Research
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44 Classifier: License :: OSI Approved :: BSD License
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45 Classifier: Operating System :: OS Independent
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46 Classifier: Programming Language :: Cython
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47 Classifier: Programming Language :: Python
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48 Classifier: Programming Language :: Python :: 3
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49 Classifier: Programming Language :: Python :: 3 :: Only
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50 Classifier: Programming Language :: Python :: 3.8
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51 Classifier: Programming Language :: Python :: 3.9
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52 Classifier: Programming Language :: Python :: 3.10
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53 Classifier: Programming Language :: Python :: 3.11
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54 Classifier: Topic :: Scientific/Engineering
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55 Requires-Python: >=3.8
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56 Description-Content-Type: text/markdown
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57 License-File: LICENSE
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58 License-File: AUTHORS.md
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59 Requires-Dist: python-dateutil (>=2.8.2)
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60 Requires-Dist: pytz (>=2020.1)
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61 Requires-Dist: tzdata (>=2022.1)
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62 Requires-Dist: numpy (>=1.20.3) ; python_version < "3.10"
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63 Requires-Dist: numpy (>=1.21.0) ; python_version >= "3.10"
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64 Requires-Dist: numpy (>=1.23.2) ; python_version >= "3.11"
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65 Provides-Extra: all
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66 Requires-Dist: beautifulsoup4 (>=4.9.3) ; extra == 'all'
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67 Requires-Dist: bottleneck (>=1.3.2) ; extra == 'all'
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68 Requires-Dist: brotlipy (>=0.7.0) ; extra == 'all'
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69 Requires-Dist: fastparquet (>=0.6.3) ; extra == 'all'
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70 Requires-Dist: fsspec (>=2021.07.0) ; extra == 'all'
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71 Requires-Dist: gcsfs (>=2021.07.0) ; extra == 'all'
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72 Requires-Dist: html5lib (>=1.1) ; extra == 'all'
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73 Requires-Dist: hypothesis (>=6.34.2) ; extra == 'all'
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74 Requires-Dist: jinja2 (>=3.0.0) ; extra == 'all'
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75 Requires-Dist: lxml (>=4.6.3) ; extra == 'all'
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76 Requires-Dist: matplotlib (>=3.6.1) ; extra == 'all'
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77 Requires-Dist: numba (>=0.53.1) ; extra == 'all'
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78 Requires-Dist: numexpr (>=2.7.3) ; extra == 'all'
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79 Requires-Dist: odfpy (>=1.4.1) ; extra == 'all'
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80 Requires-Dist: openpyxl (>=3.0.7) ; extra == 'all'
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81 Requires-Dist: pandas-gbq (>=0.15.0) ; extra == 'all'
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82 Requires-Dist: psycopg2 (>=2.8.6) ; extra == 'all'
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83 Requires-Dist: pyarrow (>=7.0.0) ; extra == 'all'
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84 Requires-Dist: pymysql (>=1.0.2) ; extra == 'all'
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85 Requires-Dist: PyQt5 (>=5.15.1) ; extra == 'all'
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86 Requires-Dist: pyreadstat (>=1.1.2) ; extra == 'all'
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87 Requires-Dist: pytest (>=7.3.2) ; extra == 'all'
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88 Requires-Dist: pytest-xdist (>=2.2.0) ; extra == 'all'
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89 Requires-Dist: pytest-asyncio (>=0.17.0) ; extra == 'all'
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90 Requires-Dist: python-snappy (>=0.6.0) ; extra == 'all'
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91 Requires-Dist: pyxlsb (>=1.0.8) ; extra == 'all'
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92 Requires-Dist: qtpy (>=2.2.0) ; extra == 'all'
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93 Requires-Dist: scipy (>=1.7.1) ; extra == 'all'
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94 Requires-Dist: s3fs (>=2021.08.0) ; extra == 'all'
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95 Requires-Dist: SQLAlchemy (>=1.4.16) ; extra == 'all'
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96 Requires-Dist: tables (>=3.6.1) ; extra == 'all'
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97 Requires-Dist: tabulate (>=0.8.9) ; extra == 'all'
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98 Requires-Dist: xarray (>=0.21.0) ; extra == 'all'
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99 Requires-Dist: xlrd (>=2.0.1) ; extra == 'all'
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100 Requires-Dist: xlsxwriter (>=1.4.3) ; extra == 'all'
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101 Requires-Dist: zstandard (>=0.15.2) ; extra == 'all'
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102 Provides-Extra: aws
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103 Requires-Dist: s3fs (>=2021.08.0) ; extra == 'aws'
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104 Provides-Extra: clipboard
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105 Requires-Dist: PyQt5 (>=5.15.1) ; extra == 'clipboard'
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106 Requires-Dist: qtpy (>=2.2.0) ; extra == 'clipboard'
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107 Provides-Extra: compression
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108 Requires-Dist: brotlipy (>=0.7.0) ; extra == 'compression'
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109 Requires-Dist: python-snappy (>=0.6.0) ; extra == 'compression'
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110 Requires-Dist: zstandard (>=0.15.2) ; extra == 'compression'
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111 Provides-Extra: computation
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112 Requires-Dist: scipy (>=1.7.1) ; extra == 'computation'
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113 Requires-Dist: xarray (>=0.21.0) ; extra == 'computation'
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114 Provides-Extra: excel
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115 Requires-Dist: odfpy (>=1.4.1) ; extra == 'excel'
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116 Requires-Dist: openpyxl (>=3.0.7) ; extra == 'excel'
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117 Requires-Dist: pyxlsb (>=1.0.8) ; extra == 'excel'
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118 Requires-Dist: xlrd (>=2.0.1) ; extra == 'excel'
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119 Requires-Dist: xlsxwriter (>=1.4.3) ; extra == 'excel'
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120 Provides-Extra: feather
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121 Requires-Dist: pyarrow (>=7.0.0) ; extra == 'feather'
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122 Provides-Extra: fss
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123 Requires-Dist: fsspec (>=2021.07.0) ; extra == 'fss'
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124 Provides-Extra: gcp
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125 Requires-Dist: gcsfs (>=2021.07.0) ; extra == 'gcp'
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126 Requires-Dist: pandas-gbq (>=0.15.0) ; extra == 'gcp'
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127 Provides-Extra: hdf5
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128 Requires-Dist: tables (>=3.6.1) ; extra == 'hdf5'
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129 Provides-Extra: html
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130 Requires-Dist: beautifulsoup4 (>=4.9.3) ; extra == 'html'
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131 Requires-Dist: html5lib (>=1.1) ; extra == 'html'
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132 Requires-Dist: lxml (>=4.6.3) ; extra == 'html'
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133 Provides-Extra: mysql
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134 Requires-Dist: SQLAlchemy (>=1.4.16) ; extra == 'mysql'
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135 Requires-Dist: pymysql (>=1.0.2) ; extra == 'mysql'
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136 Provides-Extra: output_formatting
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137 Requires-Dist: jinja2 (>=3.0.0) ; extra == 'output_formatting'
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138 Requires-Dist: tabulate (>=0.8.9) ; extra == 'output_formatting'
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139 Provides-Extra: parquet
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140 Requires-Dist: pyarrow (>=7.0.0) ; extra == 'parquet'
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141 Provides-Extra: performance
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142 Requires-Dist: bottleneck (>=1.3.2) ; extra == 'performance'
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143 Requires-Dist: numba (>=0.53.1) ; extra == 'performance'
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144 Requires-Dist: numexpr (>=2.7.1) ; extra == 'performance'
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145 Provides-Extra: plot
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146 Requires-Dist: matplotlib (>=3.6.1) ; extra == 'plot'
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147 Provides-Extra: postgresql
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148 Requires-Dist: SQLAlchemy (>=1.4.16) ; extra == 'postgresql'
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149 Requires-Dist: psycopg2 (>=2.8.6) ; extra == 'postgresql'
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150 Provides-Extra: spss
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151 Requires-Dist: pyreadstat (>=1.1.2) ; extra == 'spss'
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152 Provides-Extra: sql-other
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153 Requires-Dist: SQLAlchemy (>=1.4.16) ; extra == 'sql-other'
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154 Provides-Extra: test
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155 Requires-Dist: hypothesis (>=6.34.2) ; extra == 'test'
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156 Requires-Dist: pytest (>=7.3.2) ; extra == 'test'
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157 Requires-Dist: pytest-xdist (>=2.2.0) ; extra == 'test'
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158 Requires-Dist: pytest-asyncio (>=0.17.0) ; extra == 'test'
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159 Provides-Extra: xml
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160 Requires-Dist: lxml (>=4.6.3) ; extra == 'xml'
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161
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162 <div align="center">
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163 <img src="https://pandas.pydata.org/static/img/pandas.svg"><br>
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164 </div>
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165
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166 -----------------
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167
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168 # pandas: powerful Python data analysis toolkit
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169 [](https://pypi.org/project/pandas/)
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170 [](https://anaconda.org/anaconda/pandas/)
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171 [](https://doi.org/10.5281/zenodo.3509134)
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172 [](https://pypi.org/project/pandas/)
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173 [](https://github.com/pandas-dev/pandas/blob/main/LICENSE)
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174 [](https://codecov.io/gh/pandas-dev/pandas)
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175 [](https://pepy.tech/project/pandas)
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176 [](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack)
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177 [](https://numfocus.org)
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178 [](https://github.com/psf/black)
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179 [](https://pycqa.github.io/isort/)
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180
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181 ## What is it?
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182
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183 **pandas** is a Python package that provides fast, flexible, and expressive data
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184 structures designed to make working with "relational" or "labeled" data both
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185 easy and intuitive. It aims to be the fundamental high-level building block for
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186 doing practical, **real world** data analysis in Python. Additionally, it has
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187 the broader goal of becoming **the most powerful and flexible open source data
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188 analysis / manipulation tool available in any language**. It is already well on
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189 its way towards this goal.
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190
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191 ## Main Features
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192 Here are just a few of the things that pandas does well:
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193
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194 - Easy handling of [**missing data**][missing-data] (represented as
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195 `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
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196 - Size mutability: columns can be [**inserted and
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197 deleted**][insertion-deletion] from DataFrame and higher dimensional
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198 objects
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199 - Automatic and explicit [**data alignment**][alignment]: objects can
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200 be explicitly aligned to a set of labels, or the user can simply
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201 ignore the labels and let `Series`, `DataFrame`, etc. automatically
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202 align the data for you in computations
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203 - Powerful, flexible [**group by**][groupby] functionality to perform
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204 split-apply-combine operations on data sets, for both aggregating
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205 and transforming data
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206 - Make it [**easy to convert**][conversion] ragged,
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207 differently-indexed data in other Python and NumPy data structures
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208 into DataFrame objects
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209 - Intelligent label-based [**slicing**][slicing], [**fancy
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210 indexing**][fancy-indexing], and [**subsetting**][subsetting] of
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211 large data sets
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212 - Intuitive [**merging**][merging] and [**joining**][joining] data
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213 sets
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214 - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
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215 data sets
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216 - [**Hierarchical**][mi] labeling of axes (possible to have multiple
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217 labels per tick)
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218 - Robust IO tools for loading data from [**flat files**][flat-files]
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219 (CSV and delimited), [**Excel files**][excel], [**databases**][db],
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220 and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
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221 - [**Time series**][timeseries]-specific functionality: date range
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222 generation and frequency conversion, moving window statistics,
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223 date shifting and lagging
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224
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225
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226 [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
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227 [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion
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228 [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures
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229 [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine
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230 [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe
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231 [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges
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232 [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced
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233 [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing
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234 [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging
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235 [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index
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236 [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
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237 [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
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238 [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex
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239 [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files
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240 [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files
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241 [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries
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242 [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables
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243 [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality
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244
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245 ## Where to get it
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246 The source code is currently hosted on GitHub at:
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247 https://github.com/pandas-dev/pandas
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248
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249 Binary installers for the latest released version are available at the [Python
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250 Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/).
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251
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252 ```sh
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253 # conda
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254 conda install pandas
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255 ```
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256
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257 ```sh
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258 # or PyPI
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259 pip install pandas
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260 ```
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261
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262 ## Dependencies
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263 - [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
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264 - [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
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265 - [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz)
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266
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267 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.
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268
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269 ## Installation from sources
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270 To install pandas from source you need [Cython](https://cython.org/) in addition to the normal
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271 dependencies above. Cython can be installed from PyPI:
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272
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273 ```sh
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274 pip install cython
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275 ```
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276
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277 In the `pandas` directory (same one where you found this file after
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278 cloning the git repo), execute:
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279
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280 ```sh
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281 python setup.py install
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282 ```
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283
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284 or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):
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285
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286
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287 ```sh
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288 python -m pip install -e . --no-build-isolation --no-use-pep517
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289 ```
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290
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291 or alternatively
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292
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293 ```sh
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294 python setup.py develop
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295 ```
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296
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297 See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-from-source).
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298
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299 ## License
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300 [BSD 3](LICENSE)
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301
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302 ## Documentation
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303 The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable
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304
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305 ## Background
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306 Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and
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307 has been under active development since then.
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308
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309 ## Getting Help
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310
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311 For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas).
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312 Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).
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313
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314 ## Discussion and Development
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315 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 [Slack channel](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) is available for quick development related questions.
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316
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317 ## Contributing to pandas [](https://www.codetriage.com/pandas-dev/pandas)
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318
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319 All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
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320
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321 A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**.
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322
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323 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.
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324
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325 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).
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326
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327 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!
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328
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329 Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Slack](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack).
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330
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331 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/.github/blob/master/CODE_OF_CONDUCT.md)
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