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113+ < h1 id ="about-pandas "> About pandas< a class ="headerlink " href ="#about-pandas " title ="Permanent link "> #</ a > </ h1 >
114+ < h2 id ="history-of-development "> History of development< a class ="headerlink " href ="#history-of-development " title ="Permanent link "> #</ a > </ h2 >
115+ < p > In 2008, < em > pandas</ em > development began at < a href ="https://www.aqr.com "> AQR Capital Management</ a > .
116+ By the end of 2009 it had been < a href ="https://en.wikipedia.org/wiki/Open_source "> open sourced</ a > ,
117+ and is actively supported today by a community of like-minded individuals around the world who
118+ contribute their valuable time and energy to help make open source < em > pandas</ em >
119+ possible. Thank you to < a href ="team.html "> all of our contributors</ a > .</ p >
120+ < p > Since 2015, < em > pandas</ em > is a < a href ="https://numfocus.org/sponsored-projects "> NumFOCUS sponsored project</ a > .
121+ This will help ensure the success of development of < em > pandas</ em > as a world-class open-source project.</ p >
122+ < h3 id ="timeline "> Timeline< a class ="headerlink " href ="#timeline " title ="Permanent link "> #</ a > </ h3 >
123+ < ul >
124+ < li > < strong > 2008</ strong > : Development of < em > pandas</ em > started</ li >
125+ < li > < strong > 2009</ strong > : < em > pandas</ em > becomes open source</ li >
126+ < li > < strong > 2012</ strong > : First edition of < em > Python for Data Analysis</ em > is published</ li >
127+ < li > < strong > 2015</ strong > : < em > pandas</ em > becomes a < a href ="https://numfocus.org/sponsored-projects "> NumFOCUS sponsored project</ a > </ li >
128+ < li > < strong > 2018</ strong > : First in-person core developer sprint</ li >
129+ </ ul >
130+ < h2 id ="library-highlights "> Library Highlights< a class ="headerlink " href ="#library-highlights " title ="Permanent link "> #</ a > </ h2 >
131+ < ul >
132+ < li >
133+ < p > A fast and efficient < strong > DataFrame</ strong > object for data manipulation with
134+ integrated indexing;</ p >
135+ </ li >
136+ < li >
137+ < p > Tools for < strong > reading and writing data</ strong > between in-memory data structures and
138+ different formats: CSV and text files, Microsoft Excel, SQL databases, and
139+ the fast HDF5 format;</ p >
140+ </ li >
141+ < li >
142+ < p > Intelligent < strong > data alignment</ strong > and integrated handling of < strong > missing data</ strong > :
143+ gain automatic label-based alignment in computations and easily manipulate
144+ messy data into an orderly form;</ p >
145+ </ li >
146+ < li >
147+ < p > Flexible < strong > reshaping</ strong > and pivoting of data sets;</ p >
148+ </ li >
149+ < li >
150+ < p > Intelligent label-based < strong > slicing</ strong > , < strong > fancy indexing</ strong > , and < strong > subsetting</ strong >
151+ of large data sets;</ p >
152+ </ li >
153+ < li >
154+ < p > Columns can be inserted and deleted from data structures for < strong > size
155+ mutability</ strong > ;</ p >
156+ </ li >
157+ < li >
158+ < p > Aggregating or transforming data with a powerful < strong > group by</ strong > engine
159+ allowing split-apply-combine operations on data sets;</ p >
160+ </ li >
161+ < li >
162+ < p > High performance < strong > merging and joining</ strong > of data sets;</ p >
163+ </ li >
164+ < li >
165+ < p > < strong > Hierarchical axis indexing</ strong > provides an intuitive way of working with
166+ high-dimensional data in a lower-dimensional data structure;</ p >
167+ </ li >
168+ < li >
169+ < p > < strong > Time series</ strong > -functionality: date range generation and frequency
170+ conversion, moving window statistics, date shifting and lagging.
171+ Even create domain-specific time offsets and join time
172+ series without losing data;</ p >
173+ </ li >
174+ < li >
175+ < p > Highly < strong > optimized for performance</ strong > , with critical code paths written in
176+ < a href ="https://cython.org "> Cython</ a > or C.</ p >
177+ </ li >
178+ < li >
179+ < p > Python with < em > pandas</ em > is in use in a wide variety of < strong > academic and
180+ commercial</ strong > domains, including Finance, Neuroscience, Economics,
181+ Statistics, Advertising, Web Analytics, and more.</ p >
182+ </ li >
183+ </ ul >
184+ < h2 id ="mission "> Mission< a class ="headerlink " href ="#mission " title ="Permanent link "> #</ a > </ h2 >
185+ < p > < em > pandas</ em > aims to be the fundamental high-level building block for doing practical,
186+ real world data analysis in Python.
187+ Additionally, it has the broader goal of becoming the most powerful and flexible
188+ open source data analysis / manipulation tool available in any language.</ p >
189+ < h2 id ="vision "> Vision< a class ="headerlink " href ="#vision " title ="Permanent link "> #</ a > </ h2 >
190+ < p > A world where data analytics and manipulation software is:</ p >
191+ < ul >
192+ < li > Accessible to everyone</ li >
193+ < li > Free for users to use and modify</ li >
194+ < li > Flexible</ li >
195+ < li > Powerful</ li >
196+ < li > Easy to use</ li >
197+ < li > Fast</ li >
198+ </ ul >
199+ < h2 id ="values "> Values< a class ="headerlink " href ="#values " title ="Permanent link "> #</ a > </ h2 >
200+ < p > Is in the core of < em > pandas</ em > to be respectful and welcoming with everybody,
201+ users, contributors and the broader community. Regardless of level of experience,
202+ gender, gender identity and expression, sexual orientation, disability,
203+ personal appearance, body size, race, ethnicity, age, religion, or nationality.</ p >
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