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Getting com-fur-table with Pandas in Python
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Presented by Anna Filippova
Getting com-fur-table with Pandas: use cases, advantages, and caveats
The Pandas library is a well known native alternative to data wrangling in R or SQL. But when is it the more appropriate choice, what are its advantages, and limitations? This talk will take a deeper dive into the library, exploring not just how but why some things work the way they do, covering potential gotchas and common questions users may have. In particular, we'll look more closely at the use of single and multi-indexes, time series and group-by objects. The talk is aimed at a variety of skill levels, and will also include a brief overview of pandas basic functions, contrasting them with SQL and R, as well as look at basics of data import, visualization and statistical analysis.
Getting com-fur-table with Pandas: use cases, advantages, and caveats
The Pandas library is a well known native alternative to data wrangling in R or SQL. But when is it the more appropriate choice, what are its advantages, and limitations? This talk will take a deeper dive into the library, exploring not just how but why some things work the way they do, covering potential gotchas and common questions users may have. In particular, we'll look more closely at the use of single and multi-indexes, time series and group-by objects. The talk is aimed at a variety of skill levels, and will also include a brief overview of pandas basic functions, contrasting them with SQL and R, as well as look at basics of data import, visualization and statistical analysis.
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