Search results
Results from the WOW.Com Content Network
Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series .
In many systems for computational statistics, such as R and Python's pandas, a data frame or data table is a data type supporting the table abstraction. Conceptually, it is a list of records or observations all containing the same fields or columns. The implementation consists of a list of arrays or vectors, each with a name.
tabulate, Python module for converting data structures to wiki table markup; wikitables, Python module for reading wiki table markup; H63: Using the scope attribute to associate header cells and data cells in data tables | Techniques for WCAG 2.0. Tables | Usability & Web Accessibility. Yale University. Tables with Multi-Level Headers.
Pandas – High-performance computing (HPC) data structures and data analysis tools for Python in Python and Cython (statsmodels, scikit-learn) Perl Data Language – Scientific computing with Perl; Ploticus – software for generating a variety of graphs from raw data; PSPP – A free software alternative to IBM SPSS Statistics
In analogy with relational databases, a column family is as a "table", each key-value pair being a "row". Each column is a tuple consisting of a column name, a value, and a timestamp. In a relational database table, this data would be grouped together within a table with other non-related data. Two types of column families exist:
the Pandas (Python) module pandas – Python Data Analysis Library; the .NET Framework libraries NodaTime, TZ4Net and zoneinfo Archived 24 December 2017 at the Wayback Machine; the Haskell libraries timezone-series and timezone-olson; the Erlang module ezic; The Go standard library time package; The Rust crate chrono-tz; The Squeak Smalltalk ...
Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").
Python [24] [25] with well-known scientific computing packages: NumPy, SymPy and SciPy. [26] [27] [28] R is a widely used system with a focus on data manipulation and statistics which implements the S language. [29] Many add-on packages are available (free software, GNU GPL license). SAS, [30] a system of software products for statistics.