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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. It is free software released under the three-clause BSD license. [2]
Splitting a column into multiple columns (e.g., converting a comma-separated list, specified as a string in one column, into individual values in different columns) Disaggregating repeating columns; Looking up and validating the relevant data from tables or referential files
In a database, a table is a collection of related data organized in table format; consisting of columns and rows. In relational databases , and flat file databases , a table is a set of data elements (values) using a model of vertical columns (identifiable by name) and horizontal rows , the cell being the unit where a row and column intersect ...
Edit and move columns and rows in Calc. To drag a column first select it by clicking its header number. Then press and hold the ALT key. Then click a data cell, and drag the column to a new location. Or right click and delete the selected column (no need for ALT key). Rows are similarly moved (with the ALT key pressed), or deleted. Sort as ...
A query includes a list of columns to include in the final result, normally immediately following the SELECT keyword. An asterisk ("*") can be used to specify that the query should return all columns of all the queried tables. SELECT is the most complex statement in SQL, with optional keywords and clauses that include:
The basic data structure of the relational model is the table, where information about a particular entity (say, an employee) is represented in rows (also called tuples) and columns. Thus, the "relation" in "relational database" refers to the various tables in the database; a relation is a set of tuples. The columns enumerate the various ...
In situations where the number of unique values of a column is far less than the number of rows in the table, column-oriented storage allow significant savings in space through data compression. Columnar storage also allows fast execution of range queries (e.g., show all records where a particular column is between X and Y, or less than X.)
Columns of any conceivable data type (from string types and numeric types to array types and table types) are then acceptable in a 1NF table—although perhaps not always desirable; for example, it may be more desirable to separate a Customer Name column into two separate columns as First Name, Surname.