Glossary

Key Points

Before we start	Python is an open source and platform independent programming language. Jupyter Notebook and the Spyder IDE are great tools to code in and interact with Python. With the large Python community it is easy to find help on the internet.
Short Introduction to Programming in Python	Python is an interpreted language which can be used interactively (executing one command at a time) or in scripting mode (executing a series of commands saved in file). One can assign a value to a variable in Python. Those variables can be of several types, such as string, integer, floating point and complex numbers. Lists and tuples are similar in that they are ordered lists of elements; they differ in that a tuple is immutable (cannot be changed). Dictionaries are data structures that provide mappings between keys and values.
Starting With Data	Libraries enable us to extend the functionality of Python. Pandas is a popular library for working with data. A Dataframe is a Pandas data structure that allows one to access data by column (name or index) or row. Aggregating data using the `groupby()` function enables you to generate useful summaries of data quickly. Plots can be created from DataFrames or subsets of data that have been generated with `groupby()`.
Indexing, Slicing and Subsetting DataFrames in Python	In Python, portions of data can be accessed using indices, slices, column headings, and condition-based subsetting. Python uses 0-based indexing, in which the first element in a list, tuple or any other data structure has an index of 0. Pandas enables common data exploration steps such as data indexing, slicing and conditional subsetting.
Data Types and Formats	Pandas uses other names for data types than Python, for example: `object` for textual data. A column in a DataFrame can only have one data type. The data type in a DataFrame’s single column can be checked using `dtype`. Make conscious decisions about how to manage missing data. A DataFrame can be saved to a CSV file using the `to_csv` function.
Combining DataFrames with Pandas	Pandas’ `merge` and `concat` can be used to combine subsets of a DataFrame, or even data from different files. `join` function combines DataFrames based on index or column. Joining two DataFrames can be done in multiple ways (left, right, and inner) depending on what data must be in the final DataFrame. `to_csv` can be used to write out DataFrames in CSV format.
Data Workflows and Automation	Loops help automate repetitive tasks over sets of items. Loops combined with functions provide a way to process data more efficiently than we could by hand. Conditional statements enable execution of different operations on different data. Functions enable code reuse.
Making Plots With plotnine	The `data`, `aes` variables and a `geometry` are the main elements of a plotnine graph With the `+` operator, additional `scale_`, `theme_`, `xlab/ylab` and `facet_*` elements are added
Data Ingest and Visualization - Matplotlib and Pandas	Matplotlib is the engine behind plotnine and Pandas plots. The object-based nature of matplotlib plots enables their detailed customization after they have been created. Export plots to a file using the `savefig` method.
Accessing SQLite Databases Using Python and Pandas	sqlite3 provides a SQL-like interface to read, query, and write SQL databases from Python. sqlite3 can be used with Pandas to read SQL data to the familiar Pandas DataFrame. Pandas and sqlite3 can also be used to transfer between the CSV and SQL formats.

0-based indexing: is a way of assigning indices to elements in a sequential, ordered data structure starting from 0, i.e. where the first element of the sequence has index 0.
CSV (file): is an acronym which stands for Comma-Separated Values file. CSV files store tabular data, either numbers, strings, or a combination of the two, in plain text with columns separated by a comma and rows by the carriage return character.
database: is an organized collection of data.
dataframe: is a two-dimensional labeled data structure with columns of (potentially) different type.
data structure: is a particular way of organizing data in memory.
data type: is a particular kind of item that can be assigned to a variable, defined by the values it can take, the programming language in use and the operations that can be performed on it.
dictionary: is an unordered Python data structure designed to contain key-value pairs, where both the key and the value can be integers, floats or strings. Elements of a dictionary can be accessed by their key and can be modified.
docstring: is an optional documentation string to describe what a Python function does.
faceting: is the act of plotting relationships between set variables in multiple subsets of the data with the results appearing as different panels in the same figure.
float: is a Python data type designed to store positive and negative decimal numbers by means of a floating point representation.
function: is a group of related statements that perform a specific task.
integer: is a Python data type designed to store positive and negative integer numbers.
interactive mode: is an online mode of operation in which the user writes the commands directly on the command line one-by-one and execute them immediately by pressing a button on the keyword, usually Return.
join key: is a variable or an array representing the column names over which pandas.DataFrame.join() merge together columns of different data sets.
library: is a set of functions and methods grouped together to perform some specific sort of tasks.
list: is a Python data structure designed to contain sequences of integers, floats, strings and any combination of the previous. The sequence is ordered and indexed by integers, starting from 0. Elements of a list can be accessed by their index and can be modified.
loop: is a sequence of instructions that is continually repeated until a condition is satisfied.
NaN: is an acronym for Not-a-Number and represents that either a value is missing or the calculation cannot output any meaningful result.
None: is an object that represents no value.
scripting mode: is an offline mode of operation in which the user writes the commands to be executed in a text file (with .py extension for Python) which is then compiled or interpreted to run the program. Notes that Python interprets script on run-time and compiles a binary version of the program to speed up the execution time.
Sequential (data structure): is an ordered group of objects stored in memory which can be accessed specifying their index, i.e. their position, in the structure.
SQL: or Structured Query Language, is a domain-specific language for managing data stored in a relational database management system (RDBMS).
SQLite: is a self-contained, public domain SQL database engine.
string: is a Python data type designed to store sequences of characters.
tuple: is a Python data structure designed to contain sequences of integers, floats, strings and any combination of the previous. The sequence is ordered and indexed by integers, starting from 0. Elements of a tuple can be accessed by their index but cannot be modified.

Data Analysis and Visualisation in Python: Glossary

Key Points

Glossary