Python Flashcards
What is Python?
Python is a multipurpose programming language, and it has applicability pretty much anywhere that uses data, mathematical computation, or lines of code.
Like most programming languages, Python works in tandem with an interpreter that executes the finalized lines of codes.
Python goes beyond a basic web development tool:
Data sciences: This field makes up a sizable user base of
Python for both its computing and compiling of data
libraries.
Machine learning: Python’s code can implement machine
learning, which helps refine algorithm-based tech from
voice recognition to content recommendation.
Data mining: Python’s nimbleness and scalability also
makes it an attractive program to process and mine big
data, which has seen a lot of mileage in the finance
sector.
Here are a few of the big advantages of Python:
Popularity and access: Python has a huge community to
support it, which helps maintain its accessibility to any
skill level — it’s also free and open-source software.
Simple syntax: The Python coding language has an
easy-to-learn syntax and uses English words.
Readability: Lines of code written in Python are also
easy to read. For instance, Python uses a nice, clean
break in the form of a new line of code to complete a
command, rather than semicolons or parentheses.
Scalability: You can start a program in Python without
having to worry about the arduous task of rewriting or
adapting code for other platforms as you scale up.
How do I Print text in Python?
print(‘some message’)
How do I comment in Python?
- comment out this line of code
How are the primary math functions written in Python?
print(2580 + 198) add
print(10484 - 9274) subtract
print(23 * 42) multiply
print(1512 / 27) divide
print(234 // 132) returns integer part of division
print(356%17) returns modulus of division
print(1.01 ** 365) raises a number to a power
What is Pandas for Python?
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.
It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language. It is already well on its way toward this goal.
pandas is well suited for many different kinds of data:
• Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
• Ordered and unordered (not necessarily fixed-frequency) time series data.
• Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
• Any other form of observational / statistical data sets.
The data need not be labeled at all to be placed into a
pandas data structure
The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the
vast majority of typical use cases in finance, statistics, social science, and many areas of engineering.
For R users, DataFrame provides everything that R’s data.frame provides and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries.
Here are just a few of the things that pandas does well:
• Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
• Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
• Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply
ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations
• Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
• Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into
DataFrame objects
• Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
• Intuitive merging and joining data sets
• Flexible reshaping and pivoting of data sets
• Hierarchical labeling of axes (possible to have multiple labels per tick)
• Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading
data from the ultrafast HDF5 format
• Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting, and lagging.
