Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

pandas is a Python package that provides 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 towards this goal. Easy handling of missing data (represented as NaN, NA, or NaT) 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 

The fundamental package for scientific computing with Python.

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. 

Logistic Regression is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. In logistic regression, the dependent variable is a binary variable that contains data coded as 1 or 0

The Python micro framework for building web applications.

I learned from w3school website. lesson-1 &gt; HTML Documents. lesson-2 &gt; HTML Attributes. lesson-3 &gt; HTML Heading. lesson-4 &gt; HTML Paragraph. lesson-5 &gt; HTML Style. lesson-6 &gt; HTML Formating. lesson-7 &gt; HTML Quotations lesson-8 &gt; HTML Comments lesson-9 &gt; HTML Colors lesson-10&gt; HTML CSS lesson-11&gt; HTML Links lesson-12&gt; HTML Images. project -How to build a page with html. 

#css my resets and preferences. ##css resets resets: by michael bates. this is basically set up to override any of chrome's default handling. although the idea of css resets is not my own; the versions on the web have little in common with mine. essentially, there is nothing here specifically intended to support older browsers. this is strictly written for the current version of the chrome browser. unfortunately chrome still has some -webkit crap, which requires some prefixing. this is why there are prefixed properties in the resets (buttons). as i continue styling items; this will grow. my intension is to see every "user agent stylesheet" style crossed off in dev tools. this way, i know that all styles are those i've specifically chosen. hence my resets bare little resemblence to other resets. about the universal (*) selector: i've read it can slow down a webpage... i don't care. it does what i need; its a tool that i like. ##css preferences keeping css simple is a never-ending task. classes are great, especially when they can be effectively reused. styling by id has great specificity, but /* border: 1px solid rgba(0, 255, 50, 0.9)!important; */ 1. I keep this commented out, but can be toggled on and off 2. Easily get a visual reference to elements in my page. It's a handy tool for me. box-sizing: border-box; 1. This just ensures everything on the page is following this box model.  I only use this box model (no need to mix and match.. doesn't make sense to me; --My Opinion) Declare it once; now ot doesn't need to be re-declared for any element, class or id... This is the most concise way as far as I know to set this. This doesn't need to be overridden in cascading styles. references @CSS Tricks:1 2. this is the box model that makes sense to me and I'm used to styling with this margin is the only property that does not get coutnted in the size of the box margin is essentially how your 'box' is spaced relative to other elements('boxes') then the sizing of the box will take the dimensions you provide in the style properties border-box may be default for most browsers, but I declare it in my resets so that spacing is handled unambiguously. position: relative; 1. Apparently this can break IE6 -very cool! 2. Setting everything to relative by default  You can position child elements with absolute positioning (lots of nifty tricks with absolute positioning) Static, fixed and absolute positioning must be explicitly declared. Since these are the exceptions -at least in my experience, this makes sense to make the default relative positioning. padding: 0; margin: 0; - just my final defaults to remove any browser explicit settings. - I don't want margins/ or padding without explicit declarations. if you're new, this doesn't overwhelm you, you have time to sort through overriding styles, 

Cognitive_challengers (Challenge 1)

Libraries:

pandasby pandas-dev

pandasby pandas-dev

numpyby numpy

numpyby numpy

Data Visualization:

seabornby mwaskom

seabornby mwaskom

Model Building:

LogisticRegressionby raj_pandi

LogisticRegressionby raj_pandi

Front End Application:

flaskby pallets

flaskby pallets

HTMLby Ye-Yint-Naing

HTMLby Ye-Yint-Naing

CSSby Hachero

CSSby Hachero