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Merging DataFrames with pandas

The data you need is not in a single file. It may be spread across a number of text files, spreadsheets, or databases. Import the data you’re interested in as a collection of DataFrames and combine them to answer your central questions. This course is all about the act of combining — or merging — DataFrames. Organize, reshape, and aggregate multiple datasets to answer your specific questions.

Lead by Team Anaconda, Data Science Training

Preparing data

Different techniques to import multiple files into DataFrames. Share information between DataFrames using their indexes. How indexes work is essential to merging DataFrames.

misho-kr

Intermediate Python for Finance

Learn how to use Python data structures, execution control statements, and DataFrames to manipulate financial data. Work with pandas, using data from the Federal Reserve Bank, to explore national economic trends—an essential part of understanding investment strategies. Calculate risk based on stock price data, and display this data in easy to read plots.

By Kennedy Behrman, Data Engineer, Author, Founder

Datetimes and Dictionaries

Create and manipulate Python datetime objects to help you identify key financial events, such as Black Friday. Store and efficiently look up items using Python dictionaries.

misho-kr

Increasing http.Server boilerplate

So, how does a well configured server look according to Cloudflare?

func NewServer(addr string, handler http.Handler) *http.Server {
	return &http.Server{
		Addr:    addr,
		Handler: handler,

misho-kr

Statistical Thinking in Python (Part 1)

After acquiring data and getting them into a form you can work with, you want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. You will start building the foundation to think statistically, speak the language of your data, and understand what your data is telling you. Get up-to-speed and begin thinking statistically.

By Justin Bois Lecturer at the California Institute of Technology

Graphical exploratory data analysis

Before diving into sophisticated statistical inference techniques, you should first explore your data by plotting them and computing simple summary statistics. This process, called exploratory data analysis, is a crucial first step in statistical analysis of data.

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Analyzing Police Activity with pandas

You will explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior. Practice cleaning messy data, creating visualizations, combining and reshaping datasets, and manipulating time series data. Analyzing Police Activity with pandas will give you valuable experience analyzing a dataset.

Lead by Kevin Markham Founder of Data School

Preparing the data for analysis

Examine and clean the dataset, to make working with it a more efficient process. Fix data types, handling missing values, and dropping columns and rows while learning about the Stanford Open Policing Project dataset.

misho-kr

Introduction to SQL

This course teaches syntax in SQL shared by many types of databases, such as PostgreSQL, MySQL, SQL Server, and Oracle. This course teaches you everything you need to know to begin working with databases today!

Lead by Nick Carchedi, Product Manager at DataCamp

Selecting columns

Brief introduction to working with relational databases. Learn about their structure, how to talk about them using database lingo, and how to begin an analysis using simple SQL commands to select and summarize columns from database tables.

misho-kr

Summary of "Merging DataFrames with pandas

Lead by Timo Grossenbacher, Project Lead Automated Journalism at Tamedia

You can model different phenomena in your data, as well as the relationships between them. This gives your data structure and consistency, which results in better data quality. In this course, you'll experience this firsthand by working with a real-life dataset that was used to investigate questionable university affiliations. You'll learn how to create tables and specify their relationships, as well as how to enforce data integrity. You'll also discover other unique features of database systems, such as constraints.

Your first database

Create your very first database with a set of simple SQL commands. Next, migrate data from existing flat tables into that database. Learn how meta-information about a datab

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Introduction to Data Visualization in Python

Lead by Team Anaconda, Data Science Training

This course provides a stronger foundation in data visualization in Python, broader coverage of the Matplotlib library and an overview of seaborn, a package for statistical graphics. Topics covered include customizing graphics, plotting two-dimensional arrays (like pseudocolor plots, contour plots, and images), statistical graphics (like visualizing distributions and regressions), and working with time series and image data.

Customizing plots

Review of basic plotting with Matplotlib, customizing plots using Matplotlib. Overlaying plots, making subplots, controlling axes, adding legends and annotations, and using different plot styles.

misho-kr

Object-Oriented Programming in Python

Object-oriented programming (OOP) is a widely used programming paradigm that reduces development times—making it easier to read, reuse, and maintain your code. OOP shifts the focus from thinking about code as a sequence of actions to looking at your program as a collection of objects that interact with each other. In this course, you’ll learn how to create classes, which act as the blueprints for every object in Python. You’ll then leverage principles called inheritance and polymorphism to reuse and optimize code.

By Alex Yarosh, Curriculum Developer @ Cockroach Labs

OOP Fundamentals

Learn what object-oriented programming (OOP) is, how it differs from procedural-programming, and how it can be applied. Define your own classes, and create methods, attributes, and constructors.

misho-kr

Interactive Data Visualization with Bokeh

Lead by Team Anaconda, Data Science Training

Bokeh is an interactive data visualization library for Python—and other languages—that targets modern web browsers for presentation. It can create versatile, data-driven graphics and connect the full power of the entire Python data science stack to create rich, interactive visualizations.

• Glyphs
  • Visual shapes
• circles, squares, triangles