You can breakdown a complex query and create multiple temporary tables. Then you can run “sanity check” queries against

Another significant thing to note about GGPlot is its approach to graphics and visualization. Generally, GGPlot is more geometrically modular. This means that compositions can often be added to or altered quite easily. This is further beneficial to data science, as often there is a certain idea or data point that might want to be emphasized more than others.,This is the basis for all of SymPy and makes scientific computing with the Python programming language a lot easier. The use of this function can actually allow Python to explore one of the benefits to the Julia language, syntactical expressions. If you’d like to learn more about how the Julia language uses expressions, you can check out a full tutorial on it here:,The Blaze Ecosystem is a set of Pythonic libraries that make it far easier to query and process data in the Python programming language. The Blaze ecosystem actually consists of several different packages:,Over pizza, volunteers went through traces of data to verify details like phone numbers and operating hours at organizations in the guide. Every change was documented on shared spreadsheets, including information that couldn’t be vetted (disconnected phone lines, dead websites, etc.), which were then used to update the site.,Easily the coolest thing that SymPy has to offer is the ability to lambdify any expression or function inside of the language. I have talked before about why I think Lambda is one of the greatest tools available for Python developers. If you would like to read an article all about that topic, you can check it out here:,Many awesome implementations of Pythonic code and math use SymPy for mathematical calculations on the back-end. Here are some notable examples pulled straight from their homepage:,The second great tool I would recommend for data analysis with Python is GGPlot.py. Any scientist who has experience in the R programming language has likely used GGPlot or GGPlot2. Both the R and Python package are fantastic and make plotting a breeze and furthermore in-depth. While the R implementation is certainly worth checking out, the Pythonic version is certainly pretty awesome as well.,You can breakdown a complex query and create multiple temporary tables. Then you can run “sanity check” queries against those tables to make sure they contain correct entries. I highly recommend this approach when designing a new non-trivial query or report.,Bokeh is another interactive plotting library that was built with modern web-browsing and computing in mind. Similarly to Plot.ly, Bokeh might not be as extendable as something like GGPlot, but presents a host of benefits over the former by being incredibly integrated with Javascript. Every data scientist loves interactive visualizations, and Bokeh often takes this to a very high-level and simple API with stunning results.,Steering away from the world of visualization is a fantastic Python package called SymPy. MATLAB, Mathematica, and Julia programmers, hold on to your hats — SymPy is a Python module that allows Pythonic programmers and scientists to use more mathematical bindings. This allows the language to shift from a typically more software engineering approach to mathematics to the other end of the spectrum where code is frequently written as math.,Apple also claims that M1-powered Macs are faster than their Windows counterparts. The company estimates that the new M1 in the MacBook Air will outperform 98 percent of PC laptops sold in the past year. These claims are based on performance tests that Apple performed in-house, though the company isn’t providing more details about them except to say that they are “industry-standard” benchmarks.,Bokeh as a visualization tool is incredibly similar to Plot.ly, and that isn’t a bad thing at all. This means that Bokeh can be used to create beautiful and interactive visualizations incredibly easily.,While Bokeh is similar to Plot.ly in a lot of ways, this is certainly not the case in what is possible with Bokeh. Not only is there support for all of the typical visualization techniques, but Bokeh also allows a user to create interesting and interactive visualizations of nearly anything. One relatively common use for Bokeh is the visualization of network graphs, which is really cool! On top of that, it does come with the geo-data visualizations that one might expect from the Plot.ly library.,A significant difference between the GGPlot package and the Plot.ly package for Python is that GGPlot is much more statistically-focused. Plot.ly is a great tool for data visualization, but is also targeted at more typical data visualization, rather than specifically statistical plotting.,The last great thing about Plot.ly is that it is open-source. While this isn’t as big of a benefit to some, being able to make changes if required or understand how code is being ran on your machine is always a plus. Being open-source software also means that Plot.ly can be used in practical business applications, making it valuable not only to those who program as a hobby, but also for real-world professionals.