When I write about a library or a new concept, I typically like to showcase its working via examples. Sometimes I create simple toy datasets, while on other occasions, I go with the established dataset sites like Kaggle and Google search. Imagine my surprise when I discovered a built-in method in pandas created to address his issue.
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
Hatchet is a Python-based library that allows Pandas dataframes to be indexed by structured tree and graph data. It is intended for analyzing performance data that has a hierarchy (for example, serial or parallel profiles that represent calling context trees, call graphs, nested regions’ timers, etc.). Hatchet implements various operations to analyze a single hierarchical data set or compare multiple data sets, and its API facilitates analyzing such data programmatically.
To use hatchet, install it with pip:
$ pip install hatchet
Or, if you want to develop with this repo directly, run the install script from
the root directory, which will build the cython modules and add the cloned
directory to your
$ source install.sh
#data analysis #pandas dataframes #pandas #hierarchical performance data #graph-indexed pandas dataframes for analyzing hierarchical performance data
Python is famous for its vast selection of libraries and resources from the open-source community. As a Data Analyst/Engineer/Scientist, one might be familiar with popular packages such as Numpy, Pandas, Scikit-learn, Keras, and TensorFlow. Together these modules help us extract value out of data and propels the field of analytics. As data continue to become larger and more complex, one other element to consider is a framework dedicated to processing Big Data, such as Apache Spark. In this article, I will demonstrate the capabilities of distributed/cluster computing and present a comparison between the Pandas DataFrame and Spark DataFrame. My hope is to provide more conviction on choosing the right implementation.
Pandas has become very popular for its ease of use. It utilizes DataFrames to present data in tabular format like a spreadsheet with rows and columns. Importantly, it has very intuitive methods to perform common analytical tasks and a relatively flat learning curve. It loads all of the data into memory on a single machine (one node) for rapid execution. While the Pandas DataFrame has proven to be tremendously powerful in manipulating data, it does have its limits. With data growing at an exponentially rate, complex data processing becomes expensive to handle and causes performance degradation. These operations require parallelization and distributed computing, which the Pandas DataFrame does not support.
Apache Spark is an open-source cluster computing framework. With cluster computing, data processing is distributed and performed in parallel by multiple nodes. This is recognized as the MapReduce framework because the division of labor can usually be characterized by sets of the map, shuffle, and reduce operations found in functional programming. Spark’s implementation of cluster computing is unique because processes 1) are executed in-memory and 2) build up a query plan which does not execute until necessary (known as lazy execution). Although Spark’s cluster computing framework has a broad range of utility, we only look at the Spark DataFrame for the purpose of this article. Similar to those found in Pandas, the Spark DataFrame has intuitive APIs, making it easy to implement.
#pandas dataframe vs. spark dataframe: when parallel computing matters #pandas #pandas dataframe #pandas dataframe vs. spark dataframe #spark #when parallel computing matters
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
It’s now time for some practice problems! See below for details on how to proceed.
All of the code for this course’s practice problems can be found in this GitHub repository.
There are two options that you can use to complete the practice problems:
Note that binder can take up to a minute to load the repository, so please be patient.
Within that repository, there is a folder called
starter-files and a folder called
finished-files. You should open the appropriate practice problems within the
starter-files folder and only consult the corresponding file in the
finished-files folder if you get stuck.
The repository is public, which means that you can suggest changes using a pull request later in this course if you’d like.
#pandas #groupby methods #pandas dataframe #example #practice problems: how to use pandas dataframes' groupby method #practice problems