You’re stuck with a complex data manipulation task. What programming tools can help you out?

In terms of the Python libraries, the choice of tools really depends on the specifics of the complex data manipulation task you are trying to solve. Pandas is an invaluable tool for tabular data operations like analyzing, filtering, merging, and reshaping, etc. NumPy is essential for numerical computations on multidimensional arrays. SciPy is great for advanced statistical analysis. As far as data visualization is concerned, Matplotlib offers a range of plotting options, but Seaborn can be a better choice for more complex visualizations. When working with data, there is not really a 'cookie-cutter' solution when solving complex data manipulation tasks; one might be using one or more of these Python libraries in order to solve the problem.

Python has been my go-to for data manipulation for years. The extensive collection of libraries filled with utilities makes your task easier, you just need to find the right functions for it. Last year during the development of a project, I had to generate data for a time series chart. It was easy to generate this data using Pandas alone as it has various functions to group data according to specified time intervals. Python data processing and manipulating libraries support a variety of file extensions which can cover a majority of your datasets.

When tackling complex data manipulation tasks in engineering, the choice of programming tools is critical. Popular options like Python, MATLAB, R, SQL, Excel, and Julia offer unique strengths. MATLAB excels in numerical computation, R in statistical analysis, SQL in database querying, Excel in user-friendly manipulation, and Julia in high-performance computing. Consider the specific project requirements to select the most suitable tool, ensuring efficient and effective data handling.

In my experience, it depends on the use case, but the most frequently used ones for me are - - Excel: Power Query Editor for data transformation and reshaping. - Browser Console: JavaScript methods like map(), filter(), and reduce() for quick data manipulation. - Python: Pandas library for robust data structures and functions. - SQL: Powerful tool for querying data in relational databases.

My go to approach is making diagrams and visualising the data. I feel it is easy to comprehend images than comprehending numbers.

The data.table package in R has various useful functionalities. It can be used for filtering, grouping, creating and transforming columns, creating lags and leads and more. Also, it is computationally more efficient because it uses assignment by reference. I invested some time in early 2018 learning about this package from DataCamp. It still pays me dividends. I have used it in many of my projects.

RStudio is often misunderstood as complex, but it's a powerful tool for data analytics. R, a programming language tailored for analytics, offers versatility and a vast array of built-in models. Despite alternatives like SAS and Excel, R's open-source nature, extensive package ecosystem, and rapid analysis capabilities make it a top choice. Understanding R's syntax and utilizing functions like read.csv for data importation are key. Additionally, for handling big data, the ff package enables efficient storage and analysis on hard disks, preferably SSDs for faster performance.

?R is a programming language for data analysis and visualization. ?It has many packages for data manipulation, such as dplyr, tidyr, and ggplot2. ?Dplyr lets you manipulate data frames using verbs like select, filter, arrange, etc. ?Tidyr lets you tidy your data into a standard format where each variable is a column and each observation is a row. ?Ggplot2 lets you create plots using the grammar of graphics.

R is a powerful programming language that specializes in data analysis and visualization.Some of the most commonly used ones are dplyr, tidyr, and ggplot2.

Data analysis: Designed specifically for statistical analysis, visualization, and machine learning. Libraries: Rich collection of packages (dplyr, tidyr, ggplot2) for data wrangling, transformation, and plotting. Cons: Steeper learning curve, potential performance limitations for very large datasets.

I will choose sql due to its declarative nature, optimization capabilities, and transaction support. SQL databases enforce data integrity constraints and offer scalability across distributed environments. The mature ecosystem surrounding SQL provides robust tools and community support for handling complex tasks effectively.

SQL would be my first choice for speed and reproducability. Unless complex calculations are required -- and sometimes even if they are -- SQL will be provide a more reusable solution than scripting languages like R / Python

For properly bound data, SQL provides a powerful organizational framework on top of fast access. In fact, for sets that are around 2 GBytes in size, it can live directly in RAM and accelerate many applications.

I would determine data sources, formats, and quality. I would additionally describe data manipulation goals like filtering, aggregating, combining, transforming, or analyzing. Choose SQL Server, MySQL, PostgreSQL, SQLite, or Oracle for the task. I would also examine tool performance, scalability, security, and compatibility. I wrote SQL queries to manipulate data using best practices for syntax, naming, formatting, and commenting. I would test, debug, and optimize searches for performance and correctness. I would document data manipulation, SQL queries, and results. I would share my findings and ask stakeholders for input.

SQL would be the good option to manipulate huge data. We can filter data using WHERE clause , perform operations like aggregation , joins, sorting ,using keywords limit, percent, distinct and clauses like ORDER BY ,PARTITION BY , DML commands all are used to manipulate data efficiently. We can also automate process by creating stored procedures, functions that can run repeatedly and reduce the manual effort .

In such a scenario, Unix Shell commands can be incredibly useful. Tools like awk for text processing, sed for stream editing, grep for pattern searching, and cut for column extraction are powerful for data manipulation. Additionally, sort and uniq can help with sorting data and removing duplicates. These tools, combined with the ability to pipe commands, can solve complex data tasks efficiently.

To this i would add jq, which can solve sooo much. You've got some json response with a bunch of items that you want to investigate or prepare for other process? Earlier i'd probably write some script or just use text editor. But jq combined with piping is much more flexible and convenient.

I normally start with aws/sed/jq in bash and if it is too much for bash to handle the request, I resort back to javascript or python depending on the nature of the job. A one-liner bash command with piping and chaining is usually enough for my data processing needs.

In such scenarios, my first go to tool would be shell commands, especially awk. Even it is limited for more complex problems, it gives you a very good insight in a very short amount of time.

Basic data manipulation: Useful for preprocessing text, CSV, or JSON data using tools like awk, sed, grep. Automation: Can be scripted for repetitive tasks. Cons: Not ideal for complex transformations, can be less user-friendly compared to higher-level languages.

When dealing with complex data manipulation tasks, regular expressions (regex) are a potent tool for pattern matching and text processing. They allow you to search for specific patterns, replace text, and extract information from strings with great precision and efficiency. Regex can be utilized in many programming environments and is supported by many languages, including Python, Java, and JavaScript. Mastering regular expressions can save you time when cleaning and extracting data from unstructured text sources.

RegEx are very helpful as filtering values using string operations are not something easy as it may make exceptions, but RegEx can help us in finding the values with multiple conditions in one go .

Regex are powerful but they aren’t really their own tool. You’re going to use them in a language or in a tool with good support, but you want to pick a language or tool that will support whatever else you have to do. The other thing to watch out for when someone tries to solve a problem, is that sometimes they get hung up trying to make 1 regex that does it, instead of using a few consecutive regex.

When you are dealing with the complex data , regex or regular expressions come in handy. You can easily do it with the help of pattern matching. In the process of pattern matching to validate the data , the unwanted characters are removed , even if you want some specific information you can easily get it .

RegEx is a powerful tool, it's learn once, use everywhere. It allows processing strings with advanced features like finding some pattern. We are processing large dataset, again we have trouble, is data ok? - "^NO.*$" Will return strings starting with "NO" As before, look for correct data by optionally deep query the data preventing us from process data that should be skipped and logged as exception.

As a fan of all things AWS, I would start with something like AWS Databrew to clean and normalize your data. Depending on the size of the data set, Spark is an excellent choice. You can use a familiar language such as python and even throw in a few SQL queries in the form of Spark SQL. To keep the focus on the data, use AWS Glue so there is no infrastructure management. Once you’re happy with your transformations/manipulations, convert the data to a performant format ( Parquet/ORC/Avro ) each have their use case. Finally send it to the downstream location that will use this data.

When the data becomes big , Pandas plays a big role in manipulating and filtering down the values . Data-frame concept making it’s easy to Understand and visualise the columns and rows also gives another insights as well like describing the whole data

Address complex data manipulation by breaking it down into manageable steps. Start by clearly defining the problem. Use first principles to rethink your approach. Explore tools like Python's Pandas ro some out of the box solution for efficiency. Initially, craft a basic solution to address the task, even with potential flaws. Test thoroughly to uncover edge cases and refine your solution to ensure coverage. Continuous testing is key to developing a robust, scalable solution.

For complex data manipulation tasks, JavaScript offers a robust ecosystem with libraries for efficient processing. Leveraging Node.js Workers can handle time-consuming tasks effectively. With a vast community and diverse data types, JavaScript supports functional programming paradigms. Additionally, JavaScript libraries enable visualization, facilitating the creation of compelling charts and dashboards.

Apache Flink is one tool that can be used for big data processing. Jupyter Notebooks Excel sheets can also be used to manipulate datd