Python is a popular and versatile programming language for data analysis, but it can also be slow and memory-intensive if not optimized properly. In this article, you will learn some of the best practices to improve the performance and efficiency of your Python code for data analysis tasks.
Python has a rich set of built-in functions and modules that can help you perform common operations faster and easier. For example, you can use the math module for mathematical calculations, the itertools module for iteration tools, the collections module for data structures, and the functools module for functional programming. These built-in functions and modules are optimized for speed and memory usage, and can save you from writing your own custom code.
Using built in functions can be a good way to speed up the work but its very important to know the usage of that built function. Mist of the time we write the scripts but for the same task there are simple functions which can do the job in a single call. It’s always a good practice to explore new things everyday.
Minimize Loops, Loops in Python can be slow. Whenever possible, try to use built-in functions and libraries that operate on entire arrays or data frames, avoiding the need for explicit loops.
While considering built-in functions and modules vs libraries, there are two things to consider: 1. Will the library save many steps to perform? For example, if you have a table with 20k rows of data and want to remove characters from data in one or more columns in string format, using pandas would be more efficient instead of iterating over each row. 2. Is the library supported well and widely used? Using popular libraries like NumPy and Pandas is a good idea, while using a library that is not widely used is risky and should be avoided. Additional tip: Import only specific functions, variables, classes, etc., to improve efficiency. For example, import only ceil from math instead of the whole math module if only that function will be used.
The choice of data structures and algorithms can have a significant impact on the speed and memory consumption of your Python code. For data analysis, you should use the most appropriate data structures and algorithms for your problem domain and data type. For example, you can use lists for sequential data, dictionaries for key-value pairs, sets for unique elements, and tuples for immutable data. You can also use numpy arrays and pandas dataframes for numerical and tabular data, as they offer faster and more convenient operations than native Python data structures. Additionally, you should use efficient algorithms that minimize the time and space complexity of your code, such as sorting, searching, filtering, and aggregating.
To format the data, mostly we use numpy for the images data, pandas for the tabular data. For the text data we also use numpy after extracting the features as well.
Vectorization is a technique that applies an operation to multiple elements of a data structure at once, rather than looping over each element individually. Vectorization can improve the performance of your Python code by reducing the number of loops, function calls, and intermediate variables. You can vectorize your operations using numpy arrays and pandas dataframes, which support vectorized arithmetic, logical, and statistical operations. You can also use numpy vectorize and pandas apply functions to apply custom functions to arrays and dataframes in a vectorized manner.
For iterative operations on large data, a vectorized approach can boost speed by orders of magnitude. From my experience, leveraging vectorization is often the difference between code that takes miliseconds and code that takes seconds (or longer) to run. As a best practice, always consider vectorizing operations when working with sizable datasets. However, just because you can vectorize, doesn’t mean you should. Always weigh the clarity of your code against performance gains.
Profiling and benchmarking are tools that help you measure and analyze the performance of your Python code. Profiling tools can help you identify the bottlenecks and hotspots of your code, such as the functions that take the most time or consume the most memory. Benchmarking tools can help you compare the execution time and memory usage of different versions of your code, or different implementations of the same functionality. Some of the common profiling and benchmarking tools for Python are cProfile, line_profiler, memory_profiler, timeit, and pytest-benchmark.
When developing Python code, it is beneficial to incorporate profiling and benchmarking tools like memory_profiler, tracemalloc, and timeit from the beginning. These tools provide valuable insights into the performance characteristics of your code. These tools help to know which code is excessively using memory or slowing down the data processing, avoiding potential performance and memory-related issues in later stages of development. This approach encourages a performance-aware development approach, leading to more efficient and optimized Python code.
In my experience, it is often a good idea to write code in the dumbest, simplest way. Developers/Data Scientists are bad at predicting where the bottlenecks will be, so developing smart code in the first pass often leads to overly complicated logic to speed things up, where there would have been no bottleneck in the first place. After you've written your simple code, and you need to speed it up or lower memory usage, the first step of the process is profiling. It will help you quickly identify the bottlenecks in your code, so you can focus your efforts where it really matters. The code you get with this approach is cleaner too - it is only complicated in the areas where it needs to be.
Parallel and concurrent programming are techniques that allow you to run multiple tasks or processes simultaneously, rather than sequentially. Parallel programming can help you utilize multiple cores or processors of your machine, while concurrent programming can help you handle multiple requests or events asynchronously. You can use parallel and concurrent programming to speed up your Python code for data analysis, especially when you have to deal with large or complex data sets, or perform intensive computations. Some of the common parallel and concurrent programming tools for Python are multiprocessing, threading, asyncio, and concurrent.futures.
In the code writing, its very important to avoid creating multiple variables to assign the data. I mean we first load the data and save in the variable then we convert the format and save to another variable then we extract the features and save into another variable. Instead of doing all, we should use the same name of variable for the same kind of tasks so previous values will be updated and memory will not be allocated again and again.
If you're using programming tools such as multiprocessing, or threading for data analysis, you're probably doing it wrong. Managing threads or processes by hand is a massive pain in the neck. You can quickly run into race conditions and other nasty bugs. There are two strategies that result in more maintainable and expandable code: 1. Use a library like sklearn/Dask for parallel programming or Sparks/Kafka for concurrent programming. These libraries handle the hard stuff in the background in python. 2. Use GNU parallel (if you're on Linux/MacOS). You write dumb single threaded jobs in python, and chain them with GNU parallel. This is great for one-off data analysis.
In the realm of data science, while parallel programming can be a great way for speeding up computations, it often comes at a hidden cost: developer time. Crafting efficient parallel code can be intricate and time-consuming. Debugging, ensuring thread-safety, and managing inter-process communication can often eclipse the time saved from faster execution. If you find yourself consistently needing parallel processing on massive datasets, it might be more fruitful to pivot towards dedicated distributed computing frameworks like Apache Spark. Such platforms are designed from the ground up for scalability and parallelism, saving you both time and potential headaches.
Code optimization is the process of modifying your code to make it run faster or use less memory, without changing its functionality or output. Code optimization can involve various techniques, such as removing unnecessary code, simplifying expressions, using local variables, caching results, using generators, and compiling code. You can use code optimization techniques to improve the performance and efficiency of your Python code for data analysis, especially when you have to optimize critical or frequently used parts of your code. Some of the common code optimization tools for Python are pyminifier, numba, cython, and pyinstaller.
Before diving into intricate code optimization, always prioritize algorithm analysis. The underlying algorithm's efficiency can often make a more pronounced difference than switching to cython or even rewriting the application into C++. By choosing the most appropriate algorithm, many performance issues can be circumvented from the outset. While tools like Cython offer tremendous optimization capabilities, they introduce an added layer of complexity. It's essential to discern whether that leap is necessary. Often, a well-chosen algorithm, coupled with Python's native capabilities, suffices for robust data science tasks. Optimize your thinking first, and the code will follow.
When working in python for data analysis the most popular library is pandas. Here are some of the ways to optimize memory usage in pandas : 1. Select only required columns while reading data using `usecols`. 2. Shrink numerical columns with smaller dtypes 3. Shrink categorical data using Categorical dtypes 4.If you have a column with lots of empty values, usually represented as NaNs, you can save memory by using a sparse column representation. 5. In case of very large dataset try reading in chunks