Python: Why Is It Important for Data Analysis?

What Makes Python So Important for Data Analysis?

Every day, more than 36,000 weather forecasts covering 800 distinct areas and towns are released in the U.S. When a sudden rainstorm ruins a picnic or outdoor sporting event, some people may complain about the forecasts' imprecision, but few people consider how frequently they are right. The staff at Forecastwatch.com, a pioneer in climate intelligence and business-critical weather, did exactly that. They compiled all 36,000 forecasts, entered them into a database, and then contrasted them with the actual weather that prevailed on that specific day in that particular location. Then, forecasters across the nation use these findings to enhance their forecast models for the future cycle.

How Python is used at every step of data analysis

1. Numpy and Pandas: Imagine staring at a long Excel sheet with hundreds of rows and columns, from which you want to derive useful insights by searching for a specific type of data in each row and column and performing certain operations. Since such tasks are extremely time-consuming and cumbersome, Python can come to your aid. With Python libraries such as Pandas and Numpy, you can use parallel processing for such high-computational tasks, which makes the job faster and easier.

2. BeautifulSoup and Scrapy: Using BeautifulSoup, you can parse and extract data out of XML and HTML files. On the other hand, Scrapy – which was originally designed for web scraping, can also be used as a general-purpose web crawler or to mine data using APIs. Since the necessary data isn’t always readily available, you can use these Python libraries to extract data from the internet, which would help in data analysis.

3. Seaborn and matplotlib: Instead of seeing a lot of data jumbled on a screen, it’s much easier to visualize the data in the form of pie-charts, bar graphs, histograms, etc. Such pictographic representation or visualization of the data helps in deriving useful insights quickly and easily. Here again, Python libraries can come to the rescue. Using Seaborn (which is a matplotlib-based Python data visualization library) that provides you with a high-level interface for drawing informative and attractive statistical graphics, you can easily visualize data and draw useful insights. Apart from being equipped with beautiful default styles, the statistical plotting library of Seaborn is also designed to work extremely well with the Pandas dataframe objects.

In addition, using Python would mean having scikit-learn (a machine learning library), which would help in complex computational tasks involving probability, calculus, and matrix operations over thousands of columns and rows.  For data analysis involving images, OpenCV (which is an image and video processing library used with Python) can help.

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