Well as per what you have mentioned in description it seems like Hadoop is the best match for you as there number ofpanies is looking for Hadoopers with the knowledge of BI house. Python is programming language and you mostly worked with data analysis. There are also lots of challenging projects in Python and number of reputed organization are looking for it but Hadoop is also on great position and it has great future too as Hadoop has good contribution in Data analysis and data grrows day by day. Hadoop will be best match for you and this will take minimum time to learn and you are already have experience in SQL queries so I believe Hadoop is best for you. Best Luck!
How can I build a simple application in Python to save data into an Excel sheet?
Just build your application like u want then use one of the excel packages availablen XlsxWriter (writes excel files and charts) Xlwt (writing) Xlrd (read from excel files) Openpyxl (read) nIf your application manipulates a lot of data i suggest you to use Pandas. nPandas makes reading and writing excel files very easy but ites with the cost of learning a big it helps!
Can Python write to excel?
XlsxWriter is a Python module that can be used to write numbers formulas and hypers to multiple worksheets in an Excel 27+ XLSX file. It supports features such as formatting and many more including 1%patible Excel XLSX files.
Can you use Python to extract Excel data and export it to an SQL database?
I recently wrote a tutorial on how you can get data from Excel spreadsheets into Python Python Excel Tutorial The Definitive Ge s . Mostmon packages that youll find that are used for these purposes are Pandas OpenPyXL xlrd and xlwings. To write the data to an SQL database you might still use Pandas ( - pandas .19.2 documentation ). I think this is the easiest way. There are also other examples of importing data in an SQL database such as Import Data From Excel Into MySQL Using Python #.WLRS7RLyvdc . Consider checking out Introduction to Databases in Python s if you want to know more about how you can use Python to interact with relational databases.
What are the things necessary to learn in Python to build excellent websites?
Can we use Python to automatically retrieve data from Excel Sheets and transform it into visualisation?
Not sure what you mean by automatic but the Pandas library provides an easy way to access and transform the data in Excel worksheets (see Python Data Analysis Library s ). Once the data has been transformed there are a variety of visualization procedures that can be applied (e.g. matplotlib - see Matplotlib 2.2. s ).
Can someone give me a road map to Excel in Python?
I learnt Python very quickly and this is how I did it. Umang Ahuja's post in Get Set Python board_item board_item_id 363848 I won't brag about my blog and YouTube channel and some cool projects. You can visit and see them if you want to( in the above .). I have also tried to ex everything in detail in the above . Still if you find you need to ask somethingment or message without wasting a second. Have fun with Python )
How do I extract handwritten text to Excel using a Python scripting?
If you want to use handwritten letter recognition you can use the tesseract OCR engine. There are several modules for optical character recognition like pytesseract tesserwrap and pyocr. Excel data can be interacted with using the pandas module. You can use this module to load and save excel files and to edit them.
Should I learn Python to help with Excel or move to SQL and use Tableau or other analytical tool?
This is a tricky question to address. Mostly because it poses some artificial either distinctions. Still here off I must point out that SQL is NOT a database or any other kind of storage mechanism; it a language for manipulating data. Using SQL in the form of this question suggests that this distinction isn being made. That aside Ill assume that the question reduces to something like what are good uses of the four tools Excel Python Tableau and SQL for data analysis? Ill limit my response to their use for data analysis leaving the production of high-zoot dashboards info-graphics and other design-centricplexposite visuals for another time. Of them Tableau is by far the superior tool for basic data analysis particularly for visual exploration and aggregate quantitative analysis. This makes it the best choice for the basic process of understanding data that forms the great bulk of data-analytical activities. Excel is technically a rather poor tool for data storage manipulation and analysis. It has the great virtue of being widely familiar and effectively free so it a good viable choice in those environments where using one of the others is not available or well supported e.g. Tableau is pricey on an initial purchase basis and not cheap on an annual renewal basis. SQL was created to implement relational set-based data querying. As such it extremely well suited for these purposes. It main drawback is that it designed to support technical data manipulations and is therefore alien to normal human mental constructs. Or put another way SQL is the way machines act not the way humans think so it a vary poor choice for non-technical people much less good than Tableau and even less good than Excel for data analysis. I include technical and non-technical people in this assessment - many technical people could really benefit by adopting Tableau (or Excel or another analysis-centric tool) for their data analytical needs. Python is the best of the four at covering the full spectrum of data-analytical needs. It not nearly as good as Tableau at exploratory visual analysis; it far superior to Tableau in it support for data transformation and deep analysis (Tableau wasn designed for this and is very thin here). To sum up there isn any one tool that covers the data-analytical space well enough to be the only tool one needs. Ideally one should bepetent andfortable with as wide range of tools as possible but this isn a realistic rmendation as gaining mastery with any tool takes a good investment of time energy and effort. From a practical standpoint adopting Python and Tableau works well. Tableau provides the up-front data analytical ease of use flexibility and power to understand data well enough to answer almost all of the first-generation questions. Python provides the deep analytical capabilities necessary to address the questions Tableau can approach. Used together they offer almost everything most analysts need. And they work well in tandem; analysis with Tableau can identify correlations patterns anomalies and other possibilities that can be deeply investigated with Python. Python can conduct deep analysis the results of which can be visualized with Tableau.