![]() If you do the above correctly, the Emulator app will be successfully installed. Now click Next to accept the license agreement.įollow the on screen directives in order to install the application properly. Once you have found it, click it to install the application or exe on your PC or Mac computer. Now that you have downloaded the emulator of your choice, go to the Downloads folder on your computer to locate the emulator or Bluestacks application. Step 2: Install the emulator on your PC or Mac You can download the Bluestacks Pc or Mac software Here >. Most of the tutorials on the web recommends the Bluestacks app and I might be tempted to recommend it too, because you are more likely to easily find solutions online if you have trouble using the Bluestacks application on your computer. If you want to use the application on your computer, first visit the Mac store or Windows AppStore and search for either the Bluestacks app or the Nox App >. Import pandas as pd import numpy as np from 1: Download an Android emulator for PC and Mac When using the xlwings PRO reports package, your code simplifies to the following: You can get a free trial for xlwings PRO here. To see how Frames work, have a look at the documentation. Frames for dynamic tables: Frames are vertical containers that dynamically align and style tables that have a variable number of rows.They act as placeholders that will be replaced by the values of the variables. to_excel ( writer, sheet_name = 'Sheet1', startrow = 2 ) # Get book and sheet objects for futher manipulation belowīook = writer. ExcelWriter ( 'myreport.xlsx', engine = 'xlsxwriter' ) df. randn ( 5, 4 ), columns =, index = ) # Dump Pandas DataFrame to Excel sheet Import pandas as pd import numpy as np # Sample DataFrameĭf = pd. In that case you should be able to easily adopt this snippet by replacing engine='xlsxwriter' with engine='openpyxl' and changing the book/sheet syntax so it works with OpenPyXL: If you want to do something slightly more sophisticated than just dumping a DataFrame into an Excel spreadsheet, I found that Pandas and XlsxWriter is the easiest combination, but others may prefer OpenPyXL. Accordingly, Pandas will be used in all sections of this blog post, but we’ll start by leveraging the built-in capabilities that Pandas offers for reports in Excel and HTML format. Once you have the raw data in a DataFrame, it only requires a few lines of code to clean the data and slice & dice it into a digestible form for reporting. It’s incredibly easy to create Pandas DataFrames with data from databases, Excel and csv files or json responses from a web API. I am probably not exaggerating when I claim that almost all reporting in Python starts with Pandas. It’s very fast and powerful but comes with a steep learning curve. ReportLab creates direct PDF files without going through HTML or Excel first. It also offers a hosted solution so end users can change the input parameters that are used to create these reports. Like Pandas + HTML, it requires good HTML + CSS skills to make it look the way you want.ĭatapane allows you to create HTML reports with interactive elements. If formatted properly, it can be used as a source for PDFs, too. The Excel file can be exported to PDF.ĭash allows you to easily spin up a great looking web dashboard that is interactive without having to write any JavaScript code. It requires, however, an installation of Excel so it’s a good option when the report can be generated on a desktop, e.g. Xlwings allows the use of an Excel template so the formatting can be done by users without coding skills. it’s an ideal candidate for a “download to Excel” button in a web app. It can be run on a server where Excel is not installed, i.e. This is a great option if the report has to be in Excel. The HTML report can also be turned into a PDF for printing. You can generate beautiful reports in the form of static web pages if you know your way around HTML + CSS. Table of Contentsīefore we begin, here is a high level comparison of the libraries presented in this post: Library After reading this blog post, you should be able to pick the right library for your next reporting project according to your needs and skill set. There is a wealth of techniques and libraries available and we’re going to introduce five popular options here. Python is a popular tool for all kind of automation needs and therefore a great candidate for your reporting tasks.
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