By Robert Dempsey
The quantity of information produced by way of companies and units goes nowhere yet up. during this situation, the foremost benefit of Python is that it's a general-purpose language and provides you many of suppleness in information buildings. Python is a superb software for extra really expert research initiatives, and is powered with similar libraries to approach information streams, to imagine datasets, and to hold out clinical calculations. utilizing Python for company intelligence (BI) may also help remedy difficult difficulties in a single go.
Rather than spending every day scouring net boards for “how-to” details, right here you'll locate greater than 60 recipes that take you thru the total strategy of developing actionable intelligence out of your uncooked info, it doesn't matter what form or shape it's in. in the first half-hour of commencing this publication, you'll find out how to use the newest in Python and NoSQL databases to glean insights from information simply ready to be exploited.
We'll commence with a quick-fire advent to Python for BI and express you what difficulties Python solves. From there, we stream directly to operating with a predefined facts set to extract info as according to enterprise necessities, utilizing the Pandas library and MongoDB as our garage engine.
Next, we'll research info and practice adjustments for BI with Python. via this, you'll assemble insightful facts to help you make knowledgeable judgements on your enterprise. the ultimate a part of the booklet will express you crucial activity of BI―visualizing info by means of development wonderful dashboards utilizing Matplotlib, PyTables, and iPython pc.
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Extra info for Python Business Intelligence Cookbook
Next, we need to consider whether, by allowing zero values for dates, we are also making our x axis substantially longer (my count query yielded 1211 rows, but there are a total of 1822 days in the range specified, which is 1998-2002). Maybe showing zero count days might not work; if the graph is so crowded, we cannot see the gaps anyway. To compare, we can quickly run the same data into Google Spreadsheets (you can do this in Excel or D3 too), but this time, we will only select our count column to build the graph, thereby forcing Google Spreadsheets to not show dates on the x axis.
Yet, there are still some options that can differentiate one delimited format from another. The first differentiator is how each instance of the data (each row) is separated. Usually at the end of a line, a new line, or a carriage return, or both are used depending on the operating environment in use during the creation of the file. Seeing invisible characters In the preceding example, the new line or carriage return is invisible. How do you see the invisible characters? We will read the same file in Text Wrangler on a Mac (similar full-featured editors such as Notepad++ are available for Windows), where we can use the Show invisibles option (located by navigating to View | Text Display).
Type conversion at the SQL level Here, we will walk through a few common cases when SQL can be used to manipulate data types. Example one – parsing MySQL date into a formatted string For this example, we will return to the Enron e-mail dataset we used in Chapter 1, Why Do You Need Clean Data?. As with the previous example, we are going to look in the message table, where we have been using the date column, which is stored as a datetime MySQL data type. Suppose we want to print a full date with spelled-out months (as opposed to numbers) and even the day of the week and time of day.
Python Business Intelligence Cookbook by Robert Dempsey