In a prior entry we described how to load data from a CSV file. In this entry, we will do a quick introduction on how to load the contents of an SQL table into a DataFrame.
CSV (Comma Separated Values) files are a very simple and common format for data sharing. CSV files are simple (albeit sometimes large) text files that contain tables. Each line is a row, and within each row, each value is assigned a column by a separator.
In the previous entry, I introduced Pandas Series. I also compared it with the column of an Excel workbook. Well, following that analogy, DataFrame is the full Excel workbook, where each column is … you guessed it; a Series.
In this entry, I will quickly introduce the Series. It is the most simple structure provided by Pandas, and it can be thought of as a column in an excel spreadsheet.
In this entry, I will do a brief introduction to Pandas, a library that I have been using the last year for my data analysis needs.
Pandas brings the simplicity and elegance of Python to data analysis. It is part of the Scipy collection of libraries, that include other libraries for scientific computation.
Python is a great programming language for several diverse purposes. One of the things that make it so good is the number and diversity of libraries available. This is also a curse sometimes; because the installation of these libraries over a vast number of scenarios (different OSs, devices, etc) may give place to unexpected, headache-giving problems. To solve this several solutions have been adopted; but in order to use them, it is recommendable to plan before installing. Here, I will review the main options for installing Python in your system, reviewing the advantages of each one.