In this entry, we will do a quick review of the possibilities available for plotting and charting in Python. It is not a complete review, but just an introduction to get started. Data visualization plays a key role in the processes of data science; it is ultimately an interface between the data and the data scientist.
In this entry, we are going to get to know one of the most useful Python tools ever created: Jupyter (formerly known as the IPython Notebook). Seriously, this thing is much more powerful than it sounds when first introduced: it is a Python interpreter that works with independent code and markdown cells in a browser.
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.