In this new entry, we will see the plotting capabilities of Pandas and how to mix it with Matplotlib.
Decorating graphics with MatplotlibStandard
In the last entry, we saw a brief introduction to Matplotlib by drawing three basic graphic types (a line plot, a bar chart and pie chart). But the resulting graphics were arguably unattractive.
Introduction to MatplotlibStandard
In the previous entry, we introduced the libraries available for graphing in Python. In this entry, we will have a very basic look on the first proposed library, Matplotlib. We will create three types of graphics: a line plot, a bar chart and a pie chart.
Drawing graphs in PythonStandard
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.
Introducing Jupyter (IPython Notebook)Standard
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.
Analyzing trends in data with PandasStandard
A very important aspect in data given in time series (such as the dataset used in the time series correlation entry) are trends. Trends indicate a slow change in the behavior of a variable in time, in its average over a long period.
Correlating time series with PandasStandard
In this entry, we will see a practical application of the Pandas library. We will use a DataFrame where we will load the contents of a CSV file containing data of measurements on a flotation cell.
Loading a DataFrame from an SQL databaseStandard
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.
Reading and writing CSV files with PandasStandard
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.