In this new entry, we will see the plotting capabilities of Pandas and how to mix it with Matplotlib.
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.
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.
Introduction to PandasStandard
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.