Data Frames - Part 1

  • Pandas package contains useful functions to work with dataframes.
  • iloc property is used to index and slice a dataframe.
  • describe function is used to get a summary of basic data features.
  • The simplest way of visualisation is to use Pandas functionality.
  • Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

Data Frames - Part 2

  • Quantities based on data from two variables are referred as bivariate measures.
  • Bivariate properties can be studied using matplotlib and numpy.
  • Multivariate data analysis helps to find out relationships between recorded variables.
  • Functions corr and corrcoef are used to calculate the \(PCC\).
  • A correlation matrix is visualised as a heatmap.

Image Handling

  • imread function can interpret many different image formats.
  • Masking isolates pixels whose intensity value is below a certain threshold.
  • The colour images are comprised of three channels (corresponding to red, green and blue intensities).
  • Python Image Library (PIL) helps to set high pixel limit for larger images.

Time Series

  • plot_series is a Python function created to display multiple timeseries plots.
  • Data filtering is applied to take out specific and relevant components.
  • The Fourier spectrum decomposes the time series into a sum of sine waves.
  • Cross-correlation matrix is used for multivariate analysis.