Data Handling
-
Pandas package contains useful functions to work with
DataFrames.
- The iloc property is used to index and slice a
DataFrame.
-
describe function is used to obtain a statistical summary
of basic data features.
- The simplest method for data visualisation, is to use Pandas’
in-built functionality.
-
Matplotlib is a comprehensive library for creating
static, animated, and interactive visualizations, in Python.
- Quantities based on data from two variables are referred to as
bivariate measures.
- Bivariate properties can be studied and visualised using
matplotlib and NumPy.
- Multivariate data analyses can help to uncover relationships between
recorded variables.
- The functions
corr and corrcoef can be
used to calculate the \(PCC\).
- A correlation matrix can be visualised as a heatmap.
- The
imread function can be used to read in and
interpret multiple image formats.
- Masking isolates pixels whose intensity value is below a certain
threshold.
- Colour images typically comprise three channels (corresponding to
red, green and blue intensities).
- Python Image Library (PIL) helps to set and raise default pixel
limits for reading in and handling larger images.
-
plot_series is a Python function we defined to display
multiple time series plots.
- Data filtering is applied to remove specific and irrelevant
components.
- The Fourier spectrum decomposes the time series into a sum of sine
waves.
- Cross-correlation matrices are used for multivariate analysis.
