Seaborn

PairGrid allows us to draw a grid of subplots using the same plot type to visualize data. Unlike FacetGrid, it uses different pair of variable for each subplot. It forms…

A useful approach to explore medium-dimensional data, is by drawing multiple instances of the same plot on different subsets of your dataset. This technique is commonly called as “lattice”, or…

Most of the times, we use datasets that contain multiple quantitative variables, and the goal of an analysis is often to relate those variables to each other. This can be…

Categorical data can we visualized using two plots, you can either use the functions pointplot(), or the higher-level function factorplot(). Factorplot Factorplot draws a categorical plot on a FacetGrid. Using ‘kind’ parameter…

It is always preferable to use ‘long-from’ or ‘tidy’ datasets. But at times when we are left with no option rather than to use a ‘wide-form’ dataset, same functions can…

In most of the situations, we deal with estimations of the whole distribution of the data. But when it comes to central tendency estimation, we need a specific way to…

In categorical scatter plots which we dealt in the previous chapter, the approach becomes limited in the information it can provide about the distribution of values within each category. Now,…

In our previous chapters we learnt about scatter plots, hexbin plots and kde plots which are used to analyze the continuous variables under study. These plots are not suitable when…

Datasets under real-time study contain many variables. In such cases, the relation between each and every variable should be analyzed. Plotting Bivariate Distribution for (n,2) combinations will be a very…

Kernel Density Estimation (KDE) is a way to estimate the probability density function of a continuous random variable. It is used for non-parametric analysis. Setting the hist flag to False in distplot will yield…