Plotting graph using Seaborn: Python
Seaborn is a Python data visualization library offers many advanced data visualization capabilities. It comes up with a high-level interface for drawing attractive and explanatory statistical graphics . Its plotting functions operate on Pandas DataFrames and numPy arrays containing whole datasets and internally carry out the necessary semantic mapping and statistical aggregation to produce explanatory plots. The Seaborn library is built on top of Matplotlib and integrates closely with pandas data structures.
distplot - A Distplot or distribution plot shows the statistical distribution of data.
Distplot Without the Histogram
The jointplot is used to output the mutual distribution of each column.
The pairplot is a type of distribution plot that basically plots a joint plot for all the possible combination of numeric and Boolean columns in your data.
The rugplot is used to draw small bars along x-axis for each point in the dataset.
A barplot is basically used to aggregate the categorical data according to some methods and by default it's the mean.
The count plot is similar to the bar plot, however it displays the count of the categories in a specific column.
A box plot helps to maintain the distribution of quantitative data in such a way that it facilitates the comparisons between variables or across levels of a categorical variable.
Violin plots show the same summary statistics as box plots , but they also include Kernel Density Estimations that represent the shape/distribution of the data.
A strip plot is a scatter plot where one of the variables is categorical. They can be combined with other plots to provide additional information.
The swarm plot is a combination of the strip and the violin plots.