Countplot

A countplot is a plot between a categorical and a continuous variable. The continuous variable in this case being the number of times the categorical is present or simply the frequency. In a sense, count plot can be said to be closely linked to a histogram or a bar graph.

Syntax : seaborn.countplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, dodge=True, ax=None, **kwargs)
Parameters : This method is accepting the following parameters that are described below: 
 

• x, y: This parameter take names of variables in data or vector data, optional, Inputs for plotting long-form data.
• hue : (optional) This parameter take column name for colour encoding.
• data : (optional) This parameter take DataFrame, array, or list of arrays, Dataset for plotting. If x and y are absent, this is interpreted as wide-form. Otherwise it is expected to be long-form.
• order, hue_order : (optional) This parameter take lists of strings. Order to plot the categorical levels in, otherwise the levels are inferred from the data objects.
• orient : (optional)This parameter take “v” | “h”, Orientation of the plot (vertical or horizontal). This is usually inferred from the dtype of the input variables but can be used to specify when the “categorical” variable is a numeric or when plotting wide-form data.
• color : (optional) This parameter take matplotlib color, Color for all of the elements, or seed for a gradient palette.
• palette : (optional) This parameter take palette name, list, or dict, Colors to use for the different levels of the hue variable. Should be something that can be interpreted by color_palette(), or a dictionary mapping hue levels to matplotlib colors.
• saturation : (optional) This parameter take float value, Proportion of the original saturation to draw colors at. Large patches often look better with slightly desaturated colors, but set this to 1 if you want the plot colors to perfectly match the input color spec.
• dodge : (optional) This parameter take bool value, When hue nesting is used, whether elements should be shifted along the categorical axis.
• ax : (optional) This parameter take matplotlib Axes, Axes object to draw the plot onto, otherwise uses the current Axes.
• kwargs : This parameter take key, value mappings, Other keyword arguments are passed through to matplotlib.axes.Axes.bar().

Returns: Returns the Axes object with the plot drawn onto it.

It simply shows the number of occurrences of an item based on a certain type of category.In python, we can create a count plot using the seaborn library. Seaborn is a module in Python that is built on top of matplotlib and used for visually appealing statistical plots.

It may sometimes seem easier to go through a set of data points and build insights from it but usually this process may not yield good results. There could be a lot of things left undiscovered as a result of this process. Additionally, most of the data sets used in real life are too big to do any analysis manually. This is essentially where data visualization steps in.

Data visualization is an easier way of presenting the data, however complex it is, to analyze trends and relationships amongst variables with the help of pictorial representation.

The following are the advantages of Data Visualization

• Easier representation of compels data
• Highlights good and bad performing areas
• Explores relationship between data points
• Identifies data patterns even for larger data points

While building visualization, it is always a good practice to keep some below mentioned points in mind

• Ensure appropriate usage of shapes, colors, and size while building visualization
• Plots/graphs using a co-ordinate system are more pronounced
• Knowledge of suitable plot with respect to the data types brings more clarity to the information
• Usage of labels, titles, legends and pointers passes seamless information the wider audience