seaborn barplot in Python Tutorial with example

Seaborn barplot in Python Tutorial : The bar plot is one of most comman type of plot and show relation between numerical and categorical variable. Mistake while using bar plot is to represent the average value of each group. Doing the boxplot or violineplot you should show number of observation per group. While creating barplot the survivors of titanic crash is based on category. The seaborn will support many barplots and combine seaborn with the matplotlib to several plot. The data sets are included with the seaborn but this is only a demo. The palette parameter will define the color that are used currently ‘hls’ by any palette. A bar plot will represent an estimate of central tendency for a numeric variable with the height of each rectangle and will provide some indication of the uncertainty around estimate using error bars.
The Bar plot will  include 0 the quantitative axis range, and are of good choice when 0 is a meaningful value for the quantitative variable and you want to make comparisons against it.
The datasets where 0 is not a meaningful value a point plot will allow to focus on differences between levels of one or more categorical variables. The bar plot show only the mean value but in many cases it may be more informative to show the distribution of values at each level of the categorical variables. The Vectors of data are represented as numpy arrays or panda’s objects passed directly to the x, y, and hue parameters. It is possible to use Python objects but panda’s objects are used because the associated names will be used to annotate the axes.  Categorical types for the grouping variables are used to control the order of plot elements.

Parameters:-

x, y, hue : names of variables in data or vector data, optional Inputs for plotting long-form data. data : Dataset for plotting. If x and y are absent this is interpreted as wide-form. Otherwise it is expected to be long-form. Order to plot the categorical levels in otherwise the levels are inferred from the data objects. estimator : callable that maps vector -> scalar, optional Statistical function to estimate within each categorical bin. ci : Size of confidence intervals to draw around estimated values. If “sd”, skip bootstrapping and draw the standard deviation of the observations.  If no bootstrapping will be performed, and error bars will not be drawn. n_boot : Number of bootstrap iterations to use when computing confidence intervals. units : name of variable in data or vector data, optional Identifier of sampling units, which will be used to perform a multilevel bootstrap and account for repeated measures design. orient : “v” | “h”, optional 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 : Color for all of the elements, or seed for a gradient palette. palette : Colors to use for the different levels of the hue variable. saturation : 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. errcolor : Color for the lines that represent the confidence interval. errwidth : Thickness of error bar lines (and caps). capsize : float, optional Width of the “caps” on error bars. dodge : When hue nesting is used whether elements should be shifted along the categorical axis. ax : Axes object to draw the plot onto otherwise uses the current Axes. kwargs : Other keyword arguments are passed through to plt.bar at draw time.

Returns:

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

Seaborn barplot Example 1:-

Import numpyb as np
Import matplotlib pyplot as plt
#make a fake dataset:
Height=[3,12,5,18,45]
Bars=(‘A’,’B’,’C’,’D’,’E’)
y_pos=np.arange(len(bars))
#create bars
Plt.bar(y_pos,bars)
#show graphic
Plt.show()
Output:-

Example2:-
Now draw set of vertical bar plot grouped by a variable,
Import seaborn as sns
Sns.set(style=”whitegrid”)
tips=sns.load_dataset(“tips”)
ax=sns.barplot(x=”day”,y=”total_bill”,data=tips)

Output:-

Below is the set of vertical bard with nested grouping by the two variables.

Example 3:-
             ax=sns.barplot(x=”day”,y=”total_bill”,hue=”sex”,data=tips)
Output:-

Example 4:-
Draw the graph using the horizontal bars.
ax=sns.barplot(x=”tip”,y=”day”,data=tips)

Output:-

Example 5:-
             The control bar order by passing in an explicit order.
ax=sns.barplot(x=”time”,y=”tip”,data=tips,orde=[“Dinner”,”Lunch”])

Output:-