🔎 Learn to create beautiful charts in Python using the Seaborn library: kdnuggets.com/2022/04/data-v…
#Python #DataVisualization #Seaborn
#Python #DataVisualization #Seaborn
Histogram: The chart above is a simple histogram of the “total_bill” variable. At a glance, we can see that a single meal at this restaurant generally costs around $10 to $25, and there is a positive skew due to some diners ordering more expensive food with prices above $50.
Count Plot: Now, let’s look at the number of male and female diners at the restaurant, to determine the gender that visits the eatery more often.
Bar Chart: Now, let’s try visualizing the relationship between two variables — “sex” and “total_bill” to see if there is a difference in the amount spent by men and women when dining out.
Box plots are a useful way to understand the spread of a variable, and provides us with a five number summary of the data point — minimum, first quartile, median, third quartile, maximum.
A regression plot is the best way to visualize the relationship between two numeric variables. Using Seaborn’s lmplot() function, a regression line is created to demonstrate the correlation between X and Y.
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