ggplot:based on R’s ggplot2, uses Grammar of Graphics.Seaborn:high-level interface, great default styles.Pandas Visualization:easy to use interface, built on Matplotlib.Matplotlib:low level, provides lots of freedom.To get a little overview here are a few popular plotting libraries: No matter if you want to create interactive, live or highly customized plots python has an excellent library for you. Python offers multiple great graphing libraries that come packed with lots of different features. For more information, go to Weibull distribution.Introduction to Data Visualization in Pythonĭata visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed. The Weibull distribution is also used to model skewed process data in capability analysis. For example, the distribution is frequently used with reliability analyses to model time-to-failure data. Weibull The Weibull distribution is a versatile distribution that can be used to model a wide range of applications in engineering, medical research, quality control, finance, and climatology. For more information, go to Exponential distribution. Independent events are assumed to occur at a constant rate. Exponential Use the exponential distribution to model the time between events in a continuous Poisson process. For more information, go to Lognormal distribution. The lognormal distribution is used for reliability analysis and in financial applications, such as modeling stock behavior. Use the lognormal distribution when random variables are greater than 0. Lognormal A random variable follows the lognormal distribution if the logarithm of the random variable is normally distributed. For more information, go to Normal distribution. Many statistical analyses assume that the data come from approximately normally distributed populations. Normal The normal distribution is the most common statistical distribution because approximate normality occurs naturally in many physical, biological, and social measurement situations. To fit a lognormal distribution, an exponential distribution, or a Weibull distribution, all data values must be greater than 0. The height of the final bar is always equal to 100%. The height of each bar is equal to the percentage of the sample observations that fall within its bin and all previous bins. Cumulative Percent The bar heights accumulate from left to right.
The height of the final bar is always equal to the total number of observations in the sample. The height of each bar is equal to the number of observations that fall within the bin and all previous bins. Cumulative Frequency The bar heights accumulate from left to right. Density The area of each bar represents the proportion of the sample observations that fall within the bin (proportion = bar area = bin width × bar height).
A percent scale can be useful when comparing samples of different sizes. A histogram with a percentage scale is sometimes called a relative frequency histogram. Percent The height of each bar represents the percentage of the sample observations that fall within the bin. Frequency The height of each bar represents the number of observations that fall within the bin.
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