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and you can see that b, as visible from the closure's scope, retains the value it had; the changed binding of b inside the inner function did not propagate out. The way around this is to use a nonlocal b statement in bar. In Python 2 (which lacks nonlocal), the usual workaround is to use a mutable value and change that value, not the binding. E ...
A bar chart may be used to show the comparison across the sales persons. Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.
Bar graphs can also be used for more complex comparisons of data with grouped (or "clustered") bar charts, and stacked bar charts. [5] In grouped (clustered) bar charts, for each categorical group there are two or more bars color-coded to represent a particular grouping. For example, a business owner with two stores might make a grouped bar ...
Chart visualization is an important aspect when creating dashboards, diagrams in particular. When you have complex data it can be difficult to come to conclusions from that material and being able to have different visual elements within the dashboard can be helpful in giving a larger overview of the material.
Candlestick charts serve as a cornerstone of technical analysis. For example, when the bar is white and high relative to other time periods, it means buyers are very bullish. The opposite is true when there is a black bar. A candlestick pattern is a particular sequence of candlesticks on a candlestick chart, which is mainly used to identify trends.
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Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. A time series is very frequently plotted via a run chart (which is a temporal line chart).
The radar chart is a chart and/or plot that consists of a sequence of equi-angular spokes, called radii, with each spoke representing one of the variables. The data length of a spoke is proportional to the magnitude of the variable for the data point relative to the maximum magnitude of the variable across all data points.