Refined last para to tone down NumPy performance boost effect as suggested by Ralf - ref numpy#43 numpy#64

Author: shaloo

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                        NumPy is the key data transformation building block for the burgeoning <a href="https://pyviz.org/overviews/index.html">Python visualization landscape</a> comprising of <a href="https://matplotlib.org">Matplotlib</a>, <a href="https://seaborn.pydata.org">Seaborn</a>, <a href="https://plot.ly">Plotly</a>, <a href="https://altair-viz.github.io">Altair</a>, <a href="https://docs.bokeh.org/en/latest/">Bokeh</a>, <a href="https://github.com/yhat/ggpy">ggplot</a>, <a href="http://vispy.org">Vispy</a> and <a href="https://github.com/napari/napari">Napari</a>, to name a few. 
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-                       Implemented by low-level libraries written in C and Fortran, NumPy performs parallel operations on large arrays all at once, accelerating data-processing and visualization of large quantities of data and thus supercharging Python's relatively low native performance for data visualization.
+                       By performing parallel operations on large arrays, all at once, NumPy accelerates data-processing and visualization of large quantities of data, beyond Python's native performance levels for data visualization at scale.
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