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Data Gathering and Cleaning,res that make it easy to support data analysis and processing. Making business decisions, forecasting weather, studying protein structures in biology, and designing a marketing campaign are all examples that require collecting data and then cleaning, processing, and visualizing it.

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Data Exploring and Analysis,en source Pandas library fills that gap and deals with three different data structures: series, data frames, and panels. A . is a one-dimensional data structure such as a dictionary, array, list, tuple, and so on. A . is a two-dimensional data structure with heterogeneous data types, i.e., tabular d

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Data Visualization,rn, Bokeh, Pygal, and Ploty. Python Pandas is the simplest method for basic plotting. Python Seaborn is great for creating visually appealing statistical charts that include color. Python Bokeh works great for more complicated visualizations, especially for web-based interactive presentations. Pytho

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Security of Random Output Perturbation for Statistical Databasesace-distributed noise that can be added to user queries if database privacy is to be preserved. Our results do not require the additive noise to be bounded by . as assumed in Dinur & Nissim (2003) and Dwork & Yekhanin (2008), which, on the other hand, do admit correlated noise.

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