View: A view is essentially a stored query and it allows you to select from a dynamic set of data. I have often replaced

Clipping involves the capping of all values below or above a certain value. Clipping is useful when a column contains some outliers. We can set a maximum vmax and a minimum value vmin and set all outliers greater than the maximum value to vmax and all the outliers lower than the minimum value to vmin. For example, we can consider the column ricoverati_con_sintomi and we can set vmax = 10000 and vmin = 10.

Z-Score converts every value of a column into a number around 0. Typical values obtained by a z-score transformation range from -3 and 3. The new value is calculated as the difference between the current value and the average value, divided by the standard deviation. The average value of a column can be obtained through the mean() function, while the standard deviation through the std() function. For example, we can calculate the z-score of the column deceduti.

Note: The trade-off for more efficient queries is increased storage. Non-clustered indexes require their own storage since they are kept separate from the table data. Only index the fields that you need to filter or join on in order to find the best balance between storage and performance.

There are many indexes that you can set on a field for specific purposes, but if you are attempting to improve performance, you will generally be creating a generic non-clustered index.

Indexing is the #1 way to optimize database performance. Indexing affects the way that data is stored in pages. By default, records are not stored in any guaranteed order, so essentially indexing a field causes the data to be stored in a predictable manner. Therefore, when you filter on that field, the query will run faster because SQL Server knows exactly where to start looking.

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ers talking to? Are they “performing” to a speaker, or are they speaking like they’re the only ones there? Pay attention to how people talk to each other in life. You’ll realize that people don’t often listen to the other person and respond to them, but rather retort with a canned phrase — merely waiting for their turn to speak. This is especially evident in discussions with groups of people.

As example dataset, in this tutorial we consider the dataset provided by the Italian Protezione Civile, related to the number of COVID-19 cases registered since the beginning of the COVID-19 pandemic. The dataset is updated daily and can be downloaded from this link.

Log Scaling involves the conversion of a column to the logarithmic scale. If we want to use the natural logarithm, we can use the log() function of the numpy library. For example, we can apply log scaling to the column dimessi_guariti. We must deal with log(0) because it does not exist. We use the lambda operator to select the single rows of the column.

Single Feature Scaling converts every value of a column into a number between 0 and 1. The new value is calculated as the current value divided by the max value of the column. For example, if we consider the column tamponi, we can apply the single feature scaling by applying to the column the function max(), whic calculates the maximum value of the column:

Non-clustered index: Stores the indexed field separately from the table. This indexed field points to the data stored in the table, just like the index of a book. You can have as many non-clustered indexes as you want!

Similarly to Single Feature Scaling, Min Max converts every value of a column into a number between 0 and 1. The new value is calculated as the difference between the current value and the min value, divided by the range of the column values. For example, we can apply the min max method to the column totale_casi.

In this tutorial, I have shown you the different techniques used to perform data normalisation: single feature scaling: min max, z-score, log scaling, clipping. Thus, the question is: what is the best technique? Actually, there is not a technique better than the others, the choice of a method rather than another depends on what we want as output. Thus:

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First of all, we need to import the Python pandas library and read the dataset through the read_csv() function. Then we can drop all the columns with NaN values. This is done through dropna() function.

0 0.000000 1 0.000000 2 0.000000 3 0.000000 4 0.000000 ... 5812 9.846388 5813 10.794296 5814 9.474088 5815 8.372861 5816 10.922389 Name: dimessi_guariti, Length: 5817, dtype: float64