3 Pandas functions you might use on rare occasions.

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There is no objection that Pandas is a powerful library. For every data scientist, Pandas is one of their favorite libraries for data analysis and visualizations. It’s easy to get used to some concepts of Pandas. Today I want to share with you 3 functions that you use on very rare occasions.

So, without any further due let’s get started!!

idxmin() and idxmax()

The admin() function is used to get the index of the minimum value.

The id max() function is used to get the index of the maximum value.

Series.idxmin(self, axis=0, skipna=True, *args, **kwargs)

If multiple values equal the maximum and the minimum, the first-row label with that value is returned.

cut()

The cut() method is used when you need to sort the data values into bins it can also segregate an array of elements into separate bins. It is used to convert continuous variables to categorical variables. The reason this function is used on rare occasions is because this function is used only for 1-D arrays. And if we have a large set of scalar data and perform some statistical analysis on it, we can use the cut() method.

pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise')

In this syntax the parameter labels and duplicates are optional.

pivot_table()

Pivot tables create a spreadsheet-style table for a Data Frame. If you’ve used Excel before I am sure that you’ve used them. Nevertheless, to say, they can be implemented in Python without much effort. The Pandas pivot_table() is used to calculate, aggregate, and summarize your data. It is defined as a powerful tool that aggregates data with calculations such as Sum, Count, Average, Max, and Min. It also allows the user to sort and filter your data when the pivot table has been created.

DataFrame.pivot_table(self, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)

In the syntax of this function, the parameter values are an optional. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

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