I/O Tools

While working on data-intensive applications, We have often confronted with Input/Output(I/O) challenges which represent the bottleneck for every performance-critical application. With the increasing volume of stored data, there is a…

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Categorical data

Categoricals are a pandas data type  A categorical variable takes on a limited and usually fixed, number of possible values. for example, Gender, blood group, ratings, etc. All values of categorical data…

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Time Series

In the broadest definition, a time series is any data set where the values are measured at different points in time. Many time series are uniformly spaced at a specific frequency, for…

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Pivot Table

Pivoting is just opposite of melting. In melting we turned columns into rows but in pivoting we turn unique values into separate columns. In Pivoting we take all the unique entries…

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Reshaping

Many times our dataset comes in a way that does not suit for manipulation. That is why we use various reshaping methods like Pivot, melt, etc. so that our dataset…

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Handling missing data

Missing data is always a problem in real life scenarios. Areas like machine learning and data mining face severe issues in the accuracy of their model predictions because of the…

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Aggregation

Aggregations refer to any data transformation that produces scalar values from arrays. You may wonder what is going on when you invoke mean() on a GroupBy object. Many common aggregations,…

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Binary Operations

One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more sophisticated operations (trigonometric functions,…

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DataFrame

DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects. It…

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