Learn Pandas for Everyone in Python with Data Analysis Book Full Free PDF 2023.

 Learn Pandas for  Everyone in Python with Data Analysis 

Book Full Free PDF 2022.

What is pandas and what is it used for in Python?

When we talk about pandas we are not referring to a group of bamboo-eating black and white bears, we are talking about the Pandas library in Python, specialized in the management and analysis of data structure, open source and dependent on the Numpy library. 

With pandas we can represent tabular data with columns with labels and rows and time series. 

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Tools of the pandas library:

 It provides us with tools that allow us to read and write data in various formats such as CSV, Microsoft Excel, SQL databases and HDF5 format.

They also allow us to select and filter data tables, merge and join data, transform them by applying both global and window functions, manipulate time series and even make graphs. 

Pandas has three different data structures: Series, are one-dimensional structures DataFrame, two-dimensional structures (tables) Panel, three-dimensional structures (cubes) Let’s see an example of the first two in a quick and simple way. 

Series: They are structures of a dimension, similar to arrays, has an index that associates a name to each element of the series to access it. 

DataFrame: They are structured data sets in table form where each column is an object of type Series, all data of the same column are of the same type and the rows can contain different types.

About the Pandas Python Library:

As mentioned earlier, Pandas is an open-source, free-to-use Python library (under a BSD license) that provides tools for data analysis and manipulation. Pandas lets you work with different types of data, for example: Tabular data, such as an Excel spreadsheet or SQL table; Data ordered temporally or not; arrays; Any other dataset, which does not necessarily need to be labeled; The magic of reading, manipulating, aggregating and displaying data with just a few commands explains why the library has become so popular. Incidentally, all this is possible due to the primary structures of Pandas, the famous Series and DataFrames. Data structure: The two main objects in the Pandas library are Series and DataFrames. A Series is a one-dimensional array that contains a sequence of values ​​that have an index (which can be integers or labels), much like a single column in Excel. The DataFrame is a tabular data structure, similar to an Excel data sheet, in which both rows and columns have labels. From the main objects, the Pandas library provides a set of sophisticated indexing functionalities, which allows us to reformat, manipulate, aggregate or select specific subsets of the data we are working on.

Series and Dataframe - Pandas Python:

Explanation in visual lines about Series and DataFrame What are the advantages of using it? Pandas has many advantages compared to using native Python language structures, some of them are: The ease of learning and using the library: It is much easier to work with a Pandas object than it is to gather information through interactions of Python lists and dictionaries. To collaborate even more, on its website the library also provides a list of commands so that developers who use other languages, such as R, SQL, SAS, among others, can find the equivalent commands , with the same functionality in Pandas; Growing and very active community : In the latest Stack Overflow ' 2020 Developer Survey ', Pandas ranks fourth in the list of tools and packages that professional developers use. Generally, the more developers use a library, the easier it is to find solutions to problems available on the network. Besides the possibility that if there is a bug in the package, or in some method of it, it will be fixed more quickly; Support for automatic or explicit data alignment : Objects in Pandas can be explicitly aligned with named axes, which the user may or may not specify. This alignment avoids common misaligned data errors and makes it possible to work with data that have different indexes (which may come from different sources); Flexible and simplified handling of missing data : It makes possible in a simplified way the replacement or deletion of missing data that the dataset we are working on may present; Use of operations : The library allows the use of arithmetic operations to aggregate or transform the data found in its main structures (Series and DataFrames); Relational combinations and operations : Pandas provides methods to facilitate the combination of datasets, in addition to allowing us to select subsets of our original data, based on certain filters; If you're not familiar with the concept of filters in Pandas.

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