What is Pandas in Python?

Prerequisites

To use the Pandas module, some of the following conditions must be met before continuing:

• Knowledge of programming languages (preferably Python)
• Basic understanding of Python's Numpy library

What Are Pandas In Python?

Let's now see what is pandas in Python. Pandas is an open-source Python library that has a BSD license (BSD licenses are a low-restriction type of license for open source software that imposes minimum restrictions on the use and distribution of open source software) and is used in data science, data analysis, and machine learning activities. Both readily and intuitively, it functions with relational or labeled data.

It offers a variety of data structures and operations for working with time series and numerical data. This library is developed on top of the NumPy library, which supports multi-dimensional arrays. As a result, pandas are quick and offer users high performance and productivity. Being one of the most widely used data-wrangling tools, Pandas integrates well with a variety of different data science modules within the Python environment and is frequently available in all Python distributions, including those that come with your operating system and those sold by commercial vendors like ActiveState's ActivePython.

History

Pandas were developed by Wes McKinney, who started working on pandas in 2008, as a developer at ARQ Capital Management. He convinced management to let him open source the library before he left AQR. As a result, Chang She, an additional AQR employee, joined the project in 2012 and became the library's second-largest contributor. Pandas joined NumFOCUS in 2015, a 501(c)(3) nonprofit organization in the US, as a fiscally sponsored project. Pandas 1.4.1 is the latest version.

Timeline of Pandas Software

• 2008: Panda development began
• 2009: Pandas becomes open source
• 2012: Release of the first edition of Python for Data Analysis.
• 2015: Project Pandas being sponsored by NumFOCUS.
• 2018: Initial in-person core developer sprint

Key Features of Pandas

• Quick and efficient data manipulation and analysis.
• Tools for loading data from different file formats into in-memory data objects.
• Label-based Slicing, Indexing, and Subsetting can be performed on large datasets.
• Merges and joins two datasets easily.
• Pivoting and reshaping data sets
• Easy handling of missing data (represented as NaN) in both floating point and non-floating point data.
• Represents the data in tabular form.
• Size mutability: DataFrame and higher-dimensional object columns can be added and deleted.
• It provides time-series functionality.
• Effective grouping by functionality for splitting, applying, and combining data sets.

Advantages of using Pandas

There are lots of benefits for using the Pandas module in. Let's see one by one some advantages of Pandas.

• Data visualization Data representation with Pandas is incredibly simplified. This helps with improved data analysis and understanding. Data science projects produce better results when the data is represented more simply.
• Less writing and more productivity It is one of the Pandas' best features. With the help of Pandas, multiple lines of Python code in the absence of any support libraries can be easily completed in one or two lines. As a result, Pandas help to reduce time and procedures while also speeding up the data-handling process. As a result, we can devote more time to data analysis algorithms.
• Efficiently handles large amounts of data Pandas handle large datasets very efficiently. Pandas save a lot of time by importing large amounts of data quickly.
• A large number of features Pandas provide you with a large set of important commands and features by which data can be easily analyzed. In addition, pandas can perform various tasks, such as data filtering based on certain conditions, segmenting and segregating the data by preferences, and so on.
• Flexibility and customization of data With the help of Pandas, you may apply a wide range of features. For example, we can alter, customize, and pivot the existing data according to our wishes. Your data may be used to its greatest extent by doing this.
• Built for Python Because of its large set of features and high productivity, Python has emerged as one of the most popular programming languages in the world. Because of this, programming Pandas in Python gives access to many of Python's many other features and packages like MatPlotLib, SciPy, NumPy, etc.

Why are pandas used for Data Science?

Pandas is one of the foundational libraries for data science. Pandas is a base package that includes extra functionality from several other packages. Python's Pandas are similar to Excel: A data frame is a structure used by pandas to store data. The actual structure of the data frame is an array, which is built upon the NumPy library, another essential ML component.

Data in the form of an array is required for almost all models. Pandas provide the ability to organize your structured data into an array so that it can be managed. Pandas do the following basic tasks: Data wrangling, reading, writing, logical processes, simple plotting, updating the data, counting the instances, SQL join, etc.

Data wrangling is cleaning up errors and merging different complex data sets to make complicated data sets more accessible and understandable.

Python Pandas Data Structure

Python Pandas provides two main data structures, i.e., series and data frames, to store, retrieve, and manipulate the data. These data structures are built on the NumPy library and are very fast.

Let's take a quick view of these data structures.

Series

A series is a one-dimensional array that contains elements of the same data type. Series are mutable means we can modify the elements of a series but its size is immutable, i.e. we cannot change the size of the series once declared. It has two main components: data and an index.

Using the following constructor, a Pandas series can be created: pandas.Series( data, index, dtype, copy)

• Parameters:
  • data(required): This is the input data, which can be any list, dictionary, etc.
  • index(Optional): The index for the value you use for the series is represented by this number.
  • dtype(Optional): This describes the values contained in the series.
  • copy(Optional): This makes a copy of the input data. Let's see how to implement a Series in the following example:

Code:

Output:

Explanation: NumPy as np and pandas as pd are imported in the above code example. Array data is created using np.array() method and then converted into a Series data structure using pd.series() method and stored in df variable. We got output with the desired index and float datatype as we passed index values.

DataFrame

Dataframe is a 2-dimensional data structure that contains elements of the same data. It is mutable, and its size is also mutable, i.e. we can change both data and size of the dataframe data structure. It has labeled axes (rows and columns) and has two different indexes (row index and column index) as both rows and columns are indexed.

Using the following constructor, a Pandas DataFrame can be created:

pandas.DataFrame( data, index, columns, dtype, copy)

• Parameters:

  • data(required): Input data, can be ndarray, series, map, lists, dict, constants, and another DataFrame.
  • index(optional): For labeling rows.
  • columns(Optional): For labeling columns.
  • dtype(Optional): Data type of each column.
  • copy(Optional): This makes a copy of the input data.

Let's see how to implement DataFrame in the following example:

Code:

Output:

Explanation: In the above code example, we import NumPy as np and Pandas as pd. Array data is created using np.array() method and then converted into a DataFrame data structure using np.DataFrame() and stored in df variable. We get output as our data, column index '0', as only 1-column, and by default, row index starts from '0' as no index value is passed.

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