Pandas in Python: Features, Installation, and Key Advantages for Data Science
ML-Libraries (Part 8)
📚Chapter: 2 - Pandas
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Introduction
Def: Pandas is a library designed for working with diverse types of labeled data concurrently. For instance, you would utilize it to analyze a CSV file that contains numerical, alphabetical, and string data. Pandas is a powerful data analysis and manipulation library for Python, Developed by Wes McKinney in 2008, Pandas has become one of the most widely used tools in data science, enabling users to handle data in various forms such as CSV files, SQL databases, Excel spreadsheets, and more.
Def: Pandas is a Python library that offers flexible and expressive data structures, such as dataframes and series, designed for data manipulation.
Background: Pandas offers the ability to read and write data from various sources, including CSV files, Excel spreadsheets, SQL databases, HDFS, and more.It offers functionalities to add, update, and delete columns, merge or divide dataframes/series, manage datetime objects, impute missing values, process time series data, and convert to and from numpy arrays, among other capabilities.
Pandas operates in-memory, as it loads the entire dataset into the local memory of the machine it is running on. This limits its ability to handle large datasets. With cuDF’s pandas accelerator you can now bring accelerated computing to pandas workflows. Also their cuDF library will automatically know if you’re running on GPU or CPU and speed up your processing. RAPIDS cuDF is now integrated directly into Google Colab.
Sections
Why Pandas
Advantages
Downsides
Features
Installing Pandas
Importing
Section 1- Why Pandas
There are tons of reasons why pandas are the way to go, like…
Open Source
Easy to Learn
Great Community
Built on Top of Numpy
Easy to Analyze and pre-process data in it
Built-in Data Visualization
A lot of Built-in functions to help in Exploratory Data Analysis
Built-in support for CSV, SQL, HTML, JSON, pickle, excel, clipboard and a lot more
and a lot more
Section 2- Advantages
Remarkably user-friendly and requiring minimal effort to master, this tool simplifies the management of tabular data.
An impressive suite of utilities for loading, transforming, and writing data across various formats.
Compatible with underlying NumPy objects, it is the preferred choice for most Machine Learning libraries, such as scikit-learn and others.
The ability to create plots and visualizations readily (employs matplotlib to generate various visualizations behind the scenes).
Section 3- Downsides
Using it is easy, but it uses more memory.
Pandas makes a bunch of extra objects that can slow things down when you’re trying to work with them easily.
Inability to utilize distributed infrastructure, though pandas can work with formats like HDFS files,
It can’t use a distributed system setup to make things run faster.
Section 4- Features:
The central feature of Pandas lies in its diverse data structures, which enable users to carry out a wide range of analytical operations.
Pandas offers a range of modules for data manipulation, such as reshaping, joining, merging, and pivoting.
Pandas possesses capabilities for data visualization.
Built on Top of NumPy
Users have the ability to execute mathematical operations, encompassing calculus and statistics, without the need for external libraries.
It includes modules that assist in managing missing data.
There are many built-in functions available for Exploratory Data Analysis.
Section 5- Installing Pandas
If you’re using Anaconda, pandas should already be included. However, if it’s not installed for some reason, you can simply run the following command to install it.conda install pandas.
conda install pandasIf you’re not utilizing Anaconda, you can install the package using pip by following the appropriate command.
pip install pandasImporting numpy alongside pandas is beneficial as it provides access to a broader range of numpy features, which are useful in Exploratory Data Analysis (EDA).
Section 6- Importing
To import pandas, use
import pandas as pd
import numpy as npPlease Follow and 👏 Subscribe for the story courses teach to see latest updates on this story
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