Pandas is a Python library that gives you a fantastic set of tools to do data analysis.

If you’re going to work with data using Python then you’re gonna need to learn pandas and that’s data analysis, data science, machine learning if it involves data you’ll need to know how to use pandas. Once you know Pandas you won’t want to use anything else. You certainly won’t want to go back to excel and Pandas is free.

Pandas is really convincing. They produce you with a huge set of important commands and specialties which are used to efficiently analyze your data. With Pandas, you can load, prepare, manipulate, model, and analyze data. You can join data, you can merge data, you can reshape data, you can take data from different databases and put it together and analyze it. You can do pretty much anything you want to with data and it all revolves around a structure called a data frame.

Pandas is a free software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. Pandas is mainly used for machine learning in form of DataFrames. Pandas allows importing data of various file formats such as csv, excel etc. And it provides various data manipulation operations such as groupby, join, merge, melt, concatenation as well as data cleaning features such as filling, replacing or imputing null values.

Pandas is defined as an open-source library that provides high-performance data manipulation in Python. The name of Pandas is derived from the word Panel Data, which means Econometrics from Multidimensional data. It can be used for data analysis in Python and was developed by Wes McKinney in 2008. It can perform five significant steps that are required for processing and analysis of data irrespective of the origin of the data, load, manipulate, prepare, model, and analyze.

Pandas is essentially used for data analysis. Pandas enables importing data from numerous file formats such as comma-separated-values, JSON, SQL, Microsoft Excel. Pandas enables numerous data manipulation operations such as merging, reshaping, selecting, as well as data cleaning, and data wrangling features.

Pandas provides remarkably streamlined forms of data representation. This helps to examine and interpret data better. Simpler data description facilitates better outcomes for data science projects.

Pandas helps to save a lot of time by conveying large sums of data very quickly.

This is a short explainer video on pandas in python. Giles McMullen-Klein who has been using Python as a scientist for years, tell you what pandas is, why it’s used and gives a couple of tutorials on how to use it. He does some exploratory analysis of the titanic data set and shows you how pandas can work with time series using stock market data.

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