In This Post You Will Know About Python Pandas.
Table of Contents
What is Pandas?
Pandas is a library designed to work with Python. It is a library written for Python programming language that allows analysis and manipulation of data. It also provides data structures and functions to manipulate numerical tables as well as time series. It is an open-source Python package that is extensively used for data science, data analysis as well as machine-learning tasks.
Why use Pandas?
Pandas is mostly utilized for data analysis. Pandas permits the import of data from various formats, including comma-separated data, JSON, SQL, Microsoft Excel. Pandas lets you perform various data manipulation options, such as merging, reshaping, choosing as well as cleaning and data wrangling functions.
What can Pandas do?
Pandas can make easy on work with the following tasks: –
- Manipulate Data
- Analysis Data
- Cleaning Data
- Handling large data
- Easy to work with numerical tables and time-series
Apart from this, we can utilize Pandas to accomplish a variety of tasks such as filtering information by certain criteria or segregating and segmenting the data according to preferences, etc.
Difference between Pandas and NumPy:
- Pandas library is mainly works with numerical tables (row; column) whereas Numpy module is available for numerical data.
- Pandas has some feature like DataFrame and Series that allows to work with analyzing the data, on the other hand, Numpy provides an object called Array.
- Pandas library is slow than Numpy module in indexing Series objects.
- Pandas library is best to work with 500k rows or more whereas Numpy module is best to work with 50k rows or less.
- Pandas offers 2nd table object called DataFrame whereas Numpy module provides a multi-dimensional array.
- Numpy modules can be accepted directly as an input in Machine Learning with the help of one of the essential tools of Machine Learning, i.e., Scikit Learn and Tensorflow, whereas Pandas data object is not directly supported as an input; it needs some things to be ready before serving it is as an input to Machine Learning.
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