Introduction
Pandas in the world of programming, it is a python library used for working with data sets. Pandas library are very useful in the field of data science.
Pandas is an open source python library that was created by Wes McKinney in 2008. It is mafe for working with relational labelled data. This library provides numerous data structures and operations for manipulating numerical data and time series. Pandas is fast and has high performance when it comes to productivity for users.
Pandas has been one of the most commonly used tools for Data Science and Machine learning, which is used for data cleaning and analysis. Pandas is one of the open-source python packages built on top of NumPy. Pandas can read or load data in many formats such as CSV, SQL, JSON, Excel and many more. The source code for Pandas is located at this Github repository.
How to use pandas?
You can use pandas by installing and importing to your file.
For installing
pip install pandas
For importing
import pandas
Example
import pandas
mydataset = {
'fruits': ["banana", "Orange", "Apple", "Grapes"],
'numbers': [23, 7234, 223, 43]
}
table = pandas.DataFrame(mydataset)
print(table)
output
Why use pandas?
- Pandas allows us to evaluate big data and make conclusions based on statistical theories.
- Pandas provides remarkably streamlined forms of data representation.
- Pandas can clean messy data sets, and make them readable and relevant.
- Panda is the best library used in the field of data science.
Advantages
- Different files data can be loaded.
- Easy handling of missing data.
- Easy importing and installations.
- Provides time series functionality.
- Less code and more work done.
- Provides streamlined forms of data representation.
- Efficiently handles large data.