In this post we will discuss about pandas which is open source library in python written in assembly language to effectively and efficiently handle tabular data. we will also discuss why and how to use pandas in deep learning as a data handling tool
pandas is python library developed to handle huge tabular data locally. It can manipulate data as you wish in efficient manner. You can sort , merge ,categorize data using pandas. You can handle date time format in it. You can import data in pandas from many different sources through database server ,CSV file, JSON and many others .
It is a database server running locally in python environment. You can apply all database query in pandas using python language.
Pandas is structured from ndarrays of NUMPY. In pandas there are three terms used to handle data they are:-
it is supported by almost all Deep learning frameworks available on internet due to efficiency . it is used and supported by tensorflow, Pytorch, scipy, fastai, keras and many more. It's functionality to transfer data to NUMPY makes it more popular . it also uses mathplatlib to show histogram and graph to show data in it.
BASIC USAGE through example
Installation - python installs pandas
download CSV - Iris flower dataset
import pandas as pd
data = pd. read_csv ('iris.csv')
data.head() #view data
SL=data['sepal_lenght'] # takecolumndata
data['sepal_lenght']=1.6 #manupulate data
data.drop('sepal_lenght',inplace=True) # drop column
data['NEW']=1.6 # add new column
data.dtypes # check data type
C =data.iloc # retrive data through index
In this post we have discussed about pandas and its usage in deep learning. we have also shown some of the commands used in pandas. If you want to learn more about pandas visit this link.If you need any help or assistance comment us here . we will be happy to help you .