Python Pandas As Json

 Python Pandas As Json


Pandas allows us to analyze big data and make conclusions based on statistical theories.

Pandas can clean messy data sets, and make them readable and relevant.


Pandas gives you answers about the data. Like:

  • Is there a correlation between two or more columns?
  • What is average value?
  • Max value?
  • Min value?

Pandas are also able to delete rows that are not relevant, or contains wrong values, like empty or NULL values. This is called cleaning the data.




Installation of Pandas

If you have Python and PIP already installed on a system, then installation of Pandas is very easy.

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C:\Users\RCF>cd..


C:\Users>cd..


C:\>cd Python3.9.1


C:\Python3.9.1>cd Scripts


C:\Python3.9.1\Scripts>pip install pandas

Collecting pandas

  Downloading pandas-1.2.0-cp39-cp39-win_amd64.whl (9.3 MB)

     |████████████████████████████████| 9.3 MB 18 kB/s

Collecting python-dateutil>=2.7.3

  Downloading python_dateutil-2.8.1-py2.py3-none-any.whl (227 kB)

     |████████████████████████████████| 227 kB 19 kB/s

Collecting numpy>=1.16.5

  Downloading numpy-1.19.5-cp39-cp39-win_amd64.whl (13.3 MB)

     |████████████████████████████████| 13.3 MB 3.2 MB/s

Collecting pytz>=2017.3

  Downloading pytz-2020.5-py2.py3-none-any.whl (510 kB)

     |████████████████████████████████| 510 kB 68 kB/s

Requirement already satisfied: six>=1.5 in c:\python3.9.1\lib\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)

Installing collected packages: python-dateutil, numpy, pytz, pandas

Successfully installed numpy-1.19.5 pandas-1.2.0 python-dateutil-2.8.1 pytz-2020.5

WARNING: You are using pip version 20.2.3; however, version 20.3.3 is available.

You should consider upgrading via the 'c:\python3.9.1\python.exe -m pip install --upgrade pip' command.


C:\Python3.9.1\Scripts> DONE

EXAMPLE LET 1

#!C:/Python3.9.1/python.exe


print("content-type: text/html\n\n" )



import pandas as pd


mydataset = {

  'name': ["akhil", "aks", "nano"],

  'passings': [3, 7, 2]

}


myvar = pd.DataFrame(mydataset)


print(myvar)


OPEN BROWSER AS :hi(python file name in my case)

http://localhost:81/python/hi.py


OUT{UT:

name passings 0 akhil 3 1 aks 7 2 nano 2






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