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.

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

You can convert a Pandas DataFrame to a JSON object using the to_json() method in Pandas. Here's an example to demonstrate how to do this:

Step 1: Install Pandas (if not installed)

If you don't have Pandas installed, you can install it using the following command:

bash
pip install pandas

Step 2: Example Code to Convert Pandas DataFrame to JSON

python
import pandas as pd

# Sample DataFrame
data = {
    'id': [1, 2, 3],
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35]
}

df = pd.DataFrame(data)

# Convert DataFrame to JSON
json_data = df.to_json(orient='records', lines=True)

# Print JSON result
print(json_data)

Explanation:

Creating the DataFrame: The sample data dictionary is used to create a DataFrame df.
Converting to JSON: The to_json() method is used to convert the DataFrame to a JSON string. Here, we use the following parameters:
- orient='records': This means the JSON will be in a record-oriented format, where each row becomes a dictionary.
- lines=True: This creates a newline-delimited JSON (you can omit this if you want a compact single-line JSON).
Output: The output will be a JSON string.

Example Output:

json
{"id":1,"name":"Alice","age":25}
{"id":2,"name":"Bob","age":30}
{"id":3,"name":"Charlie","age":35}

Other `orient` options for `to_json()`:

'split': Dictionary with keys: index, columns, and data.
'records': List of dictionaries (each dictionary is a row).
'index': Dictionary with index as keys and rows as values.
'columns': Dictionary with column names as keys and rows as values.

For instance, using orient='split':

python
json_data_split = df.to_json(orient='split')
print(json_data_split)

Output:

json
{
    "columns":["id","name","age"],
    "index":[0,1,2],
    "data":[[1,"Alice",25],[2,"Bob",30],[3,"Charlie",35]]
}

This gives you flexibility depending on how you want the data formatted in your JSON.

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