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Answers for "valid json example"

Javascript
11

json data example

[
	{
		"id": "0001",
		"type": "donut",
		"name": "Cake",
		"ppu": 0.55,
		"batters":
			{
				"batter":
					[
						{ "id": "1001", "type": "Regular" },
						{ "id": "1002", "type": "Chocolate" },
						{ "id": "1003", "type": "Blueberry" },
						{ "id": "1004", "type": "Devil's Food" }
					]
			},
		"topping":
			[
				{ "id": "5001", "type": "None" },
				{ "id": "5002", "type": "Glazed" },
				{ "id": "5005", "type": "Sugar" },
				{ "id": "5007", "type": "Powdered Sugar" },
				{ "id": "5006", "type": "Chocolate with Sprinkles" },
				{ "id": "5003", "type": "Chocolate" },
				{ "id": "5004", "type": "Maple" }
			]
	},
	{
		"id": "0002",
		"type": "donut",
		"name": "Raised",
		"ppu": 0.55,
		"batters":
			{
				"batter":
					[
						{ "id": "1001", "type": "Regular" }
					]
			},
		"topping":
			[
				{ "id": "5001", "type": "None" },
				{ "id": "5002", "type": "Glazed" },
				{ "id": "5005", "type": "Sugar" },
				{ "id": "5003", "type": "Chocolate" },
				{ "id": "5004", "type": "Maple" }
			]
	},
	{
		"id": "0003",
		"type": "donut",
		"name": "Old Fashioned",
		"ppu": 0.55,
		"batters":
			{
				"batter":
					[
						{ "id": "1001", "type": "Regular" },
						{ "id": "1002", "type": "Chocolate" }
					]
			},
		"topping":
			[
				{ "id": "5001", "type": "None" },
				{ "id": "5002", "type": "Glazed" },
				{ "id": "5003", "type": "Chocolate" },
				{ "id": "5004", "type": "Maple" }
			]
	}
]
Posted by: Guest on July-27-2020
3

json object

var myObj, x;
myObj = {"name":"John", "age":30, "car":null};
x = myObj.name;
document.getElementById("demo").innerHTML = x;
Posted by: Guest on April-16-2020
0

JSON validate

import numpy as np
df['first_five_Letter']=df['Country (region)'].str.extract(r'(^w{5})')
df.head()
Posted by: Guest on April-07-2021
0

JSON validate

#convert column to string
df['movie_title'] = df['movie_title'].astype(str)

#but it remove numbers in names of movies too
df['titles'] = df['movie_title'].str.extract('([a-zA-Z ]+)', expand=False).str.strip()
df['titles1'] = df['movie_title'].str.split('(', 1).str[0].str.strip()
df['titles2'] = df['movie_title'].str.replace(r'([^)]*)', '').str.strip()
print df
          movie_title      titles      titles1      titles2
0  Toy Story 2 (1995)   Toy Story  Toy Story 2  Toy Story 2
1    GoldenEye (1995)   GoldenEye    GoldenEye    GoldenEye
2   Four Rooms (1995)  Four Rooms   Four Rooms   Four Rooms
3   Get Shorty (1995)  Get Shorty   Get Shorty   Get Shorty
4      Copycat (1995)     Copycat      Copycat      Copycat
Posted by: Guest on April-07-2021
0

JSON validate

**Output:** ['Finland', 'Florida', 'france']
Posted by: Guest on April-07-2021
0

JSON validate

S=pd.Series(['Finland','Colombia','Florida','Japan','Puerto Rico','Russia','france'])
[itm[0] for itm in S.str.findall('^[Ff].*') if len(itm)>0]
Posted by: Guest on July-01-2020

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