Nicobar | dynamic scripting framework for java | Build Tool library

by Netflix Java Version: Current License: Apache-2.0

kandi X-RAY | Nicobar Summary

Nicobar is a Java library typically used in Utilities, Build Tool applications. Nicobar has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub, Maven.

Nicobar is a dynamic scripting framework for java, driven by a powerful module loading system based on JBoss Modules. Scripts can be source, written in JVM compatible languages (like Groovy), or can be compiled bytecode, in the form of .class files. Scripts can be fetched from persistence dynamically, (compiled and) converted into modules, and inserted in the correct place in a runtime module graph based upon module metadata.

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Quality

Security

License

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kandi-support Support

Nicobar has a low active ecosystem.
It has 168 star(s) with 39 fork(s). There are 398 watchers for this library.
It had no major release in the last 6 months.
There are 5 open issues and 12 have been closed. On average issues are closed in 515 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of Nicobar is current.

Nicobar Support

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Nicobar Support

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quality kandi Quality

Nicobar has 0 bugs and 0 code smells.

Nicobar Quality

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Nicobar Quality

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security Security

Nicobar has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Nicobar code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.

Nicobar Security

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Nicobar Security

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license License

Nicobar is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

Nicobar License

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Nicobar License

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build Reuse

Nicobar releases are not available. You will need to build from source code and install.
Deployable package is available in Maven.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.

Nicobar Reuse

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Nicobar Reuse

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Top functions reviewed by kandi - BETA

kandi has reviewed Nicobar and discovered the below as its top functions. This is intended to give you an instant insight into Nicobar implemented functionality, and help decide if they suit your requirements.

Adds a language plugin to this module

Adds a module spec to the internal repository
Populates a ModuleCompilerModuleSpec from the plugin spec
Returns a snapshot of the moduleIds with the highest revision

Insert a jar into the script archive

Calculates the hash for the given content

Gets all the scripts for the given set of modules

Get the module spec

Gets all rows

Generate CQL to select columns

Read an entity from the stream

Insert a JAR archive in a jar file

Converts a ScriptModule to a compiled ScriptArchive

Write the given object to the entity stream

Returns the script archives for the given modules

Query the summaries of all repositories

Get the script archives for the given modules

Returns the groovy2 compiler spec

Compiles the given script archive

Sets the columns

Inserts the given jar into the root directory

Compile the given source

Inserts a module archive in the given jar archive

Injector

Gets all of the paths from a jar file

Load classes from the archive

Get all kandi verified functions for this library.

Nicobar Key Features

Nicobar Examples and Code Snippets

Community Discussions

Trending Discussions on Nicobar

Opencv facing an error for tesseract moule not found even after installations
How to loop this type of Json Data in Angular front end
How can I append other data in a list of dictionaries based on matching key values using Python
Is there a way to have insertion of tags or labels with respect to a certain value range?
How to print only a portion of JSON object's data?
How to create multiple pivot_longer() columns in r?
How to extract dictionary from JSON data from API? I tried some solutions from Stackoverflow
Format the extracted covid vaccine data from website
How to merge/ add columns to dataframes in pandas when the joining column has slight spelling differences?
How to get data from an api in r?

Trending Discussions on Nicobar

QUESTION

Opencv facing an error for tesseract moule not found even after installations

Asked 2022-Feb-01 at 14:04

Opencv facing an error for tesseract moule not found even after installations

pip install tesseract

opencv reinstallation also done but it wont work.

opencv installation commands are

pip install opencv-python

pip intstall opencv-contrib-python

tesseract.exe installtion using this github guidance -link

Still iam facing this below error

error - tesseract module not found

from PIL.Image import ImageTransformHandler
import cv2
import numpy as np
import pytesseract

pytesseract.pytesseract.tesseract_cmd='C:\Program Files(x86)\Tesseract-OCR\tesseract.exe'

cascade= cv2.CascadeClassifier("haarcascade_russian_plate_number.xml")
states={"AN":"Andaman and Nicobar",
    "AP":"Andhra Pradesh","AR":"Arunachal Pradesh",
    "AS":"Assam","BR":"Bihar","CH":"Chandigarh",
    "DN":"Dadra and Nagar Haveli","DD":"Daman and Diu",
    "DL":"Delhi","GA":"Goa","GJ":"Gujarat",
    "HR":"Haryana","HP":"Himachal Pradesh",
    "JK":"Jammu and Kashmir","KA":"Karnataka","KL":"Kerala",
    "LD":"Lakshadweep","MP":"Madhya Pradesh","MH":"Maharashtra","MN":"Manipur",
    "ML":"Meghalaya","MZ":"Mizoram","NL":"Nagaland","OD":"Odissa",
    "PY":"Pondicherry","PN":"Punjab","RJ":"Rajasthan","SK":"Sikkim","TN":"TamilNadu",
    "TR":"Tripura","UP":"Uttar Pradesh", "WB":"West Bengal","CG":"Chhattisgarh",
    "TS":"Telangana","JH":"Jharkhand","UK":"Uttarakhand"}

def extract_num(img_filename):
    img=cv2.imread(img_filename)
    #Img To Gray
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    nplate=cascade.detectMultiScale(gray,1.1,4)
    #crop portion
    for (x,y,w,h) in nplate:
        wT,hT,cT=img.shape
        a,b=(int(0.02*wT),int(0.02*hT))
        plate=img[y+a:y+h-a,x+b:x+w-b,:]
        #make the img more darker to identify LPR
        kernel=np.ones((1,1),np.uint8)
        plate=cv2.dilate(plate,kernel,iterations=1)
        plate=cv2.erode(plate,kernel,iterations=1)
        plate_gray=cv2.cvtColor(plate,cv2.COLOR_BGR2GRAY)
        (thresh,plate)=cv2.threshold(plate_gray,127,255,cv2.THRESH_BINARY)
        #read the text on the plate
        read=pytesseract.image_to_string(plate)
        read=''.join(e for e in read if e.isalnum())
        stat=read[0:2]
        cv2.rectangle(img,(x,y),(x+w,y+h),(51,51,255),2)
        cv2.rectangle(img,(x-1,y-40),(x+w+1,y),(51,51,255),-1)
        cv2.putText(img,read,(x,y-10),cv2.FONT_HERSHEY_SIMPLEX,0.9,(255,255,255),2)

        cv2.imshow("plate",plate)
        
    cv2.imwrite("Result.png",img)
    cv2.imshow("Result",img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

extract_num('car_img.png')

ANSWER

Answered 2022-Feb-01 at 14:04

After fixing this error working fine changing file path (C:\Program Files(x86)\Tesseract-OCR\tesseract.exe) to ->(C:\Program Files\Tesseract-OCR\tesseract.exe)

Whatever your path in windows programfiles, do not use a Program Files(x86) it wont work

Still showing or not installing error you are facing then press windows + R keys and run your file path (C:\Program Files\Tesseract-OCR\tesseract.exe) it wil work for me,

This mentioned error had fixed - tesseract module not found

Hope it will help some one.

from PIL.Image import ImageTransformHandler
import cv2
import numpy as np
import pytesseract

pytesseract.pytesseract.tesseract_cmd=r'C:\Program Files(x86)\Tesseract-OCR\tesseract.exe'

cascade= cv2.CascadeClassifier("haarcascade_russian_plate_number.xml")
states={"AN":"Andaman and Nicobar",
    "AP":"Andhra Pradesh","AR":"Arunachal Pradesh",
    "AS":"Assam","BR":"Bihar","CH":"Chandigarh",
    "DN":"Dadra and Nagar Haveli","DD":"Daman and Diu",
    "DL":"Delhi","GA":"Goa","GJ":"Gujarat",
    "HR":"Haryana","HP":"Himachal Pradesh",
    "JK":"Jammu and Kashmir","KA":"Karnataka","KL":"Kerala",
    "LD":"Lakshadweep","MP":"Madhya Pradesh","MH":"Maharashtra","MN":"Manipur",
    "ML":"Meghalaya","MZ":"Mizoram","NL":"Nagaland","OD":"Odissa",
    "PY":"Pondicherry","PN":"Punjab","RJ":"Rajasthan","SK":"Sikkim","TN":"TamilNadu",
    "TR":"Tripura","UP":"Uttar Pradesh", "WB":"West Bengal","CG":"Chhattisgarh",
    "TS":"Telangana","JH":"Jharkhand","UK":"Uttarakhand"}

def extract_num(img_filename):
    img=cv2.imread(img_filename)
    #Img To Gray
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    nplate=cascade.detectMultiScale(gray,1.1,4)
    #crop portion
    for (x,y,w,h) in nplate:
        wT,hT,cT=img.shape
        a,b=(int(0.02*wT),int(0.02*hT))
        plate=img[y+a:y+h-a,x+b:x+w-b,:]
        #make the img more darker to identify LPR
        kernel=np.ones((1,1),np.uint8)
        plate=cv2.dilate(plate,kernel,iterations=1)
        plate=cv2.erode(plate,kernel,iterations=1)
        plate_gray=cv2.cvtColor(plate,cv2.COLOR_BGR2GRAY)
        (thresh,plate)=cv2.threshold(plate_gray,127,255,cv2.THRESH_BINARY)
        #read the text on the plate
        read=pytesseract.image_to_string(plate)
        read=''.join(e for e in read if e.isalnum())
        stat=read[0:2]
        cv2.rectangle(img,(x,y),(x+w,y+h),(51,51,255),2)
        cv2.rectangle(img,(x-1,y-40),(x+w+1,y),(51,51,255),-1)
        cv2.putText(img,read,(x,y-10),cv2.FONT_HERSHEY_SIMPLEX,0.9,(255,255,255),2)

        cv2.imshow("plate",plate)
        
    cv2.imwrite("Result.png",img)
    cv2.imshow("Result",img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

extract_num('car_img.png')

Source https://stackoverflow.com/questions/70941904

QUESTION

How to loop this type of Json Data in Angular front end

Asked 2021-Nov-03 at 14:04

Please help me out to loop this below data in the 'Select dropdown' in Angular 10. I want to show all the States in India and its districts. I got this data from the net, but no idea to list them on the front end.

                {
                "AN": {
                    "name": "Andaman and Nicobar Island (UT)",
                    "capital": "Port Blair",
                    "districts": {
                        "1": "Nicobar",
                        "2": "North and Middle Andaman",
                        "3": "South Andaman"
                    }
                },
                "AP": {
                    "name": "Andhra Pradesh",
                    "capital": "Amaravati",
                    "districts": {
                        "1": "Anantapur",
                        "2": "Chittoor",
                        "3": "East Godavari",
                        "4": "Guntur",
                        "5": "Krishna",
                        "6": "Kurnool",
                        "7": "Prakasam",
                        "8": "Srikakulam",
                        "9": "Sri Potti Sriramulu Nellore",
                        "10": "Visakhapatnam",
                        "11": "Vizianagaram",
                        "12": "West Godavari",
                        "13": "YSR District, Kadapa (Cuddapah)"
                    }
                },}

ANSWER

Answered 2021-Nov-03 at 13:53

One of the solutions could be this.

data = {
    "AN": {
        "name": "Andaman and Nicobar Island (UT)",
        "capital": "Port Blair",
        "districts": {
            "1": "Nicobar",
            "2": "North and Middle Andaman",
            "3": "South Andaman"
        }
    },
    "AP": {
        "name": "Andhra Pradesh",
        "capital": "Amaravati",
        "districts": {
            "1": "Anantapur",
            "2": "Chittoor",
            "3": "East Godavari",
            "4": "Guntur",
            "5": "Krishna",
            "6": "Kurnool",
            "7": "Prakasam",
            "8": "Srikakulam",
            "9": "Sri Potti Sriramulu Nellore",
            "10": "Visakhapatnam",
            "11": "Vizianagaram",
            "12": "West Godavari",
            "13": "YSR District, Kadapa (Cuddapah)"
        }
    },
};

constructor() {
  let array = Object.keys(this.data).map(key => this.data[key]);
  console.log(array);
}

Source https://stackoverflow.com/questions/69826012

QUESTION

How can I append other data in a list of dictionaries based on matching key values using Python

Asked 2021-Oct-15 at 08:03

I have a list containing dictionaries. The below piece of code is an example.

[{
    'state': 'Andaman and Nicobar Islands',
    'district': 'Nicobars',
    'chartdata': [{
        'name': 'No of Confirmed Cases',
        'data': [4982, 4983, 4988, 4989, 4991, 4991, 4992, 4993, 4993, 4994, 4994, 4994, 4994, 4994, 4994, 4994, 4994, 4994, 4994, 4994, 4997, 5000, 5001, 5005, 5007, 5007, 5007, 5007, 5009, 5009, 5009, 5013, 5014, 5014, 5014, 5014, 5014, 5015, 5016, 5016, 5016, 5017, 5018, 5020, 5020, 5020, 5022, 5024, 5024, 5024, 5024, 5026, 5028, 5028, 5028, 5029, 5030, 5031, 5031, 5032, 5035, 5036, 5038, 5038, 5039, 5039, 5039, 5041, 5042, 5043, 5044, 5046, 5052, 5081, 5083, 5084, 5084, 5098, 5109, 5116, 5123, 5131, 5149, 5161, 5175, 5190, 5201, 5209, 5247, 5262, 5289, 5331, 5390, 5421, 5466, 5490, 5527, 5569, 5614, 5665, 5716, 5764, 5816, 5875, 5949, 6046, 6084, 6150, 6181, 6223, 6255, 6311, 6341, 6367, 6398, 6426, 6470, 6510, 6542, 6568, 6603, 6638, 6674, 6709, 6758, 6789, 6820, 6844, 6853, 6878, 6901, 6917, 6936, 6964, 6984, 7005, 7018, 7043, 7070, 7088, 7105, 7119, 7131, 7154, 7168, 7197, 7233, 7243, 7261, 7269, 7280, 7306, 7335, 7363, 7394, 7406, 7415, 7425, 7438, 7440, 7441, 7446, 7455, 7462, 7466, 7467, 7469, 7475, 7478, 7482, 7482, 7487, 7491, 7492, 7492, 7492, 7492, 7493, 7496, 7499, 7501, 7505, 7506, 7510, 7516, 7518, 7521, 7525, 7525, 7525, 7529, 7530, 7530, 7531, 7534, 7535, 7537, 7539, 7539, 7540, 7541, 7541, 7541, 7544, 7546, 7546, 7548, 7548, 7548, 7548, 7548, 7548, 7549, 7549, 7550, 7553, 7556, 7557, 7557, 7559, 7560, 7560, 7560, 7560, 7560, 7564, 7565, 7566, 7566, 7567, 7570, 7572, 7572, 7572, 7573, 7576, 7577, 7579, 7580, 7582, 7584, 7592, 7592, 7595, 7596, 7598, 7600, 7602, 7607, 7615, 7616, 7617, 7618, 7618, 7619, 7620, 7620, 7621, 7621, 7622, 7623, 7624, 7626, 7627, 7629, 7629, 7632, 7633, 7634, 7635, 7637, 7637]
    }, {
        'name': 'No of Vaccinations',
        'data': []
    }]
}, {
    'state': 'Andhra Pradesh',
    'district': 'Anantapur',
    'chartdata': [{
        'name': 'No of Confirmed Cases',
        'data': [67553, 67558, 67558, 67562, 67570, 67578, 67593, 67604, 67614, 67614, 67621, 67629, 67633, 67638, 67641, 67644, 67648, 67651, 67656, 67661, 67667, 67673, 67676, 67677, 67678, 67680, 67683, 67689, 67689, 67695, 67697, 67698, 67702, 67704, 67715, 67718, 67721, 67722, 67724, 67735, 67740, 67746, 67753, 67757, 67760, 67770, 67781, 67785, 67795, 67801, 67815, 67815, 67819, 67823, 67829, 67841, 67848, 67857, 67866, 67891, 67906, 67919, 67924, 67946, 67986, 68012, 68041, 68077, 68133, 68179, 68214, 68250, 68307, 68356, 68422, 68483, 68509, 68545, 68604, 68627, 68698, 68900, 69031, 69198, 69331, 69540, 69656, 69784, 70081, 70415, 70728, 71148, 71453, 71609, 71884, 72733, 73522, 74723, 75789, 76884, 77279, 77981, 79272, 80810, 82692, 84414, 85717, 86875, 89043, 91347, 93218, 94997, 96738, 98777, 99416, 101408, 103593, 105673, 107886, 110861, 114217, 116311, 119115, 121449, 123243, 125061, 126848, 128694, 129741, 130775, 132651, 134123, 135315, 136539, 137754, 138298, 139251, 140757, 141798, 142663, 143665, 144660, 145195, 146113, 147073, 147979, 148677, 149227, 149594, 149866, 150219, 150646, 151005, 151321, 151647, 152033, 152161, 152425, 152661, 152944, 153168, 153348, 153536, 153602, 153792, 153957, 154098, 154228, 154328, 154465, 154525, 154616, 154718, 154813, 154898, 154982, 155088, 155125, 155206, 155275, 155355, 155407, 155480, 155578, 155614, 155687, 155756, 155825, 155870, 155937, 155978, 155994, 156043, 156113, 156174, 156225, 156272, 156331, 156349, 156379, 156424, 156460, 156494, 156536, 156578, 156594, 156622, 156673, 156713, 156740, 156771, 156794, 156799, 156809, 156838, 156859, 156890, 156907, 156936, 156947, 156978, 157004, 157030, 157056, 157068, 157103, 157105, 157126, 157166, 157188, 157212, 157233, 157250, 157253, 157270, 157296, 157319, 157353, 157371, 157390, 157397, 157413, 157441, 157461, 157479, 157497, 157507, 157508, 157516, 157520, 157526, 157539, 157550, 157563, 157571, 157583, 157597, 157606, 157611, 157618, 157619, 157620, 157629, 157641, 157664, 157673, 157680, 157689, 157691, 157706, 157709, 157709]
    }, {
        'name': 'No of Vaccinations',
        'data': []
    }]
}]

What I am trying to do is add another list to the empty list in the above code if the state/district matches.

{
        'name': 'No of Vaccinations',
        'data': []
    }

from another dataset using loop I have extracted the following item . and the list in the item needs to be added to the empty vaccine list in the first sample

1
Andaman and Nicobar Islands
Nicobars
[67553, 67558, 67558, 67562, 67570, 67578, 67593, 67604, 67614, 67614, 67621, 67629]

2
Andhra Pradesh
Anantapur
[0, 0, 0, 0, 0, 0, 0, 0, 0, 33, 33, 244, 244, 390, 390, 392, 392, 719, 719, 719]

Expected outcome is:

[{
    'state': 'Andaman and Nicobar Islands',
    'district': 'Nicobars',
    'chartdata': [{
        'name': 'No of Confirmed Cases',
        'data': [4982, 4983, 4988, 4989, 4991, 4991, 4992, 4993, 4993, 4994, 4994, 4994, 4994, 4994, 4994, 4994, 4994, 4994, 4994, 4994, 4997, 5000, 5001, 5005, 5007, 5007, 5007, 5007, 5009, 5009, 5009, 5013, 5014, 5014, 5014, 5014, 5014, 5015, 5016, 5016, 5016, 5017, 5018, 5020, 5020, 5020, 5022, 5024, 5024, 5024, 5024, 5026, 5028, 5028, 5028, 5029, 5030, 5031, 5031, 5032, 5035, 5036, 5038, 5038, 5039, 5039, 5039, 5041, 5042, 5043, 5044, 5046, 5052, 5081, 5083, 5084, 5084, 5098, 5109, 5116, 5123, 5131, 5149, 5161, 5175, 5190, 5201, 5209, 5247, 5262, 5289, 5331, 5390, 5421, 5466, 5490, 5527, 5569, 5614, 5665, 5716, 5764, 5816, 5875, 5949, 6046, 6084, 6150, 6181, 6223, 6255, 6311, 6341, 6367, 6398, 6426, 6470, 6510, 6542, 6568, 6603, 6638, 6674, 6709, 6758, 6789, 6820, 6844, 6853, 6878, 6901, 6917, 6936, 6964, 6984, 7005, 7018, 7043, 7070, 7088, 7105, 7119, 7131, 7154, 7168, 7197, 7233, 7243, 7261, 7269, 7280, 7306, 7335, 7363, 7394, 7406, 7415, 7425, 7438, 7440, 7441, 7446, 7455, 7462, 7466, 7467, 7469, 7475, 7478, 7482, 7482, 7487, 7491, 7492, 7492, 7492, 7492, 7493, 7496, 7499, 7501, 7505, 7506, 7510, 7516, 7518, 7521, 7525, 7525, 7525, 7529, 7530, 7530, 7531, 7534, 7535, 7537, 7539, 7539, 7540, 7541, 7541, 7541, 7544, 7546, 7546, 7548, 7548, 7548, 7548, 7548, 7548, 7549, 7549, 7550, 7553, 7556, 7557, 7557, 7559, 7560, 7560, 7560, 7560, 7560, 7564, 7565, 7566, 7566, 7567, 7570, 7572, 7572, 7572, 7573, 7576, 7577, 7579, 7580, 7582, 7584, 7592, 7592, 7595, 7596, 7598, 7600, 7602, 7607, 7615, 7616, 7617, 7618, 7618, 7619, 7620, 7620, 7621, 7621, 7622, 7623, 7624, 7626, 7627, 7629, 7629, 7632, 7633, 7634, 7635, 7637, 7637]
    }, {
        'name': 'No of Vaccinations',
        'data': [67553, 67558, 67558, 67562, 67570, 67578, 67593, 67604, 67614, 67614, 67621, 67629]
    }]
}, {
    'state': 'Andhra Pradesh',
    'district': 'Anantapur',
    'chartdata': [{
        'name': 'No of Confirmed Cases',
        'data': [67553, 67558, 67558, 67562, 67570, 67578, 67593, 67604, 67614, 67614, 67621, 67629, 67633, 67638, 67641, 67644, 67648, 67651, 67656, 67661, 67667, 67673, 67676, 67677, 67678, 67680, 67683, 67689, 67689, 67695, 67697, 67698, 67702, 67704, 67715, 67718, 67721, 67722, 67724, 67735, 67740, 67746, 67753, 67757, 67760, 67770, 67781, 67785, 67795, 67801, 67815, 67815, 67819, 67823, 67829, 67841, 67848, 67857, 67866, 67891, 67906, 67919, 67924, 67946, 67986, 68012, 68041, 68077, 68133, 68179, 68214, 68250, 68307, 68356, 68422, 68483, 68509, 68545, 68604, 68627, 68698, 68900, 69031, 69198, 69331, 69540, 69656, 69784, 70081, 70415, 70728, 71148, 71453, 71609, 71884, 72733, 73522, 74723, 75789, 76884, 77279, 77981, 79272, 80810, 82692, 84414, 85717, 86875, 89043, 91347, 93218, 94997, 96738, 98777, 99416, 101408, 103593, 105673, 107886, 110861, 114217, 116311, 119115, 121449, 123243, 125061, 126848, 128694, 129741, 130775, 132651, 134123, 135315, 136539, 137754, 138298, 139251, 140757, 141798, 142663, 143665, 144660, 145195, 146113, 147073, 147979, 148677, 149227, 149594, 149866, 150219, 150646, 151005, 151321, 151647, 152033, 152161, 152425, 152661, 152944, 153168, 153348, 153536, 153602, 153792, 153957, 154098, 154228, 154328, 154465, 154525, 154616, 154718, 154813, 154898, 154982, 155088, 155125, 155206, 155275, 155355, 155407, 155480, 155578, 155614, 155687, 155756, 155825, 155870, 155937, 155978, 155994, 156043, 156113, 156174, 156225, 156272, 156331, 156349, 156379, 156424, 156460, 156494, 156536, 156578, 156594, 156622, 156673, 156713, 156740, 156771, 156794, 156799, 156809, 156838, 156859, 156890, 156907, 156936, 156947, 156978, 157004, 157030, 157056, 157068, 157103, 157105, 157126, 157166, 157188, 157212, 157233, 157250, 157253, 157270, 157296, 157319, 157353, 157371, 157390, 157397, 157413, 157441, 157461, 157479, 157497, 157507, 157508, 157516, 157520, 157526, 157539, 157550, 157563, 157571, 157583, 157597, 157606, 157611, 157618, 157619, 157620, 157629, 157641, 157664, 157673, 157680, 157689, 157691, 157706, 157709, 157709]
    }, {
        'name': 'No of Vaccinations',
        'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 33, 33, 244, 244, 390, 390, 392, 392, 719, 719, 719]
    }]
}]

I am not sure how to do this. Any help will be appreciated.

ANSWER

Answered 2021-Oct-15 at 08:03

Let the dictionary that is meant to be filled be a_dict and the dict that has to be inserted a as b_dict, you can use the following statement for the above mentioned problem,

a_dict[1]['chartdata'][1]['data'] = b_dict

Source https://stackoverflow.com/questions/69581424

QUESTION

Is there a way to have insertion of tags or labels with respect to a certain value range?

Asked 2021-Sep-26 at 14:10

I have this data and would like to insert a new column titled 'Level'. I understand 'insert' is a mode of entering in a new column. I tried an 'if' for the argument 'value', but this is not yielding anything.

Data:

                                         Active  Discharged  Deaths
State/UTs                                                           
Andaman and Nicobar                            6        7437     129
Andhra Pradesh                             14550     1993589   13925
Arunachal Pradesh                            634       52507     267
Assam                                       6415      580491    5710
Bihar                                         55      716048    9656
Chandigarh                                    35       64273     814
Chhattisgarh                                 354      990757   13557
Dadra and Nagar Haveli and Daman and Diu       2       10659       4
Delhi                                        367     1412542   25082
Goa                                          885      170391    3210
Gujarat                                      152      815275   10082
Haryana                                      617      760271    9685
Himachal Pradesh                            1699      209420    3613
Jammu and Kashmir                           1286      320337    4410
Jharkhand                                    126      342716    5133
Karnataka                                  17412     2901299   37426
Kerala                                    239338     3966557   21631
Ladakh                                        54       20327     207
Lakshadweep                                    9       10288      51
Madhya Pradesh                               125      781629   10516
Maharashtra                                51234     6300755  137811
Manipur                                     3180      110602    1802
Meghalaya                                   2104       73711    1329
Mizoram                                    11414       54056     226
Nagaland                                     712       29045     631
Odisha                                      6322      997790    8055
Puducherry                                   914      121452    1818
Punjab                                       326      584079   16444
Rajasthan                                     86      945097    8954
Sikkim                                       913       28968     375
Tamil Nadu                                 16256     2572942   35036
Telengana                                   5505      650453    3886
Tripura                                      691       81866     803
Uttar Pradesh                                227     1686369   22861
Uttarakhand                                  379      335358    7388
West Bengal                                 8480     1525581   18515

code:

data = Table.read_table('IndiaStatus.csv')#.drop('Discharged', 'Discharge Ratio (%)','Total Cases','Active','Deaths')


data2.info()

data3 = data2.set_index("State/UTs")

data3 = data3[["Active","Discharged","Deaths"]]
print(data3)

data3.insert(1, column = "Level", value = "Severe" if data3["Active"] > 91874)

output:

line 49
    data3.insert(1, column = "Level", value = "Severe" if data3["Active"] > 91874)
                                                                                 ^
SyntaxError: invalid syntax

ANSWER

Answered 2021-Sep-26 at 00:18

The SyntaxError is because you need a else condition, so something like value = "Severe", if data3["Active"] > 91874 else 'OTHER' would remove the error. That said, it won't work in this case and return another error of using a Series - in this case data3["Active"] > 91874 - in a if statement.

I believe you can use np.where here

data3.insert(1, column = "Level", 
             value = np.where(data3["Active"] > 91874, "Severe", 'OTHER')

Replace OTHER in the above code by any value you want to assign in the column when the condition data3["Active"] > 91874 is not met

Source https://stackoverflow.com/questions/69330763

QUESTION

How to create multiple pivot_longer() columns in r?

Asked 2021-Jul-11 at 15:42

I am working on data set that has multiple cumulative field cols and multiple daily field cols and by using pivot_longer looking to convert them into:

single/long cumulative field col and
single/long daily field col

Snapshot of Data

"Updated.On","State","First.Dose.Administered","Second.Dose.Administered","Daily_First_dose","Daily_Second_dose"
2021-07-09,"India",297184419,69894633,2071121,1141754
2021-07-09,"Andaman and Nicobar Islands",164079,51646,795,6336
2021-07-09,"Andhra Pradesh",13372990,3379660,25571,11168
2021-07-09,"Arunachal Pradesh",583021,108393,8423,3430
2021-07-09,"Assam",6462249,1328646,52913,7681
2021-07-09,"Bihar",15741778,2638686,262299,55007
2021-07-09,"Chandigarh",483886,110610,4351,3491
2021-07-09,"Chhattisgarh",7438953,1819709,64193,42342
2021-07-09,"Dadra and Nagar Haveli and Daman and Diu",458057,57874,7122,1745
2021-07-09,"Delhi",6761634,2049318,95352,35848

Transformation of cols to:

club First.Dose.Administered, Second.Dose.Administered cols to names_to = "Dose_type", values_to = "Dose_Administered"
club Daily_First_dose, Daily_Second_dose cols to names_to = "Dose_type_daily", values_to = "Dose_Administered_daily"

Desired Data frame Example:

Updated.On State Dose_type                Dose_Administered Dose_type_daily   Dose_Administered_daily 
2021-07-09 India First.Dose.Administered  297184419         Daily_First_dose  2071121
2021-07-09 India Second.Dose.Administered  69894633         Daily_Second_dose 1141754

df can be downloaded from:

library(tidyverse)
library(lubridate)

file_url1 <- "https://raw.githubusercontent.com/johnsnow09/covid19-df_stack-code/main/df_vaccination_june.csv"

df_vaccination <- read.csv(url(file_url1))

df_vaccination <- df_vaccination %>%
  mutate(Updated.On = as.Date(Updated.On))

Attempt: I tried below code but not sure how to create two different values_to columns?

df_vaccination %>% 
  pivot_longer(cols = c(First.Dose.Administered:Second.Dose.Administered, 
                        Daily_First_dose:Daily_Second_dose),
               names_to = c("Dose_type","Dose_type_daily"), 
               values_to = c("Dose_Administered","Dose_Administered_daily"))

ANSWER

Answered 2021-Jul-11 at 14:29

I think you need pivot_longer twice with a proper names_pattern regex argument.

library(tidyverse)

df_vaccination %>%
  pivot_longer(cols = 3:4, names_to = c('dose_adminstered','.value'), 
               names_pattern = '(.*)\\.(Administered)') %>%
  pivot_longer(cols = 3:4, names_to = c('dose_type_daily', '.value'), 
               names_pattern = '(.*)_(.*)$')

#> # A tibble: 3,552 x 6
#>    Updated.On State         dose_adminstered Administered dose_type_daily   dose
#>    <date>     <chr>         <chr>                   <int> <chr>            <int>
#>  1 2021-07-09 India         First.Dose          297184419 Daily_First     2.07e6
#>  2 2021-07-09 India         First.Dose          297184419 Daily_Second    1.14e6
#>  3 2021-07-09 India         Second.Dose          69894633 Daily_First     2.07e6
#>  4 2021-07-09 India         Second.Dose          69894633 Daily_Second    1.14e6
#>  5 2021-07-09 Andaman and ~ First.Dose             164079 Daily_First     7.95e2
#>  6 2021-07-09 Andaman and ~ First.Dose             164079 Daily_Second    6.34e3
#>  7 2021-07-09 Andaman and ~ Second.Dose             51646 Daily_First     7.95e2
#>  8 2021-07-09 Andaman and ~ Second.Dose             51646 Daily_Second    6.34e3
#>  9 2021-07-09 Andhra Prade~ First.Dose           13372990 Daily_First     2.56e4
#> 10 2021-07-09 Andhra Prade~ First.Dose           13372990 Daily_Second    1.12e4
#> # ... with 3,542 more rows

To return only 2 rows per State you may do this workaround

df_vaccination[1:4] %>%
  pivot_longer(cols = 3:4, names_to = c('dose_adminstered','.value'), 
               names_pattern = '(.*)\\.(Administered)') %>%
  cbind(df_vaccination[5:6] %>% pivot_longer(everything(), names_to = c('dose_type_daily', '.value'), 
               names_pattern = '(.*)_(.*)$'))

    Updated.On                                    State dose_adminstered Administered dose_type_daily    dose
1   2021-07-09                                    India       First.Dose    297184419     Daily_First 2071121
2   2021-07-09                                    India      Second.Dose     69894633    Daily_Second 1141754
3   2021-07-09              Andaman and Nicobar Islands       First.Dose       164079     Daily_First     795
4   2021-07-09              Andaman and Nicobar Islands      Second.Dose        51646    Daily_Second    6336
5   2021-07-09                           Andhra Pradesh       First.Dose     13372990     Daily_First   25571
6   2021-07-09                           Andhra Pradesh      Second.Dose      3379660    Daily_Second   11168
7   2021-07-09                        Arunachal Pradesh       First.Dose       583021     Daily_First    8423
8   2021-07-09                        Arunachal Pradesh      Second.Dose       108393    Daily_Second    3430
.
.
.

Source https://stackoverflow.com/questions/68336779

QUESTION

How to extract dictionary from JSON data from API? I tried some solutions from Stackoverflow

Asked 2021-Jun-13 at 04:28

I am trying to extract the data. I tried many solutions from Stackoverflow, but I failed to extract state_id and state_name. I know many similar questions have been answered but I am not able to do it.

Here you can get the json data:

import requests
import json

my_headers = 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36 RuxitSynthetic/1.0 v10151387278 t38550 ath9b965f92 altpub cvcv=2'
response = requests.get("https://cdn-api.co-vin.in/api/v2/admin/location/states",headers = {'User-Agent':my_headers})

values=json.loads(response.text)

It's output is like:

{'states': [{'state_id': 1, 'state_name': 'Andaman and Nicobar Islands'}, {'state_id': 2, 'state_name': 'Andhra Pradesh'}, {'state_id': 3, 'state_name': 'Arunachal Pradesh'}, {'state_id': 4, 'state_name': 'Assam'}, {'state_id': 5, 'state_name': 'Bihar'}, {'state_id': 6, 'state_name': 'Chandigarh'}, {'state_id': 7, 'state_name': 'Chhattisgarh'}, {'state_id': 8, 'state_name': 'Dadra and Nagar Haveli'}, {'state_id': 37, 'state_name': 'Daman and Diu'}, {'state_id': 9, 'state_name': 'Delhi'}, {'state_id': 10, 'state_name': 'Goa'}, {'state_id': 11, 'state_name': 'Gujarat'}, {'state_id': 12, 'state_name': 'Haryana'}, {'state_id': 13, 'state_name': 'Himachal Pradesh'}, {'state_id': 14, 'state_name': 'Jammu and Kashmir'}, {'state_id': 15, 'state_name': 'Jharkhand'}, {'state_id': 16, 'state_name': 'Karnataka'}, {'state_id': 17, 'state_name': 'Kerala'}, {'state_id': 18, 'state_name': 'Ladakh'}, {'state_id': 19, 'state_name': 'Lakshadweep'}, {'state_id': 20, 'state_name': 'Madhya Pradesh'}, {'state_id': 21, 'state_name': 'Maharashtra'}, {'state_id': 22, 'state_name': 'Manipur'}, {'state_id': 23, 'state_name': 'Meghalaya'}, {'state_id': 24, 'state_name': 'Mizoram'}, {'state_id': 25, 'state_name': 'Nagaland'}, {'state_id': 26, 'state_name': 'Odisha'}, {'state_id': 27, 'state_name': 'Puducherry'}, {'state_id': 28, 'state_name': 'Punjab'}, {'state_id': 29, 'state_name': 'Rajasthan'}, {'state_id': 30, 'state_name': 'Sikkim'}, {'state_id': 31, 'state_name': 'Tamil Nadu'}, {'state_id': 32, 'state_name': 'Telangana'}, {'state_id': 33, 'state_name': 'Tripura'}, {'state_id': 34, 'state_name': 'Uttar Pradesh'}, {'state_id': 35, 'state_name': 'Uttarakhand'}, {'state_id': 36, 'state_name': 'West Bengal'}], 'ttl': 24}

ANSWER

Answered 2021-Jun-13 at 04:28

The dictionary is actually quite simple. We have the key states, and it's value is a list containing sub dictionaries. If we iterate through this list we can easily index state_id and state_name. Now I don't know how you want to store this gathered data, so I assumed it would be best to put each group of "state_id" & "state_name" in a tuple.

data = {'states': [{'state_id': 1, 'state_name': 'Andaman and Nicobar Islands'}, {'state_id': 2, 'state_name': 'Andhra Pradesh'}, {'state_id': 3, 'state_name': 'Arunachal Pradesh'}]}
gathered = [(info['state_id'],info['state_name']) for info in data['states']]

output

[(1, 'Andaman and Nicobar Islands'), (2, 'Andhra Pradesh'), (3, 'Arunachal Pradesh')]

Source https://stackoverflow.com/questions/67954827

QUESTION

How to merge/ add columns to dataframes in pandas when the joining column has slight spelling differences?

Asked 2021-Jun-02 at 04:42

So I have a data frame like this

   Rank        State/Union territory  NSDP Per Capita (Nominal)(2019–20)[1][2]  state_id
0     1                          Goa                                  466585.0      30.0
1     2                       Sikkim                                  425656.0      11.0
2     3                        Delhi                                  376143.0       NaN
3     4                   Chandigarh                                       NaN       4.0
4     5                      Haryana                                  247207.0       6.0
5     6                    Telangana                                  225756.0       0.0
6     7                    Karnataka                                  223246.0      29.0
7     8                       Kerala                                  221904.0      32.0
8     9                   Puducherry                                  220949.0      34.0
9    10  Andaman and Nicobar Islands                                  219842.0       NaN
10   11                   Tamil Nadu                                  218599.0      33.0
11   12                      Gujarat                                  216329.0      24.0
12   13                      Mizoram                                  204018.0      15.0
13   14                  Uttarakhand                                  202895.0       5.0
14   15                  Maharashtra                                  202130.0      27.0
15   16             Himachal Pradesh                                  190255.0       2.0
16   17               Andhra Pradesh                                  168480.0      28.0
17   18            Arunachal Pradesh                                  164615.0       NaN
18   19                       Punjab                                  161083.0       3.0
20   20                     Nagaland                                  130282.0      13.0
21   21                      Tripura                                  125630.0      16.0
22   22                    Rajasthan                                  115492.0       8.0
23   23                  West Bengal                                  115348.0      19.0
24   24                       Odisha                                   98896.0      21.0
25   25                 Chhattisgarh                                  105281.0      22.0
26   26            Jammu and Kashmir                                  102882.0       NaN
27   27               Madhya Pradesh                                  103288.0      23.0
28   28                    Meghalaya                                   92174.0      17.0
29   29                        Assam                                   90758.0      18.0
30   30                      Manipur                                   84746.0      14.0
31   31                    Jharkhand                                   79873.0      20.0
32   32                Uttar Pradesh                                   65704.0       9.0
33   33                        Bihar                                   46664.0      10.0

And my other dictionary has

{'Telangana': 0, 'Andaman & Nicobar Island': 35, 'Andhra Pradesh': 28, 'Arunanchal Pradesh': 12, 'Assam': 18, 'Bihar': 10, 'Chhattisgarh': 22, 'Daman
& Diu': 25, 'Goa': 30, 'Gujarat': 24, 'Haryana': 6, 'Himachal Pradesh': 2, 'Jammu & Kashmir': 1, 'Jharkhand': 20, 'Karnataka': 29, 'Kerala': 32, 'Lakshadweep': 31, 'Madhya Pradesh': 23, 'Maharashtra': 27, 'Manipur': 14, 'Chandigarh': 4, 'Puducherry': 34, 'Punjab': 3, 'Rajasthan': 8, 'Sikkim': 11, 'Tamil Nadu': 33, 'Tripura': 16, 'Uttar Pradesh': 9, 'Uttarakhand': 5, 'West Bengal': 19, 'Odisha': 21, 'Dadara & Nagar Havelli': 26, 'Meghalaya': 17, 'Mizoram': 15, 'Nagaland': 13, 'NCT of Delhi': 7}

So you have probably seen the problem, Andaman and Nicobar Islands is present in both but spelled differently, like ' Andaman & Nicobar Island' in the dictionary. And this makes the final column NaN
9 10 Andaman and Nicobar Islands 219842.0 NaN

How can I combine this with difflib library?

I've tried

df_19_20['State/Union territory'] = df_19_20['State/Union territory'].apply(get_close_matches(df_19_20['State/Union territory'], id_d.keys()))

and

df_19_20['State/Union territory'] = get_close_matches(df_19_20['State/Union territory'], id_d.keys())

Is there something I'm missing? How can I process the columns to get the best match?

ANSWER

Answered 2021-Jun-01 at 18:37

The issue was in the application of df.apply

df.apply needs to be given a function taking in the value from each row it is being iterated over. You also need to clean the return of get_close_matches which returns a list, so you need to take the first element

df_19_20['State/Union territory'].apply(lambda x: get_close_matches(x, id_d.keys())[0])

should work

Source https://stackoverflow.com/questions/67794098

QUESTION

How to get data from an api in r?

Asked 2021-May-25 at 20:12

I am new to api stuff and have come across a small piece of code in python to retrieve data which I would like to replicate in r:

python code:

import requests
import json
from datetime import date
import time
import smtplib, ssl

#API URL
url = 'http://cdn-api.co-vin.in/api/v2/admin/location/states'
headers = {'accept': 'application/json','Accept-Language' : 'hi_IN','User-Agent': 'Mozilla/4.0'}
result = requests.get(url, headers=headers)
#Print state ID
print(result.content.decode())

Results:

{"states":[{"state_id":1,"state_name":"Andaman and Nicobar Islands"},{"state_id":2,"state_name":"Andhra Pradesh"},{"state_id":3,"state_name":"Arunachal Pradesh"},{"state_id":4,"state_name":"Assam"},{"state_id":5,"state_name":"Bihar"},{"state_id":6,"state_name":"Chandigarh"},{"state_id":7,"state_name":"Chhattisgarh"},{"state_id":8,"state_name":"Dadra and Nagar Haveli"},{"state_id":37,"state_name":"Daman and Diu"},{"state_id":9,"state_name":"Delhi"},{"state_id":10,"state_name":"Goa"},{"state_id":11,"state_name":"Gujarat"},{"state_id":12,"state_name":"Haryana"},{"state_id":13,"state_name":"Himachal Pradesh"},{"state_id":14,"state_name":"Jammu and Bengal"}],"ttl":24}

Info about API:

url: 'http://cdn-api.co-vin.in/api/v2/admin/location/states'

from: https://apisetu.gov.in/public/marketplace/api/cowin#/Metadata%20APIs/states

from: https://github.com/cowinapi/developer.cowin/issues/339

(Note: cowin API are restricted to be accessed only in INDIA. So I guess many of you will not be able to use it. But it will be still helpful if you could suggest some code changes.)

I have googled abit and tried below pieces of code but none of them worked so far:

library(tidyverse)
library(rjson)
library(jsonlite)
library(RCurl)
library(httr)

states_url = 'http://cdn-api.co-vin.in/api/v2/admin/location/states'

headers = c({'accept' = 'application/json'},
            {'Accept-Language' = 'hi_IN'},
            {'User-Agent' = 'Mozilla/4.0'})

url(states_url, headers = headers)

GET(states_url)$content

GET(states_url)$headers

Update

I have tried this and it didnt give error but not sure what to do next:

states_url = 'http://cdn-api.co-vin.in/api/v2/admin/location/states'

headers = c('accept' = {'application/json'},
            'Accept-Language' = {'hi_IN'},
            'User-Agent' = {'Mozilla/4.0'})

url(states_url, headers = headers)

A connection with
description "http://cdn-api.co-vin.in/api/v2/admin/location/states" class "url-wininet"
mode "r"
text "text"
opened "closed"
can read "yes"
can write "no"

GET(states_url, header = headers)$content

1 3c 21 44 4f 43 54 59 50 45 20 48 54 4d 4c 20 50 55 42 4c [20] 49 43 20 22 2d 2f 2f 57 33 43 2f 2f 44 54 44 20 48 54 4d [39] 4c 20 34 2e 30 31 20 54 72 61 6e 73 69 74 69 6f 6e 61 6c [58] 2f 2f 45 4e 22 20 22 68 74 74 70 3a 2f 2f 77 77 77 2e 77 [77] 33 2e 6f 72 67 2f 54 52 2f 68 74 6d 6c 34 2f 6c 6f 6f 73 [96] 65 2e 64 74 64 22 3e 0a 3c 48 54 4d 4c 3e 3c 48 45 41 44 [115] 3e 3c 4d 45 54 41 20 48 54 54 50 2d 45 51 55 49 56 3d 22 [134] 43 6f 6e 74 65 6e 74 2d 54 79 70 65 22 20 43 4f 4e 54 45 [153] 4e 54 3d 22 74 65 78 74 2f 68 74 6d 6c 3b 20 63 68 61 7

str(GET(states_url))

List of 10
 $ url        : chr "http://cdn-api.co-vin.in/api/v2/admin/location/states"
 $ status_code: int 403
 $ headers    :List of 9
  ..$ server        : chr "CloudFront"
  ..$ date          : chr "Tue, 25 May 2021 12:39:02 GMT"
  ..$ content-type  : chr "text/html"
  ..$ content-length: chr "919"
  ..$ connection    : chr "keep-alive"
  ..$ x-cache       : chr "Error from cloudfront"
  ..$ via           : chr "1.1 85ad220378d99bdabeb6c46016f1cf16.cloudfront.net (CloudFront)"
  ..$ x-amz-cf-pop  : chr "BOM51-C1"
  ..$ x-amz-cf-id   : chr "eeJq5ZtSJHZkLGoJZBTUL2xL5PcU2gjesnY7Qmg_kMnxZxZ1JUHPWA=="
  ..- attr(*, "class")= chr [1:2] "insensitive" "list"
 $ all_headers:List of 1
  ..$ :List of 3
  .. ..$ status : int 403
  .. ..$ version: chr "HTTP/1.1"
  .. ..$ headers:List of 9
  .. .. ..$ server        : chr "CloudFront"
  .. .. ..$ date          : chr "Tue, 25 May 2021 12:39:02 GMT"
  .. .. ..$ content-type  : chr "text/html"
  .. .. ..$ content-length: chr "919"
  .. .. ..$ connection    : chr "keep-alive"
  .. .. ..$ x-cache       : chr "Error from cloudfront"
  .. .. ..$ via           : chr "1.1 85ad220378d99bdabeb6c46016f1cf16.cloudfront.net (CloudFront)"
  .. .. ..$ x-amz-cf-pop  : chr "BOM51-C1"
  .. .. ..$ x-amz-cf-id   : chr "eeJq5ZtSJHZkLGoJZBTUL2xL5PcU2gjesnY7Qmg_kMnxZxZ1JUHPWA=="
  .. .. ..- attr(*, "class")= chr [1:2] "insensitive" "list"
 $ cookies    :'data.frame':    0 obs. of  7 variables:
  ..$ domain    : logi(0) 
  ..$ flag      : logi(0) 
  ..$ path      : logi(0) 
  ..$ secure    : logi(0) 
  ..$ expiration: 'POSIXct' num(0) 
  ..$ name      : logi(0) 
  ..$ value     : logi(0) 
 $ content    : raw [1:919] 3c 21 44 4f ...
 $ date       : POSIXct[1:1], format: "2021-05-25 12:39:02"
 $ times      : Named num [1:6] 0 0.242 0.283 0.284 0.321 ...
  ..- attr(*, "names")= chr [1:6] "redirect" "namelookup" "connect" "pretransfer" ...
 $ request    :List of 7
  ..$ method    : chr "GET"
  ..$ url       : chr "http://cdn-api.co-vin.in/api/v2/admin/location/states"
  ..$ headers   : Named chr "application/json, text/xml, application/xml, */*"
  .. ..- attr(*, "names")= chr "Accept"
  ..$ fields    : NULL
  ..$ options   :List of 2
  .. ..$ useragent: chr "libcurl/7.64.1 r-curl/4.3.1 httr/1.4.2"
  .. ..$ httpget  : logi TRUE
  ..$ auth_token: NULL
  ..$ output    : list()
  .. ..- attr(*, "class")= chr [1:2] "write_memory" "write_function"
  ..- attr(*, "class")= chr "request"
 $ handle     :Class 'curl_handle' <externalptr> 
 - attr(*, "class")= chr "response"
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http_status(GET(states_url))

$category
[1] "Client error"

$reason
[1] "Forbidden"

$message
[1] "Client error: (403) Forbidden"

stringi::stri_enc_detect(GET(states_url, header = headers)$content)

[[1]]
     Encoding Language Confidence
1  ISO-8859-1       en       0.54
2  ISO-8859-2       ro       0.26
3       UTF-8                0.15
4    UTF-16BE                0.10
5    UTF-16LE                0.10
6   Shift_JIS       ja       0.10
7     GB18030       zh       0.10
8      EUC-JP       ja       0.10
9      EUC-KR       ko       0.10
10       Big5       zh       0.10
11 ISO-8859-9       tr       0.06
12 IBM424_rtl       he       0.02
13 IBM424_ltr       he       0.01

content(GET(states_url, header = headers), encoding = "UTF-8")

{html_document}
<html>
[1] <head>\n<meta http-equiv="Content-Type" content="text/htm ...
[2] <body>\n<h1>403 ERROR</h1>\n<h2>The request could not be  ...

content(GET(states_url, header = headers), encoding = "ISO-8859-1")

{html_document}
<html>
[1] <head>\n<meta http-equiv="Content-Type" content="text/htm ...
[2] <body>\n<h1>403 ERROR</h1>\n<h2>The request could not be  ...

python code image: