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atmosphere | Realtime Client Server Framework | Websocket library

 by   Atmosphere Java Version: Current License: No License

 by   Atmosphere Java Version: Current License: No License

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kandi X-RAY | atmosphere Summary

atmosphere is a Java library typically used in Networking, Websocket, Spring applications. atmosphere has no bugs, it has no vulnerabilities, it has build file available and it has high support. You can download it from GitHub, Maven.
The Atmosphere Framework contains client and server side components for building Asynchronous Web Applications. Atmosphere transparently supports WebSockets, Server Sent Events (SSE), Long-Polling, HTTP Streaming (Forever frame) and JSONP.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • atmosphere has a highly active ecosystem.
  • It has 3565 star(s) with 757 fork(s). There are 247 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 37 open issues and 2066 have been closed. On average issues are closed in 72 days. There are 1 open pull requests and 0 closed requests.
  • It has a negative sentiment in the developer community.
  • The latest version of atmosphere is current.
atmosphere Support
Best in #Websocket
Average in #Websocket
atmosphere Support
Best in #Websocket
Average in #Websocket

quality kandi Quality

  • atmosphere has 0 bugs and 0 code smells.
atmosphere Quality
Best in #Websocket
Average in #Websocket
atmosphere Quality
Best in #Websocket
Average in #Websocket

securitySecurity

  • atmosphere has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • atmosphere code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
atmosphere Security
Best in #Websocket
Average in #Websocket
atmosphere Security
Best in #Websocket
Average in #Websocket

license License

  • atmosphere does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
atmosphere License
Best in #Websocket
Average in #Websocket
atmosphere License
Best in #Websocket
Average in #Websocket

buildReuse

  • atmosphere 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.
  • It has 43493 lines of code, 4089 functions and 438 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
atmosphere Reuse
Best in #Websocket
Average in #Websocket
atmosphere Reuse
Best in #Websocket
Average in #Websocket
Top functions reviewed by kandi - BETA

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

  • Pushes a deliver message .
  • Create a filter from a method
  • Invokes a setter on a property .
  • Loads the AtmosphereHandler from the given stream .
  • On notification handler .
  • Handles a state change .
  • Configures the given WebSocket connection to the given WebSocket .
  • Initialize the servlets .
  • Escapes the Java style string using Java style encoding .
  • Parse the name .

atmosphere Key Features

Realtime Client Server Framework for the JVM, supporting WebSockets with Cross-Browser Fallbacks

To use Atmosphere, add the following dependency:

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     <dependency>
         <groupId>org.atmosphere</groupId>
         <artifactId>atmosphere-{atmosphere-module}</artifactId>
         <version>2.7.5</version> // MUST BE USED with atmosphere-javascript 3.1+
      </dependency>

Save dictionary to Pandas dataframe with keys as columns and merge indices

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d = {'atmosphere':pd.DataFrame({0: {2: 5, 9: 4, 15: 1, 26: 5, 29: 5, 
                                    2621: 4, 6419: 3}}),
     'communication':pd.DataFrame({0: {13: 1, 15: 1, 26: 1, 2621: 2,
                                       3119: 5, 6419: 4, 6532: 1}})}

print (d['atmosphere'])
      0
2     5
9     4
15    1
26    5
29    5
2621  4
6419  3

print (d['communication'])
      0
13    1
15    1
26    1
2621  2
3119  5
6419  4
6532  1
df = pd.concat(d, axis=1).droplevel(1, axis=1)
print (df)
      atmosphere  communication
2            5.0            NaN
9            4.0            NaN
13           NaN            1.0
15           1.0            1.0
26           5.0            1.0
29           5.0            NaN
2621         4.0            2.0
3119         NaN            5.0
6419         3.0            4.0
6532         NaN            1.0
df = pd.concat({k: v[0] for k, v in d.items()}, axis=1)
-----------------------
d = {'atmosphere':pd.DataFrame({0: {2: 5, 9: 4, 15: 1, 26: 5, 29: 5, 
                                    2621: 4, 6419: 3}}),
     'communication':pd.DataFrame({0: {13: 1, 15: 1, 26: 1, 2621: 2,
                                       3119: 5, 6419: 4, 6532: 1}})}

print (d['atmosphere'])
      0
2     5
9     4
15    1
26    5
29    5
2621  4
6419  3

print (d['communication'])
      0
13    1
15    1
26    1
2621  2
3119  5
6419  4
6532  1
df = pd.concat(d, axis=1).droplevel(1, axis=1)
print (df)
      atmosphere  communication
2            5.0            NaN
9            4.0            NaN
13           NaN            1.0
15           1.0            1.0
26           5.0            1.0
29           5.0            NaN
2621         4.0            2.0
3119         NaN            5.0
6419         3.0            4.0
6532         NaN            1.0
df = pd.concat({k: v[0] for k, v in d.items()}, axis=1)
-----------------------
d = {'atmosphere':pd.DataFrame({0: {2: 5, 9: 4, 15: 1, 26: 5, 29: 5, 
                                    2621: 4, 6419: 3}}),
     'communication':pd.DataFrame({0: {13: 1, 15: 1, 26: 1, 2621: 2,
                                       3119: 5, 6419: 4, 6532: 1}})}

print (d['atmosphere'])
      0
2     5
9     4
15    1
26    5
29    5
2621  4
6419  3

print (d['communication'])
      0
13    1
15    1
26    1
2621  2
3119  5
6419  4
6532  1
df = pd.concat(d, axis=1).droplevel(1, axis=1)
print (df)
      atmosphere  communication
2            5.0            NaN
9            4.0            NaN
13           NaN            1.0
15           1.0            1.0
26           5.0            1.0
29           5.0            NaN
2621         4.0            2.0
3119         NaN            5.0
6419         3.0            4.0
6532         NaN            1.0
df = pd.concat({k: v[0] for k, v in d.items()}, axis=1)
-----------------------
out = pd.concat(d.values(), axis=1).set_axis(d, axis=1)
      atmosphere  communication
2            5.0            NaN
9            4.0            NaN
13           NaN            1.0
15           1.0            1.0
26           5.0            1.0
29           5.0            NaN
2621         4.0            2.0
3119         NaN            5.0
6419         3.0            4.0
6532         NaN            1.0
-----------------------
out = pd.concat(d.values(), axis=1).set_axis(d, axis=1)
      atmosphere  communication
2            5.0            NaN
9            4.0            NaN
13           NaN            1.0
15           1.0            1.0
26           5.0            1.0
29           5.0            NaN
2621         4.0            2.0
3119         NaN            5.0
6419         3.0            4.0
6532         NaN            1.0

Swift how to represent standard atmosphere pressure for unit conversion

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extension UnitPressure {
    static let standardAtmospheres = UnitPressure(
        symbol: "atm", 
        converter: UnitConverterLinear(coefficient: 101325)
    )
}

Measurement(value: 1, unit: UnitPressure.bars)
    .converted(to: .standardAtmospheres) // 0.9869232667160128 atm
Measurement(value: 1, unit: UnitPressure.standardAtmospheres)
    .converted(to: .bars) // 1.01325 bar

React Styled Components - How to change color of clicked element in map function?

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//className props of NumberCircle component

className={toggle.id===tech.id?'selected':''} 
style={toggle.id===tech.id?{ backgroundColor: 'blue'}:{}}
-----------------------
//className props of NumberCircle component

className={toggle.id===tech.id?'selected':''} 
style={toggle.id===tech.id?{ backgroundColor: 'blue'}:{}}

React - How to pass image url from data map to component?

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const [ toggle, setToggle ] = useState({
  name: "",
  images: {
    png: somePlaceholderImage
  }
});

return (
  <Wrapper>
    {/* <Title>...</Title> */}

    <ImageContainer>
      <img src={toggle.images.png} alt="planet" />
    </ImageContainer>

    <PlanetChoose>
      {data.destinations.map(dest => (
        <p
          key={dest.name} 
          onClick={() => setToggle(dest)}
        >{dest.name}</p>
      ))}
    </PlanetChoose>

    <h1>{toggle.name}</h1>
  </Wrapper>
);
-----------------------
import images from '../assets/destination';
<img src={images["image-moon.png"]} alt="planet" />
import React, { useState, useEffect, useRef, useMemo } from "react";
import styled from "styled-components";
import backgroundImage from "../assets/destination/background-destination-mobile.jpg";
import { data } from "../data/data";
import images from '../assets/destination';

function Destination() {
  const [ toggle, setToggle ] = useState("");

  const imageName = useMemo(() => 
    toggle.length ? `image-${toggle.toLocaleLowerCase()}.png` : ""
  , [toggle]);

  return (
    <Wrapper>
      <Title>
        <p style={{ marginRight: "10px", color: "grey", fontWeight: "bold" }}>
          01
        </p>
        <p>PICK YOUR DESTINATION</p>
      </Title>

      <ImageContainer>
//HERE I WANT TO PASS URL FROM MAP
        {imageName.length && <img src={images[imageName]} alt="planet" />}
      </ImageContainer>

      <PlanetChoose>
        {data.destinations.map(({ name, images }) => (
          <p onClick={() => setToggle(name)}>{name}</p>
        )}
      </PlanetChoose>
      <h1>{toggle}</h1>
    </Wrapper>
  );
}
-----------------------
import images from '../assets/destination';
<img src={images["image-moon.png"]} alt="planet" />
import React, { useState, useEffect, useRef, useMemo } from "react";
import styled from "styled-components";
import backgroundImage from "../assets/destination/background-destination-mobile.jpg";
import { data } from "../data/data";
import images from '../assets/destination';

function Destination() {
  const [ toggle, setToggle ] = useState("");

  const imageName = useMemo(() => 
    toggle.length ? `image-${toggle.toLocaleLowerCase()}.png` : ""
  , [toggle]);

  return (
    <Wrapper>
      <Title>
        <p style={{ marginRight: "10px", color: "grey", fontWeight: "bold" }}>
          01
        </p>
        <p>PICK YOUR DESTINATION</p>
      </Title>

      <ImageContainer>
//HERE I WANT TO PASS URL FROM MAP
        {imageName.length && <img src={images[imageName]} alt="planet" />}
      </ImageContainer>

      <PlanetChoose>
        {data.destinations.map(({ name, images }) => (
          <p onClick={() => setToggle(name)}>{name}</p>
        )}
      </PlanetChoose>
      <h1>{toggle}</h1>
    </Wrapper>
  );
}
-----------------------
import images from '../assets/destination';
<img src={images["image-moon.png"]} alt="planet" />
import React, { useState, useEffect, useRef, useMemo } from "react";
import styled from "styled-components";
import backgroundImage from "../assets/destination/background-destination-mobile.jpg";
import { data } from "../data/data";
import images from '../assets/destination';

function Destination() {
  const [ toggle, setToggle ] = useState("");

  const imageName = useMemo(() => 
    toggle.length ? `image-${toggle.toLocaleLowerCase()}.png` : ""
  , [toggle]);

  return (
    <Wrapper>
      <Title>
        <p style={{ marginRight: "10px", color: "grey", fontWeight: "bold" }}>
          01
        </p>
        <p>PICK YOUR DESTINATION</p>
      </Title>

      <ImageContainer>
//HERE I WANT TO PASS URL FROM MAP
        {imageName.length && <img src={images[imageName]} alt="planet" />}
      </ImageContainer>

      <PlanetChoose>
        {data.destinations.map(({ name, images }) => (
          <p onClick={() => setToggle(name)}>{name}</p>
        )}
      </PlanetChoose>
      <h1>{toggle}</h1>
    </Wrapper>
  );
}

Find substring in pandas dataframe and save in new column

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df = pd.DataFrame({"a":["apple", "orange", "today atmosphere"],
                   "b":["pineapple", "atmosphere humid", "kiwi"],
                   "c":["the atmosphere now", "watermelon", "grapes"]})

                  a                 b                   c
0             apple         pineapple  the atmosphere now
1            orange  atmosphere humid          watermelon
2  today atmosphere              kiwi              grapes


print (df[df.apply(lambda col: col.str.contains('atmosphere', case=False), axis=1)].stack())

0  c    the atmosphere now
1  b      atmosphere humid
2  a      today atmosphere
dtype: object

User logged in/out status set as context, and creating problems when that context is used

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const { activeUser } = useContext(FirebaseContext);
const { uid } = activeUser; // can't destructure from activeUser when null!
console.log(uid);
const { activeUser } = useContext(FirebaseContext);
const { uid } = activeUser || {}; // <-- provide fallback object to destructure from
console.log(uid); // value or undefined, but OK, won't throw error
-----------------------
const { activeUser } = useContext(FirebaseContext);
const { uid } = activeUser; // can't destructure from activeUser when null!
console.log(uid);
const { activeUser } = useContext(FirebaseContext);
const { uid } = activeUser || {}; // <-- provide fallback object to destructure from
console.log(uid); // value or undefined, but OK, won't throw error

Images from json does not appear

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const data = [
    {
        "name": "Mars",
        "image": require("../public/images/test.jpg")
    },
    {
        "name": "Europa",
        "images": {
            "png": require("./assets/destination/image-mars.png"),
            "webp": require("./assets/destination/image-mars.webp")
        }
    }
]

How solve for a variable given a condition in Python?

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pip install sympy
h = Symbol('h')
p = Lambda(h, p_0*exp(-h/lambda_p))
B = Lambda(h, B_0*pow((1 + (h/h_D)), -3))
β = Lambda(h, p(h)/(B(h)**2 / (2*μ_0) ))
βequals1 = β(h) - 1 # by default sympy solves for fn = 0, so fn = 1 needs to be rewritted as fn - 1 = 0
solutions = solveset(βequals1, h)
print(solutions)
import numpy as np
from sympy import Symbol, Lambda, solveset, exp

B_0 = 0.02 # base magnetic field [T]
p_0 = 0.015 # base pressure [J/m^-3]
h_D = 7.5E7 # dipole depth [m]
R_sol = 6.96E8 # solar radius [m]
M_sol = 1.9891E30 # solar mass [kg]
G = 6.673E-11 # gravitational constant [m^3 kg^-1 s^-2]
μ = 0.61 # mean molecular weight of solar corona
m_H = 1.6726E-27 # mass of H particle [kg]
μ_0 = (4*np.pi)*pow(10,-7) # permeability [H/m]
k = 1.3806e-23 # boltzmann constant [J/K]
T = 1e6 # temperature [K]

g = (G*M_sol)/(pow(R_sol,2))
lambda_p = (k*T)/(μ*m_H*g)    


h = Symbol('h')

p = Lambda(h, p_0*exp(-h/lambda_p))
B = Lambda(h, B_0*pow((1 + (h/h_D)), -3))
β = Lambda(h, p(h)/(B(h)**2 / (2*μ_0) ))

βequals1 = β(h) - 1 # by default sympy solves for fn = 0, so fn = 1 needs to be rewritted as fn - 1 = 0
solutions = solveset(βequals1, h) # set the domain parameter if you want also, like this solveset(βequals1, h, domain=S.Reals). Make sure to import S above if you do. S.Complex is default. See all options here: https://docs.sympy.org/latest/modules/sets.html#module-sympy.sets.fancysets
print(solutions)
-----------------------
pip install sympy
h = Symbol('h')
p = Lambda(h, p_0*exp(-h/lambda_p))
B = Lambda(h, B_0*pow((1 + (h/h_D)), -3))
β = Lambda(h, p(h)/(B(h)**2 / (2*μ_0) ))
βequals1 = β(h) - 1 # by default sympy solves for fn = 0, so fn = 1 needs to be rewritted as fn - 1 = 0
solutions = solveset(βequals1, h)
print(solutions)
import numpy as np
from sympy import Symbol, Lambda, solveset, exp

B_0 = 0.02 # base magnetic field [T]
p_0 = 0.015 # base pressure [J/m^-3]
h_D = 7.5E7 # dipole depth [m]
R_sol = 6.96E8 # solar radius [m]
M_sol = 1.9891E30 # solar mass [kg]
G = 6.673E-11 # gravitational constant [m^3 kg^-1 s^-2]
μ = 0.61 # mean molecular weight of solar corona
m_H = 1.6726E-27 # mass of H particle [kg]
μ_0 = (4*np.pi)*pow(10,-7) # permeability [H/m]
k = 1.3806e-23 # boltzmann constant [J/K]
T = 1e6 # temperature [K]

g = (G*M_sol)/(pow(R_sol,2))
lambda_p = (k*T)/(μ*m_H*g)    


h = Symbol('h')

p = Lambda(h, p_0*exp(-h/lambda_p))
B = Lambda(h, B_0*pow((1 + (h/h_D)), -3))
β = Lambda(h, p(h)/(B(h)**2 / (2*μ_0) ))

βequals1 = β(h) - 1 # by default sympy solves for fn = 0, so fn = 1 needs to be rewritted as fn - 1 = 0
solutions = solveset(βequals1, h) # set the domain parameter if you want also, like this solveset(βequals1, h, domain=S.Reals). Make sure to import S above if you do. S.Complex is default. See all options here: https://docs.sympy.org/latest/modules/sets.html#module-sympy.sets.fancysets
print(solutions)
-----------------------
pip install sympy
h = Symbol('h')
p = Lambda(h, p_0*exp(-h/lambda_p))
B = Lambda(h, B_0*pow((1 + (h/h_D)), -3))
β = Lambda(h, p(h)/(B(h)**2 / (2*μ_0) ))
βequals1 = β(h) - 1 # by default sympy solves for fn = 0, so fn = 1 needs to be rewritted as fn - 1 = 0
solutions = solveset(βequals1, h)
print(solutions)
import numpy as np
from sympy import Symbol, Lambda, solveset, exp

B_0 = 0.02 # base magnetic field [T]
p_0 = 0.015 # base pressure [J/m^-3]
h_D = 7.5E7 # dipole depth [m]
R_sol = 6.96E8 # solar radius [m]
M_sol = 1.9891E30 # solar mass [kg]
G = 6.673E-11 # gravitational constant [m^3 kg^-1 s^-2]
μ = 0.61 # mean molecular weight of solar corona
m_H = 1.6726E-27 # mass of H particle [kg]
μ_0 = (4*np.pi)*pow(10,-7) # permeability [H/m]
k = 1.3806e-23 # boltzmann constant [J/K]
T = 1e6 # temperature [K]

g = (G*M_sol)/(pow(R_sol,2))
lambda_p = (k*T)/(μ*m_H*g)    


h = Symbol('h')

p = Lambda(h, p_0*exp(-h/lambda_p))
B = Lambda(h, B_0*pow((1 + (h/h_D)), -3))
β = Lambda(h, p(h)/(B(h)**2 / (2*μ_0) ))

βequals1 = β(h) - 1 # by default sympy solves for fn = 0, so fn = 1 needs to be rewritted as fn - 1 = 0
solutions = solveset(βequals1, h) # set the domain parameter if you want also, like this solveset(βequals1, h, domain=S.Reals). Make sure to import S above if you do. S.Complex is default. See all options here: https://docs.sympy.org/latest/modules/sets.html#module-sympy.sets.fancysets
print(solutions)
-----------------------
pip install sympy
h = Symbol('h')
p = Lambda(h, p_0*exp(-h/lambda_p))
B = Lambda(h, B_0*pow((1 + (h/h_D)), -3))
β = Lambda(h, p(h)/(B(h)**2 / (2*μ_0) ))
βequals1 = β(h) - 1 # by default sympy solves for fn = 0, so fn = 1 needs to be rewritted as fn - 1 = 0
solutions = solveset(βequals1, h)
print(solutions)
import numpy as np
from sympy import Symbol, Lambda, solveset, exp

B_0 = 0.02 # base magnetic field [T]
p_0 = 0.015 # base pressure [J/m^-3]
h_D = 7.5E7 # dipole depth [m]
R_sol = 6.96E8 # solar radius [m]
M_sol = 1.9891E30 # solar mass [kg]
G = 6.673E-11 # gravitational constant [m^3 kg^-1 s^-2]
μ = 0.61 # mean molecular weight of solar corona
m_H = 1.6726E-27 # mass of H particle [kg]
μ_0 = (4*np.pi)*pow(10,-7) # permeability [H/m]
k = 1.3806e-23 # boltzmann constant [J/K]
T = 1e6 # temperature [K]

g = (G*M_sol)/(pow(R_sol,2))
lambda_p = (k*T)/(μ*m_H*g)    


h = Symbol('h')

p = Lambda(h, p_0*exp(-h/lambda_p))
B = Lambda(h, B_0*pow((1 + (h/h_D)), -3))
β = Lambda(h, p(h)/(B(h)**2 / (2*μ_0) ))

βequals1 = β(h) - 1 # by default sympy solves for fn = 0, so fn = 1 needs to be rewritted as fn - 1 = 0
solutions = solveset(βequals1, h) # set the domain parameter if you want also, like this solveset(βequals1, h, domain=S.Reals). Make sure to import S above if you do. S.Complex is default. See all options here: https://docs.sympy.org/latest/modules/sets.html#module-sympy.sets.fancysets
print(solutions)
-----------------------
pip install sympy
h = Symbol('h')
p = Lambda(h, p_0*exp(-h/lambda_p))
B = Lambda(h, B_0*pow((1 + (h/h_D)), -3))
β = Lambda(h, p(h)/(B(h)**2 / (2*μ_0) ))
βequals1 = β(h) - 1 # by default sympy solves for fn = 0, so fn = 1 needs to be rewritted as fn - 1 = 0
solutions = solveset(βequals1, h)
print(solutions)
import numpy as np
from sympy import Symbol, Lambda, solveset, exp

B_0 = 0.02 # base magnetic field [T]
p_0 = 0.015 # base pressure [J/m^-3]
h_D = 7.5E7 # dipole depth [m]
R_sol = 6.96E8 # solar radius [m]
M_sol = 1.9891E30 # solar mass [kg]
G = 6.673E-11 # gravitational constant [m^3 kg^-1 s^-2]
μ = 0.61 # mean molecular weight of solar corona
m_H = 1.6726E-27 # mass of H particle [kg]
μ_0 = (4*np.pi)*pow(10,-7) # permeability [H/m]
k = 1.3806e-23 # boltzmann constant [J/K]
T = 1e6 # temperature [K]

g = (G*M_sol)/(pow(R_sol,2))
lambda_p = (k*T)/(μ*m_H*g)    


h = Symbol('h')

p = Lambda(h, p_0*exp(-h/lambda_p))
B = Lambda(h, B_0*pow((1 + (h/h_D)), -3))
β = Lambda(h, p(h)/(B(h)**2 / (2*μ_0) ))

βequals1 = β(h) - 1 # by default sympy solves for fn = 0, so fn = 1 needs to be rewritted as fn - 1 = 0
solutions = solveset(βequals1, h) # set the domain parameter if you want also, like this solveset(βequals1, h, domain=S.Reals). Make sure to import S above if you do. S.Complex is default. See all options here: https://docs.sympy.org/latest/modules/sets.html#module-sympy.sets.fancysets
print(solutions)

Efficient way to extract data from NETCDF files

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# convert xarray data to a pandas dataframe
def xr_to_df(data):
    data = data.to_dataframe()
    data.reset_index(inplace=True)
    return data

# convert your xarray data to a pandas dataframe
full_df = xr_to_df(full_xarray)

# create a 2 columns pandas dataframe containing your target coordinates
points = pd.DataFrame({'lat':target_lat, 'lon':target_lon})

# get the values at your target points only via merging on the left
subset = pd.merge(points,full_df)
-----------------------
# Make the index on your coordinates DataFrame the station ID,
# then convert to a dataset.
# This results in a Dataset with two DataArrays, lat and lon, each
# of which are indexed by a single dimension, stid
crd_ix = crd.set_index('stid').to_xarray()

# now, select using the arrays, and the data will be re-oriented to have
# the data only for the desired pixels, indexed by 'stid'. The
# non-indexing coordinates lat and lon will be indexed by (stid) as well.
NC.sel(lon=crd_ix.lon, lat=crd_ix.lat, method='nearest')

Scraping Yelp review content displaying different tags using Beautiful Soup

copy iconCopydownload iconDownload
import json

import requests
from bs4 import BeautifulSoup

url = 'https://www.yelp.com/biz/jajaja-plantas-mexicana-new-york-2?osq=Vegetarian+Food'

r = requests.get(
    url,
    headers={'User-Agent':'Mozilla/5.0'},
)

parsed_page = BeautifulSoup(r.text, 'html.parser')

page_jsons = []
for x in parsed_page.select('script[type="application/ld+json"]'):
    try:
        data = json.loads(x.text)
    except:
        continue

    page_jsons.append(data)

for d in page_jsons:
    if d.get('name'):
        print(d['name'])  # => Jajaja Plantas Mexicana

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QUESTION

Save dictionary to Pandas dataframe with keys as columns and merge indices

Asked 2022-Apr-05 at 08:27

I know there are already lots of posts on how to convert a pandas dict to a dataframe, however I could not find one discussing the issue I have. My dictionary looks as follows:

[Out 23]:
{'atmosphere':       0
 2     5
 9     4
 15    1
 26    5
 29    5
 ...  ..
 2621  4
 6419  3
 
 [6934 rows x 1 columns],
 'communication':       0
 13    1
 15    1
 26    1
 2621  2
 3119  5
 ...  ..
 6419  4
 6532  1
 
 [714 rows x 1 columns]

Now, what I want is to create a dataframe out of this dictionary, where the 'atmosphere' and 'communication' are the columns, and the indices of both items are merged, so that the dataframe looks as follows:

index    atmosphere    commmunication
2           5
9           4
13                           1
15          1                1
26          5                1
29          5
2621        4                2
3119                         5
6419        3                4
6532                         1

I already tried pd.DataFrame.from_dict, but it saves all values in one row. Any help is much appreciated!

ANSWER

Answered 2022-Apr-05 at 08:27

Use concat with DataFrame.droplevel for remove second level 0 from MultiIndex in columns:

d = {'atmosphere':pd.DataFrame({0: {2: 5, 9: 4, 15: 1, 26: 5, 29: 5, 
                                    2621: 4, 6419: 3}}),
     'communication':pd.DataFrame({0: {13: 1, 15: 1, 26: 1, 2621: 2,
                                       3119: 5, 6419: 4, 6532: 1}})}

print (d['atmosphere'])
      0
2     5
9     4
15    1
26    5
29    5
2621  4
6419  3

print (d['communication'])
      0
13    1
15    1
26    1
2621  2
3119  5
6419  4
6532  1

df = pd.concat(d, axis=1).droplevel(1, axis=1)
print (df)
      atmosphere  communication
2            5.0            NaN
9            4.0            NaN
13           NaN            1.0
15           1.0            1.0
26           5.0            1.0
29           5.0            NaN
2621         4.0            2.0
3119         NaN            5.0
6419         3.0            4.0
6532         NaN            1.0

Alternative solution:

df = pd.concat({k: v[0] for k, v in d.items()}, axis=1)

Source https://stackoverflow.com/questions/71748131

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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Install atmosphere

The Atmosphere Framework ships with many examples describing how to implement WebSockets, Server-Sent Events, Long-Polling, HTTP Streaming and JSONP client applications. Take a look at this page to pick the best sample to start with. Take a look at the PubSub Client-Server or the infamous Chat Client-Server to realize how simple Atmosphere is! Z. Atmosphere 2.5.x requires JDK 8 or 11. Atmosphere 2.4.x requires JDK 1.7 or newer. 2.7.x releases: [2.7.5] (https://github.com/Atmosphere/atmosphere/issues?q=is%3Aissue+is%3Aclosed+label%3A2.7.5)2.7.4 2.7.3 2.7.1 2.7.0. 2.6.x releases: 2.6.4 2.6.1 2.6.0. 2.5.x releases: 2.5.14 2.5.9 2.5.5 2.5.3 2.5.2 2.5.0. 2.4.x releases: 2.4.32 2.4.302.4.29 2.4.27 2.4.26 2.4.24 2.4.23 2.4.22 2.4.19 2.4.18 2.4.17 2.4.16 2.4.13 2.4.12 2.4.11 2.4.9 2.4.8 2.4.7 2.4.6 2.4.5 2.4.4 2.4.3 2.4.2 2.4.1 2.4.0. 2.3.x releases: 2.3.10 2.3.8 2.3.7 2.3.6 2.3.5 2.3.4 2.3.3 2.3.2 2.3.1 2.3.0. 2.2.x releases: 2.2.13 2.2.10 2.2.9 2.2.8 2.2.7 2.2.6 2.2.5 2.2.4 2.2.3 2.2.2 2.2.1 2.2.0. 2.1.x releases: 2.1.14 2.1.12 2.1.11 2.1.10 2.1.9 2.1.8 2.1.7 2.1.6 2.1.5 2.1.4 2.1.2 2.1.1 2.1.0. 2.0.x releases: 2.0.12 2.0.11 2.0.10 2.0.9 2.0.8 2.0.7 2.0.6 2.0.5 2.0.4 2.0.3 [2.0.2] (http://goo.gl/44qnsU) 2.0.1. 1.0 releases: 1.0.21 1.0.17 1.0.16 1.0.14 1.0.13 1.0.11 1.0.10 1.0.8 1.0.6 1.0.5 1.0.4 1.0.3 1.0.2 1.0.1 1.0.

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