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estudos | Repositório com projetos de teste e notas de estudo

 by   mstuttgart C Version: Current License: MIT

 by   mstuttgart C Version: Current License: MIT

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kandi X-RAY | estudos Summary

estudos is a C library. estudos has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
:seedling: Repositório com projetos de teste e notas de estudo
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Quality
Security
Security
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kandi-support Support

  • estudos has a low active ecosystem.
  • It has 16 star(s) with 6 fork(s). There are 1 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 15 open issues and 150 have been closed. On average issues are closed in 517 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of estudos is current.
estudos Support
Best in #C
Average in #C
estudos Support
Best in #C
Average in #C

quality kandi Quality

  • estudos has no bugs reported.
estudos Quality
Best in #C
Average in #C
estudos Quality
Best in #C
Average in #C

securitySecurity

  • estudos has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
estudos Security
Best in #C
Average in #C
estudos Security
Best in #C
Average in #C

license License

  • estudos is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
estudos License
Best in #C
Average in #C
estudos License
Best in #C
Average in #C

buildReuse

  • estudos releases are not available. You will need to build from source code and install.
estudos Reuse
Best in #C
Average in #C
estudos Reuse
Best in #C
Average in #C
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estudos Key Features

:seedling: Repositório com projetos de teste e notas de estudo

Vuetify: My navigation drawer is positioned over another element (toolbar)

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Add clipped to v-navigation-drawer props like: 
<v-navigation-drawer
  clipped>
  <!-- ... -->
</v-navigation-drawer>

How to sort/order a list according to time in 00:00 format?

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void main() {
  json.sort((x, y) => '${x['time']}'.seconds.compareTo('${y['time']}'.seconds));
  for (final element in json) {
    print(element);
  }
}

final json = [
  {'id': 1, 'time': '10:00:01'},
  {'id': 3, 'time': '30:00'},
  {'id': 2, 'time': '20:00'},
  {'id': 4, 'time': '40:00'},
  {'id': 0, 'time': '10:00'},
];

extension _Time on String {
  int get seconds {
    var hours = 0;
    var minutes = 0;
    var seconds = 0;
    final parts = split(':');
    switch (parts.length) {
      case 2:
        minutes = _toInt(parts[0], 59);
        seconds = _toInt(parts[1], 59);
        break;
      case 3:
        hours = _toInt(parts[0], 23);
        minutes = _toInt(parts[1], 59);
        seconds = _toInt(parts[2], 59);
        break;
      default:
        _error();
    }

    return hours * 3600 + minutes * 60 + seconds;
  }

  void _error() {
    throw FormatException('Invalid time format: $this');
  }

  int _toInt(String part, int max) {
    final result = int.tryParse(part, radix: 10);
    if (result == null) {
      _error();
    }

    if (result < 0 || result > max) {
      _error();
    }

    return result;
  }
}
{id: 0, time: 10:00}
{id: 1, time: 10:00:01}
{id: 2, time: 20:00}
{id: 3, time: 30:00}
{id: 4, time: 40:00}
-----------------------
void main() {
  json.sort((x, y) => '${x['time']}'.seconds.compareTo('${y['time']}'.seconds));
  for (final element in json) {
    print(element);
  }
}

final json = [
  {'id': 1, 'time': '10:00:01'},
  {'id': 3, 'time': '30:00'},
  {'id': 2, 'time': '20:00'},
  {'id': 4, 'time': '40:00'},
  {'id': 0, 'time': '10:00'},
];

extension _Time on String {
  int get seconds {
    var hours = 0;
    var minutes = 0;
    var seconds = 0;
    final parts = split(':');
    switch (parts.length) {
      case 2:
        minutes = _toInt(parts[0], 59);
        seconds = _toInt(parts[1], 59);
        break;
      case 3:
        hours = _toInt(parts[0], 23);
        minutes = _toInt(parts[1], 59);
        seconds = _toInt(parts[2], 59);
        break;
      default:
        _error();
    }

    return hours * 3600 + minutes * 60 + seconds;
  }

  void _error() {
    throw FormatException('Invalid time format: $this');
  }

  int _toInt(String part, int max) {
    final result = int.tryParse(part, radix: 10);
    if (result == null) {
      _error();
    }

    if (result < 0 || result > max) {
      _error();
    }

    return result;
  }
}
{id: 0, time: 10:00}
{id: 1, time: 10:00:01}
{id: 2, time: 20:00}
{id: 3, time: 30:00}
{id: 4, time: 40:00}

C# - Entity Framework Code first, lazy loading not working

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[ForeignKey("idProgramas")]
public virtual Programas Programas { get; set; }

[ForeignKey("idProjetos")]
public virtual Projetos Projetos { get; set; }
db.Estudos
  .Include(x => x.Programas)
  .Include(x => x.Projetos)
  .ToList();
-----------------------
[ForeignKey("idProgramas")]
public virtual Programas Programas { get; set; }

[ForeignKey("idProjetos")]
public virtual Projetos Projetos { get; set; }
db.Estudos
  .Include(x => x.Programas)
  .Include(x => x.Projetos)
  .ToList();

Conditionally render JSX on specific routes

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import React from 'react';
import { Link, useLocation, useHistory, withRouter } from 'react-router-dom';

import logoImg from '../../assets/images/logo.svg';
import backIcon from '../../assets/images/icons/back.svg';

import './styles.css';

function SplitScreen(props: { children: React.ReactNode; }) {
  const { pathname } = useLoction();

  const showBack = !pathname.startsWith("/login");

  return (
    <section className="split-page-container">
      <div className="right-side">
        {showBack && (
          <Link
            className="back-arrow"
            to="/">
            <img src={backIcon} alt="Voltar" />
          </Link>
        )}
        <div className="proffy">
          <div className="proffy-fundo">
            <img src={logoImg} alt="Proffy Logo" />
            <h2>Sua plataforma de <br /> estudos online.</h2>
          </div>
        </div>
      </div>

      <div className="left-side">
        <div className="content-box">
          {props.children}
        </div>
      </div>
    </section>
  );
}
function SplitScreen(props: { children: React.ReactNode; }) {
  const { pathname } = useLoction();

  const showBack = !pathname.startsWith("/login");

  return (
    <section className="split-page-container">
      <div className="left-side"> // <-- now the left side
        {showBack && (
          <Link
            className="back-arrow"
            to="/">
            <img src={backIcon} alt="Voltar" />
          </Link>
        )}
        <div className="proffy">
          <div className="proffy-fundo">
            <img src={logoImg} alt="Proffy Logo" />
            <h2>Sua plataforma de <br /> estudos online.</h2>
          </div>
        </div>
      </div>

      <div className="right-side"> // <-- now the right side
        <div className="content-box">
          {props.children}
        </div>
      </div>
    </section>
  );
}
-----------------------
import React from 'react';
import { Link, useLocation, useHistory, withRouter } from 'react-router-dom';

import logoImg from '../../assets/images/logo.svg';
import backIcon from '../../assets/images/icons/back.svg';

import './styles.css';

function SplitScreen(props: { children: React.ReactNode; }) {
  const { pathname } = useLoction();

  const showBack = !pathname.startsWith("/login");

  return (
    <section className="split-page-container">
      <div className="right-side">
        {showBack && (
          <Link
            className="back-arrow"
            to="/">
            <img src={backIcon} alt="Voltar" />
          </Link>
        )}
        <div className="proffy">
          <div className="proffy-fundo">
            <img src={logoImg} alt="Proffy Logo" />
            <h2>Sua plataforma de <br /> estudos online.</h2>
          </div>
        </div>
      </div>

      <div className="left-side">
        <div className="content-box">
          {props.children}
        </div>
      </div>
    </section>
  );
}
function SplitScreen(props: { children: React.ReactNode; }) {
  const { pathname } = useLoction();

  const showBack = !pathname.startsWith("/login");

  return (
    <section className="split-page-container">
      <div className="left-side"> // <-- now the left side
        {showBack && (
          <Link
            className="back-arrow"
            to="/">
            <img src={backIcon} alt="Voltar" />
          </Link>
        )}
        <div className="proffy">
          <div className="proffy-fundo">
            <img src={logoImg} alt="Proffy Logo" />
            <h2>Sua plataforma de <br /> estudos online.</h2>
          </div>
        </div>
      </div>

      <div className="right-side"> // <-- now the right side
        <div className="content-box">
          {props.children}
        </div>
      </div>
    </section>
  );
}

Converting a block of code into a function results in error

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 def substance_evaluation(substance) 
 for ..., substance in ...: 
 ... >= substance_mean(substance) 
 median = df[substance].mean()
 if substance >= median:
for ..., value in ...:
    if value >= median:
def substance_evaluation(substance):

    median = df[substance].mean()

    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance] = np.where(mask, 'high', 'low')

    print(df.groupby(substance).quality.mean())
    df["new column"] = np.where(mask, 'high', 'low')
import pandas as pd
import random
import numpy as np
import time

def version1(df, substance):
    median = df[substance].mean()
    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

def version2(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

def version3(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance] = np.where(mask, 'high', 'low')

# ---

random.seed(0) # to generate always the same values

df = pd.DataFrame({'pH': [random.randint(0,7) for _ in range(5)]})

substance = 'pH'

print('--- before ---')
print(df)

# ---

df1 = df.copy()
start = time.time()

version1(df1, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df2 = df.copy()
start = time.time()

version2(df2, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df3 = df.copy()
start = time.time()

version3(df3, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)
-----------------------
 def substance_evaluation(substance) 
 for ..., substance in ...: 
 ... >= substance_mean(substance) 
 median = df[substance].mean()
 if substance >= median:
for ..., value in ...:
    if value >= median:
def substance_evaluation(substance):

    median = df[substance].mean()

    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance] = np.where(mask, 'high', 'low')

    print(df.groupby(substance).quality.mean())
    df["new column"] = np.where(mask, 'high', 'low')
import pandas as pd
import random
import numpy as np
import time

def version1(df, substance):
    median = df[substance].mean()
    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

def version2(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

def version3(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance] = np.where(mask, 'high', 'low')

# ---

random.seed(0) # to generate always the same values

df = pd.DataFrame({'pH': [random.randint(0,7) for _ in range(5)]})

substance = 'pH'

print('--- before ---')
print(df)

# ---

df1 = df.copy()
start = time.time()

version1(df1, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df2 = df.copy()
start = time.time()

version2(df2, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df3 = df.copy()
start = time.time()

version3(df3, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)
-----------------------
 def substance_evaluation(substance) 
 for ..., substance in ...: 
 ... >= substance_mean(substance) 
 median = df[substance].mean()
 if substance >= median:
for ..., value in ...:
    if value >= median:
def substance_evaluation(substance):

    median = df[substance].mean()

    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance] = np.where(mask, 'high', 'low')

    print(df.groupby(substance).quality.mean())
    df["new column"] = np.where(mask, 'high', 'low')
import pandas as pd
import random
import numpy as np
import time

def version1(df, substance):
    median = df[substance].mean()
    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

def version2(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

def version3(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance] = np.where(mask, 'high', 'low')

# ---

random.seed(0) # to generate always the same values

df = pd.DataFrame({'pH': [random.randint(0,7) for _ in range(5)]})

substance = 'pH'

print('--- before ---')
print(df)

# ---

df1 = df.copy()
start = time.time()

version1(df1, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df2 = df.copy()
start = time.time()

version2(df2, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df3 = df.copy()
start = time.time()

version3(df3, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)
-----------------------
 def substance_evaluation(substance) 
 for ..., substance in ...: 
 ... >= substance_mean(substance) 
 median = df[substance].mean()
 if substance >= median:
for ..., value in ...:
    if value >= median:
def substance_evaluation(substance):

    median = df[substance].mean()

    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance] = np.where(mask, 'high', 'low')

    print(df.groupby(substance).quality.mean())
    df["new column"] = np.where(mask, 'high', 'low')
import pandas as pd
import random
import numpy as np
import time

def version1(df, substance):
    median = df[substance].mean()
    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

def version2(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

def version3(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance] = np.where(mask, 'high', 'low')

# ---

random.seed(0) # to generate always the same values

df = pd.DataFrame({'pH': [random.randint(0,7) for _ in range(5)]})

substance = 'pH'

print('--- before ---')
print(df)

# ---

df1 = df.copy()
start = time.time()

version1(df1, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df2 = df.copy()
start = time.time()

version2(df2, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df3 = df.copy()
start = time.time()

version3(df3, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)
-----------------------
 def substance_evaluation(substance) 
 for ..., substance in ...: 
 ... >= substance_mean(substance) 
 median = df[substance].mean()
 if substance >= median:
for ..., value in ...:
    if value >= median:
def substance_evaluation(substance):

    median = df[substance].mean()

    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance] = np.where(mask, 'high', 'low')

    print(df.groupby(substance).quality.mean())
    df["new column"] = np.where(mask, 'high', 'low')
import pandas as pd
import random
import numpy as np
import time

def version1(df, substance):
    median = df[substance].mean()
    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

def version2(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

def version3(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance] = np.where(mask, 'high', 'low')

# ---

random.seed(0) # to generate always the same values

df = pd.DataFrame({'pH': [random.randint(0,7) for _ in range(5)]})

substance = 'pH'

print('--- before ---')
print(df)

# ---

df1 = df.copy()
start = time.time()

version1(df1, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df2 = df.copy()
start = time.time()

version2(df2, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df3 = df.copy()
start = time.time()

version3(df3, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)
-----------------------
 def substance_evaluation(substance) 
 for ..., substance in ...: 
 ... >= substance_mean(substance) 
 median = df[substance].mean()
 if substance >= median:
for ..., value in ...:
    if value >= median:
def substance_evaluation(substance):

    median = df[substance].mean()

    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance] = np.where(mask, 'high', 'low')

    print(df.groupby(substance).quality.mean())
    df["new column"] = np.where(mask, 'high', 'low')
import pandas as pd
import random
import numpy as np
import time

def version1(df, substance):
    median = df[substance].mean()
    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

def version2(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

def version3(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance] = np.where(mask, 'high', 'low')

# ---

random.seed(0) # to generate always the same values

df = pd.DataFrame({'pH': [random.randint(0,7) for _ in range(5)]})

substance = 'pH'

print('--- before ---')
print(df)

# ---

df1 = df.copy()
start = time.time()

version1(df1, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df2 = df.copy()
start = time.time()

version2(df2, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df3 = df.copy()
start = time.time()

version3(df3, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)
-----------------------
 def substance_evaluation(substance) 
 for ..., substance in ...: 
 ... >= substance_mean(substance) 
 median = df[substance].mean()
 if substance >= median:
for ..., value in ...:
    if value >= median:
def substance_evaluation(substance):

    median = df[substance].mean()

    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance] = np.where(mask, 'high', 'low')

    print(df.groupby(substance).quality.mean())
    df["new column"] = np.where(mask, 'high', 'low')
import pandas as pd
import random
import numpy as np
import time

def version1(df, substance):
    median = df[substance].mean()
    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

def version2(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

def version3(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance] = np.where(mask, 'high', 'low')

# ---

random.seed(0) # to generate always the same values

df = pd.DataFrame({'pH': [random.randint(0,7) for _ in range(5)]})

substance = 'pH'

print('--- before ---')
print(df)

# ---

df1 = df.copy()
start = time.time()

version1(df1, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df2 = df.copy()
start = time.time()

version2(df2, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df3 = df.copy()
start = time.time()

version3(df3, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)
-----------------------
 def substance_evaluation(substance) 
 for ..., substance in ...: 
 ... >= substance_mean(substance) 
 median = df[substance].mean()
 if substance >= median:
for ..., value in ...:
    if value >= median:
def substance_evaluation(substance):

    median = df[substance].mean()

    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance] = np.where(mask, 'high', 'low')

    print(df.groupby(substance).quality.mean())
    df["new column"] = np.where(mask, 'high', 'low')
import pandas as pd
import random
import numpy as np
import time

def version1(df, substance):
    median = df[substance].mean()
    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

def version2(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

def version3(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance] = np.where(mask, 'high', 'low')

# ---

random.seed(0) # to generate always the same values

df = pd.DataFrame({'pH': [random.randint(0,7) for _ in range(5)]})

substance = 'pH'

print('--- before ---')
print(df)

# ---

df1 = df.copy()
start = time.time()

version1(df1, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df2 = df.copy()
start = time.time()

version2(df2, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df3 = df.copy()
start = time.time()

version3(df3, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)
-----------------------
 def substance_evaluation(substance) 
 for ..., substance in ...: 
 ... >= substance_mean(substance) 
 median = df[substance].mean()
 if substance >= median:
for ..., value in ...:
    if value >= median:
def substance_evaluation(substance):

    median = df[substance].mean()

    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance] = np.where(mask, 'high', 'low')

    print(df.groupby(substance).quality.mean())
    df["new column"] = np.where(mask, 'high', 'low')
import pandas as pd
import random
import numpy as np
import time

def version1(df, substance):
    median = df[substance].mean()
    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

def version2(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

def version3(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance] = np.where(mask, 'high', 'low')

# ---

random.seed(0) # to generate always the same values

df = pd.DataFrame({'pH': [random.randint(0,7) for _ in range(5)]})

substance = 'pH'

print('--- before ---')
print(df)

# ---

df1 = df.copy()
start = time.time()

version1(df1, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df2 = df.copy()
start = time.time()

version2(df2, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df3 = df.copy()
start = time.time()

version3(df3, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)
-----------------------
 def substance_evaluation(substance) 
 for ..., substance in ...: 
 ... >= substance_mean(substance) 
 median = df[substance].mean()
 if substance >= median:
for ..., value in ...:
    if value >= median:
def substance_evaluation(substance):

    median = df[substance].mean()

    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance] = np.where(mask, 'high', 'low')

    print(df.groupby(substance).quality.mean())
    df["new column"] = np.where(mask, 'high', 'low')
import pandas as pd
import random
import numpy as np
import time

def version1(df, substance):
    median = df[substance].mean()
    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

def version2(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

def version3(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance] = np.where(mask, 'high', 'low')

# ---

random.seed(0) # to generate always the same values

df = pd.DataFrame({'pH': [random.randint(0,7) for _ in range(5)]})

substance = 'pH'

print('--- before ---')
print(df)

# ---

df1 = df.copy()
start = time.time()

version1(df1, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df2 = df.copy()
start = time.time()

version2(df2, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df3 = df.copy()
start = time.time()

version3(df3, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)
-----------------------
 def substance_evaluation(substance) 
 for ..., substance in ...: 
 ... >= substance_mean(substance) 
 median = df[substance].mean()
 if substance >= median:
for ..., value in ...:
    if value >= median:
def substance_evaluation(substance):

    median = df[substance].mean()

    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

    print(df.groupby(substance).quality.mean())
def substance_evaluation(substance):

    median = df[substance].mean()

    mask = (df[substance] >= mediam)

    df[substance] = np.where(mask, 'high', 'low')

    print(df.groupby(substance).quality.mean())
    df["new column"] = np.where(mask, 'high', 'low')
import pandas as pd
import random
import numpy as np
import time

def version1(df, substance):
    median = df[substance].mean()
    for index, value in enumerate(df[substance]):
        if value >= median:
            df.loc[index, substance] = 'high'
        else:
            df.loc[index, substance] = 'low'

def version2(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance][  mask ] = 'high'
    df[substance][ ~mask ] = 'low'

def version3(df, substance):
    median = df[substance].mean()
    mask = (df[substance] >= median)
    df[substance] = np.where(mask, 'high', 'low')

# ---

random.seed(0) # to generate always the same values

df = pd.DataFrame({'pH': [random.randint(0,7) for _ in range(5)]})

substance = 'pH'

print('--- before ---')
print(df)

# ---

df1 = df.copy()
start = time.time()

version1(df1, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df2 = df.copy()
start = time.time()

version2(df2, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

# ---

df3 = df.copy()
start = time.time()

version3(df3, substance)

end = time.time()
print('--- after --- time:', end-start)
print(df1)

Python numpy error: only integer scalar arrays can be converted to a scalar index

copy iconCopydownload iconDownload
File "C:\Users\Lucas\Desktop\Estudos\Python\Simple Pendulum.py", line 27, in 
position x = np.cumsum(self.origin[0], L*np.sin(self.state[0]))
self.origin[0]
L*np.sin(self.state[0]
numpy.cumsum(a, axis=None, dtype=None, out=None)[source]
-----------------------
File "C:\Users\Lucas\Desktop\Estudos\Python\Simple Pendulum.py", line 27, in 
position x = np.cumsum(self.origin[0], L*np.sin(self.state[0]))
self.origin[0]
L*np.sin(self.state[0]
numpy.cumsum(a, axis=None, dtype=None, out=None)[source]
-----------------------
File "C:\Users\Lucas\Desktop\Estudos\Python\Simple Pendulum.py", line 27, in 
position x = np.cumsum(self.origin[0], L*np.sin(self.state[0]))
self.origin[0]
L*np.sin(self.state[0]
numpy.cumsum(a, axis=None, dtype=None, out=None)[source]

Nodejs with sequelize Returns Error Running Update

copy iconCopydownload iconDownload
const { id, name, provider } = await User.update(req.body);
const { id, name, provider } = await User.update(req.body, {
   where: {? : ?}
});
-----------------------
const { id, name, provider } = await User.update(req.body);
const { id, name, provider } = await User.update(req.body, {
   where: {? : ?}
});

How can I remove a large empty space from my page?

copy iconCopydownload iconDownload
* {
  border: 1px solid red;
}
article img {
  width: 700px;
  position: absolute;
  bottom: 250px;
  left: 900px; /* <-- Causing horizontal scroll */
}
.handwriting {
  font-family: Sepet;
  font-size: 30px;
  position: relative;
  left: 1000px; /* <-- Causing horizontal scroll */
  bottom: 150px;
}
-----------------------
* {
  border: 1px solid red;
}
article img {
  width: 700px;
  position: absolute;
  bottom: 250px;
  left: 900px; /* <-- Causing horizontal scroll */
}
.handwriting {
  font-family: Sepet;
  font-size: 30px;
  position: relative;
  left: 1000px; /* <-- Causing horizontal scroll */
  bottom: 150px;
}
-----------------------
.handwriting{
font-family: Sepet;
font-size: 30px;
position: relative;
left: 1000px;
bottom: 150px;
}
<p class="handwriting"><a href="#">Aulas</a></p>
-----------------------
.handwriting{
font-family: Sepet;
font-size: 30px;
position: relative;
left: 1000px;
bottom: 150px;
}
<p class="handwriting"><a href="#">Aulas</a></p>

Clean improperly positioned CR+LF in texts

copy iconCopydownload iconDownload
\R          # any kind of linebreak (ie. \r, \n, \r\n)
(?!         # negative lookahead, zero length assertion that makes sure we do not have after:
    \|      # a pipe character
)           # end lookahead
| 1020941333    |     569|SP    |500000343 | 9|18.05.2011|15:27:00|18.05.2011|18.05.2011|Y-0444871-ENCR    |           1,93 |BRL  |8000800000  |Juros, Comissões e T       |                  |           |                                        |    |          |     |                     |CLB082902  |     |     |                 |COEL  |COEL  |Y-0444871               |
| 1020941586    |      43|SP    |500000344 |43|18.05.2011|15:41:43|18.05.2011|18.05.2011|B-0447039-ENCR    |           9,02 |BRL  |8000800000  |Juros, Comissões e T       |                  |           |                                        |    |          |     |                     |CLB082902  |     |     |                 |COEL  |COEL  |B-0447039               |
-----------------------
\R          # any kind of linebreak (ie. \r, \n, \r\n)
(?!         # negative lookahead, zero length assertion that makes sure we do not have after:
    \|      # a pipe character
)           # end lookahead
| 1020941333    |     569|SP    |500000343 | 9|18.05.2011|15:27:00|18.05.2011|18.05.2011|Y-0444871-ENCR    |           1,93 |BRL  |8000800000  |Juros, Comissões e T       |                  |           |                                        |    |          |     |                     |CLB082902  |     |     |                 |COEL  |COEL  |Y-0444871               |
| 1020941586    |      43|SP    |500000344 |43|18.05.2011|15:41:43|18.05.2011|18.05.2011|B-0447039-ENCR    |           9,02 |BRL  |8000800000  |Juros, Comissões e T       |                  |           |                                        |    |          |     |                     |CLB082902  |     |     |                 |COEL  |COEL  |B-0447039               |

Serenity Cucumber: tests exit in first fail and report is empty

copy iconCopydownload iconDownload
<plugin>
            <groupId>net.serenity-bdd.maven.plugins</groupId>
            <artifactId>serenity-maven-plugin</artifactId>
            <version>${serenity.version}</version>
            <dependencies>
                <dependency>
                    <groupId>net.serenity-bdd</groupId>
                    <artifactId>serenity-core</artifactId>
                    <version>${serenity.version}</version>
                </dependency>
            </dependencies>

Community Discussions

Trending Discussions on estudos
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  • Nodejs with sequelize Returns Error Running Update
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  • Rendering a layout using an example as guide
Trending Discussions on estudos

QUESTION

Vuetify: My navigation drawer is positioned over another element (toolbar)

Asked 2021-Feb-11 at 16:35

I would like to put my navigation drawer under the toolbar.

I'm trying to achieve something like this :

enter image description here

I am trying to do something similar but all attempts are unsuccessful, at the moment I have the following: enter image description here

My code:

<template>
 <nav>
   
    <v-snackbar v-model="snackbar" :timeout="4000" top color="success">
      <span>Awesome! You added a new project.</span>
      <v-btn text flat @click="snackbar = false">Close</v-btn>
    </v-snackbar>

        <v-toolbar app clipped-left >
            <v-toolbar-side-icon></v-toolbar-side-icon>
             <v-app-bar-nav-icon  @click.stop="drawer = !drawer"></v-app-bar-nav-icon>
            <v-toolbar-title class="text-uppercase gr ey--text">
                <span class="font-weight-light">estudos</span>
                <span>vue</span>
            </v-toolbar-title>
            <v-spacer></v-spacer>

            <v-menu offset-y>
      <template v-slot:activator="{ on, attrs }">
        <v-btn text
          color="primary"
          dark
          v-bind="attrs"
          v-on="on"
        >
          <v-icon left>expand_more</v-icon>
          <span>Menu</span>
        </v-btn>
      </template>
        <v-list>
          <v-list-item v-for="link in links" :key="link.text" router :to="link.route">
            <v-list-item-title>{{link.text}}</v-list-item-title>
          </v-list-item>
        </v-list>
    </v-menu>

            
            <v-btn text color="grey">
                <span>Sign Out</span>
                <v-icon right>exit_to_app</v-icon>
            </v-btn>
        </v-toolbar>


    

    <v-navigation-drawer  v-model="drawer" app  class="indigo white--text"> 
             <v-app-bar-nav-icon  @click.stop="drawer = !drawer"></v-app-bar-nav-icon>
             <v-layout column align-center>
              <v-flex class="mt-5">
                <v-avatar size="90">
                  <img src="/avatar-64.png">
                </v-avatar>
                <p class="white-text dubheading mt-1">
                  Estudos Vue
                </p>
              </v-flex>
              <v-flex class="mt-4 mb-3">
                <popup @projectAdded="snackbar=true" />
              </v-flex>
             </v-layout>
       <v-list >
        <v-divider></v-divider>

        <v-list-item 
          v-for="link in links"
          :key="link.text"
          router :to="link.route"
        >
          <v-list-item-action >
            <v-icon class="white--text">{{ link.icon }}</v-icon>
          </v-list-item-action>

          <v-list-item-content class="white--text">
            <v-list-item-title>{{ link.text }}</v-list-item-title>
          </v-list-item-content>
        </v-list-item>
      </v-list>
    </v-navigation-drawer>
    
     <v-row>
    <v-col
     
  
    >
      <v-img
      
      max-height="76%"
      max-width="100%"
        src="/imgtest.jpg"
        gradient="to top right, rgba(14,12,11,.51), rgba(14,12,11,.71)"
      >
      <v-img-title class="heading white--text">
        Bien saude</v-img-title></v-img>
    </v-col>

  </v-row>
    </nav>
    
</template>

<script>
import Popup from './Popup'
export default {
    components: { Popup },
    data() {
        return {
            drawer:false,
            links:[
                 {icon: 'dashboard', text:'Dashboard', route:'/'},
                 {icon: 'folder', text:'My Projects', route:'/projects'},
                 {icon: 'person', text:'Team', route:'/team'},

            ],
            items: [
        { title: 'Click Me' },
        { title: 'Click Me' },
        { title: 'Click Me' },
        { title: 'Click Me 2' },
      ],
            snackbar: true
        }
    },
  }
</script>

I tried adding Block and removing the app, but it didn't solve the problem... How do I put my drawer under the toolbar?

ANSWER

Answered 2021-Feb-10 at 16:07

Add clipped to v-navigation-drawer props like: 
<v-navigation-drawer
  clipped>
  <!-- ... -->
</v-navigation-drawer>

Source https://stackoverflow.com/questions/66138975

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