hawk | ️ Secure, simple keyvalue storage for Android | Encryption library

by orhanobut Java Version: 2.0.1 License: Apache-2.0

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kandi X-RAY | hawk Summary

hawk is a Java library typically used in Security, Encryption applications. hawk has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub, Maven.

Secure, simple key-value storage for android.

Support

Quality

Security

License

Reuse

kandi-support Support

hawk has a medium active ecosystem.
It has 3785 star(s) with 383 fork(s). There are 97 watchers for this library.
It had no major release in the last 12 months.
There are 41 open issues and 129 have been closed. On average issues are closed in 421 days. There are 5 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of hawk is 2.0.1

hawk Support

Best in #Encryption

Average in #Encryption

hawk Support

Best in #Encryption

Average in #Encryption

quality kandi Quality

hawk has 0 bugs and 0 code smells.

hawk Quality

Best in #Encryption

Average in #Encryption

hawk Quality

Best in #Encryption

Average in #Encryption

security Security

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

hawk Security

Best in #Encryption

Average in #Encryption

hawk Security

Best in #Encryption

Average in #Encryption

license License

hawk 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.

hawk License

Best in #Encryption

Average in #Encryption

hawk License

Best in #Encryption

Average in #Encryption

build Reuse

hawk releases are available to install and integrate.
Deployable package is available in Maven.
Build file is available. You can build the component from source.
Installation instructions are not available. Examples and code snippets are available.
It has 1900 lines of code, 266 functions and 40 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.

hawk Reuse

Best in #Encryption

Average in #Encryption

hawk Reuse

Best in #Encryption

Average in #Encryption

Top functions reviewed by kandi - BETA

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

Serialize the given key to the given value .
Parse the given JSON string into a map .
Time Hawk .
Gets the log interceptor .
Decrypt a string using the specified key .
Parse the given JSON content into an object .
Throws NullPointerException if the given value is null .
Initialize a new HawkBuilder with the specified context .
Log a message
Encodes a byte array .

hawk Key Features

✔️ Secure, simple key-value storage for Android

hawk Examples and Code Snippets

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Download

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compile "com.orhanobut:hawk:2.0.1"

Initialize

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Hawk.init(context).build();

Usage

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Hawk.put(key, T);

More options

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Hawk.init(context)
  .setEncryption(new NoEncryption())
  .setLogInterceptor(new MyLogInterceptor())
  .setConverter(new MyConverter())
  .setParser(new MyParser())
  .setStorage(new MyStorage())
  .build();

License

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Copyright 2016 Orhan Obut

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Is there an R function to help turn State abbreviations into full names? Or Vice Versa?

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a <- c('NJ', 'MA', 'FL')
state.name[sapply(a, \(x) which(x == state.abb))]

[1] "New Jersey"    "Massachusetts" "Florida"

birthdata2$State <- trimws(birthdata2$State)
birthdata2$state_name <- state.name[sapply(birthdata2$State, \(x) which(x == state.abb)[1])]

   County          State     x  year state_name    
   <chr>           <chr> <dbl> <dbl> <chr>         
 1 Ada County      ID      358  2010 Idaho         
 2 Adams County    CO      549  2010 Colorado      
 3 Adams County    PA       NA  2010 Pennsylvania  
 4 Aiken County    SC      149  2010 South Carolina
 5 Alachua County  FL      219  2010 Florida       
 6 Alamance County NC      178  2010 North Carolina
 7 Alameda County  CA     1367  2010 California    
 8 Albany County   NY      257  2010 New York      
 9 Alexandria city VA      194  2010 Virginia      
10 Allegan County  MI       76  2010 Michigan      
# … with 618 more rows

-----------------------

a <- c('NJ', 'MA', 'FL')
state.name[sapply(a, \(x) which(x == state.abb))]

[1] "New Jersey"    "Massachusetts" "Florida"

birthdata2$State <- trimws(birthdata2$State)
birthdata2$state_name <- state.name[sapply(birthdata2$State, \(x) which(x == state.abb)[1])]

   County          State     x  year state_name    
   <chr>           <chr> <dbl> <dbl> <chr>         
 1 Ada County      ID      358  2010 Idaho         
 2 Adams County    CO      549  2010 Colorado      
 3 Adams County    PA       NA  2010 Pennsylvania  
 4 Aiken County    SC      149  2010 South Carolina
 5 Alachua County  FL      219  2010 Florida       
 6 Alamance County NC      178  2010 North Carolina
 7 Alameda County  CA     1367  2010 California    
 8 Albany County   NY      257  2010 New York      
 9 Alexandria city VA      194  2010 Virginia      
10 Allegan County  MI       76  2010 Michigan      
# … with 618 more rows

Error ' not supported between instances of float and str ' when I try to use .sort() on a Python list

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para_planilha.sort(key=lambda row: row[0])

-----------------------

...
['2022-04-01T14:00:00Z', '2022/04/01 00:58:53', '-', 'Csm Slatina', 'ACS Vedita Colonesti MS', '-', '-', '-', '-', '-'],
['2022-04-01T14:00:00Z', '2022/04/01 00:58:53', '-', 'CSM Flacara Moreni', 'ACS FC Academica Clinceni 2', '-', '-', '-', '-', '-'],
[2.36, '-'],
['2022-04-01T14:00:00Z', '2022/04/01 00:58:55', '-', 'Dinamo Bucuresti II', 'CS Real Bradu', 1.93, '-', '-', '-', '-'],
['2022-04-01T14:00:00Z', '2022/04/01 00:58:56', '-', 'FCSB II', 'FC Arges II', '-', '-', '-', '-', '-'],
...

para_planiha = [k for k in para_planiha if len(k) > 2]

-----------------------

...
['2022-04-01T14:00:00Z', '2022/04/01 00:58:53', '-', 'Csm Slatina', 'ACS Vedita Colonesti MS', '-', '-', '-', '-', '-'],
['2022-04-01T14:00:00Z', '2022/04/01 00:58:53', '-', 'CSM Flacara Moreni', 'ACS FC Academica Clinceni 2', '-', '-', '-', '-', '-'],
[2.36, '-'],
['2022-04-01T14:00:00Z', '2022/04/01 00:58:55', '-', 'Dinamo Bucuresti II', 'CS Real Bradu', 1.93, '-', '-', '-', '-'],
['2022-04-01T14:00:00Z', '2022/04/01 00:58:56', '-', 'FCSB II', 'FC Arges II', '-', '-', '-', '-', '-'],
...

para_planiha = [k for k in para_planiha if len(k) > 2]

-----------------------

new_list = sorted(filter(lambda x: len(x)==10, para_planilha), key=lambda x: x[0])

My function isnt taking a string when its suppossed to

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Vector<string> Actor(string &actorData)
{
  Vector<string> Actors;
  Actors.push_back((split(actorData, "\t"))); //Trying to remove tab from the
  return Actors;                               //previous vector
}

Vector<string> Actor(string &actorData)
{
  Vector<string> Actors;
  Vector<string> splitStrs = split(actorData, "\t");
  Actors.push_back(splitStrs); //Trying to remove tab from the
  return Actors;                               //previous vector
}

Actors.push_back(splitStrs[0]);

Actors.insert(Actors.begin(), splitStrs.begin(), stritStes.end());

-----------------------

Vector<string> Actor(string &actorData)
{
  Vector<string> Actors;
  Actors.push_back((split(actorData, "\t"))); //Trying to remove tab from the
  return Actors;                               //previous vector
}

Vector<string> Actor(string &actorData)
{
  Vector<string> Actors;
  Vector<string> splitStrs = split(actorData, "\t");
  Actors.push_back(splitStrs); //Trying to remove tab from the
  return Actors;                               //previous vector
}

Actors.push_back(splitStrs[0]);

Actors.insert(Actors.begin(), splitStrs.begin(), stritStes.end());

-----------------------

Vector<string> Actor(string &actorData)
{
  Vector<string> Actors;
  Actors.push_back((split(actorData, "\t"))); //Trying to remove tab from the
  return Actors;                               //previous vector
}

Vector<string> Actor(string &actorData)
{
  Vector<string> Actors;
  Vector<string> splitStrs = split(actorData, "\t");
  Actors.push_back(splitStrs); //Trying to remove tab from the
  return Actors;                               //previous vector
}

Actors.push_back(splitStrs[0]);

Actors.insert(Actors.begin(), splitStrs.begin(), stritStes.end());

-----------------------

Vector<string> Actor(string &actorData)
{
  Vector<string> Actors;
  Actors.push_back((split(actorData, "\t"))); //Trying to remove tab from the
  return Actors;                               //previous vector
}

Vector<string> Actor(string &actorData)
{
  Vector<string> Actors;
  Vector<string> splitStrs = split(actorData, "\t");
  Actors.push_back(splitStrs); //Trying to remove tab from the
  return Actors;                               //previous vector
}

Actors.push_back(splitStrs[0]);

Actors.insert(Actors.begin(), splitStrs.begin(), stritStes.end());

Retrieve value from IndexedDB using Dexie and Svelte

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  let total = liveQuery(() =>
    db.storage
      .where("name")
      .equals("total")
      .value.then(function(a) {
        totals = a;
      })
  );

  let total = liveQuery(() =>
    db.storage
      .where("name")
      .equals("total")
      .first()
  );

<script>
    // ...your existing script code with updated query...
    $: daysMax = $total?.value // undefined until the query resolves, actual value once the query has resolved
</script>

<main>
    {#if daysMax !== undefined}
        <Counter daysLeft={left} {daysMax} />
    {/if}
    <New />
    <History />
</main>

-----------------------

  let total = liveQuery(() =>
    db.storage
      .where("name")
      .equals("total")
      .value.then(function(a) {
        totals = a;
      })
  );

  let total = liveQuery(() =>
    db.storage
      .where("name")
      .equals("total")
      .first()
  );

<script>
    // ...your existing script code with updated query...
    $: daysMax = $total?.value // undefined until the query resolves, actual value once the query has resolved
</script>

<main>
    {#if daysMax !== undefined}
        <Counter daysLeft={left} {daysMax} />
    {/if}
    <New />
    <History />
</main>

-----------------------

  let total = liveQuery(() =>
    db.storage
      .where("name")
      .equals("total")
      .value.then(function(a) {
        totals = a;
      })
  );

  let total = liveQuery(() =>
    db.storage
      .where("name")
      .equals("total")
      .first()
  );

<script>
    // ...your existing script code with updated query...
    $: daysMax = $total?.value // undefined until the query resolves, actual value once the query has resolved
</script>

<main>
    {#if daysMax !== undefined}
        <Counter daysLeft={left} {daysMax} />
    {/if}
    <New />
    <History />
</main>

Decrypt message with random shift of letters

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letter_counts = Counter(message)
e_encrypted = [k for k, v in letter_counts.items() if v == max(count for c, count in letter_counts.items() if c != ' ')][0]
shift = (ord('e') - ord(e_encrypted)) % 26

letter_counts = Counter(message)
e_encrypted, max_v = None, 0
for k, v in letter_counts.items():
    if v > max_v and k != ' ':
        e_encrypted, max_v = k, v
shift = (ord('e') - ord(e_encrypted)) % 26

-----------------------

letter_counts = Counter(message)
e_encrypted = [k for k, v in letter_counts.items() if v == max(count for c, count in letter_counts.items() if c != ' ')][0]
shift = (ord('e') - ord(e_encrypted)) % 26

letter_counts = Counter(message)
e_encrypted, max_v = None, 0
for k, v in letter_counts.items():
    if v > max_v and k != ' ':
        e_encrypted, max_v = k, v
shift = (ord('e') - ord(e_encrypted)) % 26

-----------------------

test_str = "" # your string
counter = Counter(test_str)
keys = sorted(counter, key=counter.get, reverse=True)
res = keys[1] if keys[0] == " " else keys[0]

shift = ord('e') - ord(res)

-----------------------

test_str = "" # your string
counter = Counter(test_str)
keys = sorted(counter, key=counter.get, reverse=True)
res = keys[1] if keys[0] == " " else keys[0]

shift = ord('e') - ord(res)

Adding a title to a plot that was made in an R function

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library(tidyverse)
x <- c(0:30)
comp_2 <- function(o, h, x){
  for (j in 1:30){
    on = 0.2*o[j] - .001*h[j]*o[j] + o[j]
    hn = 0.3*h[j] - .002 *h[j]*o[j] + h[j]
    o <- append(o, on)
    h <- append(h, hn)
  }
data.frame(x = x, o = o, h = h) %>% 
  ggplot(aes(x = x)) + 
  geom_line(aes(y = o, col="Owls")) + 
  geom_line(aes(y =h, col="Hawks")) +
  theme_bw()+
 labs(x = "X", y="On, Hn") + 
  theme(legend.title = element_blank(), 
        legend.position = c(0.1, 0.8))
}
comp_2(151, 199, x) + labs(title = "Figure 1") # add others if needed
comp_2(149, 201, x) + labs(title = "Figure 2")
comp_2(10, 10, x) + labs(title = "Figure 3")

How to return an object property from an array of objects in react

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const [questions, setQuestions] = useState([]);

{questions.length > 0 && questions[index].category}

-----------------------

const [questions, setQuestions] = useState([]);

{questions.length > 0 && questions[index].category}

-----------------------

let results = questions.map(item => item.category)

Pandas - Merging Two Data frames with different index names but same amount of Columns

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DF1.columns = pd.MultiIndex.from_frame(DF2.T, names= [None] * len(DF2))
print (DF1)
     Desc Round 1  Round 2 Round 3 Round 4 Round 5
      Opp   Hawks Dolphins Raptors  Sharks   Bears
     Date   02/04    08/04   16/04   24/04   30/04
    Venue    MELB    PERTH    MELB     SYD    MELB
0     Tom       4        2       1       -       3
1    Mark       2        2       3       2       1
2   James       -        1       5       5       4
30    Ted       2        3       3       3       2

-----------------------

df1.columns = pd.MultiIndex.from_arrays(df2.values)
print(df1)

# Output
     Desc Round 1  Round 2 Round 3 Round 4 Round 5
      Opp   Hawks Dolphins Raptors  Sharks   Bears
     Date   02/04    08/04   16/04   24/04   30/04
    Venue    MELB    PERTH    MELB     SYD    MELB
0     Tom       4        2       1       -       3
1    Mark       2        2       3       2       1
2   James       -        1       5       5       4
30    Ted       2        3       3       3       2

df1.columns = tuple(tuple(l) for l in df2.T.values)
print(df1)

# Output
     Desc Round 1  Round 2 Round 3 Round 4 Round 5
      Opp   Hawks Dolphins Raptors  Sharks   Bears
     Date   02/04    08/04   16/04   24/04   30/04
    Venue    MELB    PERTH    MELB     SYD    MELB
0     Tom       4        2       1       -       3
1    Mark       2        2       3       2       1
2   James       -        1       5       5       4
30    Ted       2        3       3       3       2

-----------------------

df1.columns = pd.MultiIndex.from_arrays(df2.values)
print(df1)

# Output
     Desc Round 1  Round 2 Round 3 Round 4 Round 5
      Opp   Hawks Dolphins Raptors  Sharks   Bears
     Date   02/04    08/04   16/04   24/04   30/04
    Venue    MELB    PERTH    MELB     SYD    MELB
0     Tom       4        2       1       -       3
1    Mark       2        2       3       2       1
2   James       -        1       5       5       4
30    Ted       2        3       3       3       2

df1.columns = tuple(tuple(l) for l in df2.T.values)
print(df1)

# Output
     Desc Round 1  Round 2 Round 3 Round 4 Round 5
      Opp   Hawks Dolphins Raptors  Sharks   Bears
     Date   02/04    08/04   16/04   24/04   30/04
    Venue    MELB    PERTH    MELB     SYD    MELB
0     Tom       4        2       1       -       3
1    Mark       2        2       3       2       1
2   James       -        1       5       5       4
30    Ted       2        3       3       3       2

XSLT - Add new element and value

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<m2:InvokeWebService xmlns:m2="http://www.w3.org/2001/XMLSchema-instance">
    <m2:request>
        <m2:action>ADD</m2:action>
        <m2:commonDetails>
            <m2:needSupport>Y</m2:needSupport>
        </m2:commonDetails>
        <m2:custDetails>
            <m2:name>Tony,Hawk</m2:name>
            <m2:accountNumber>23232423566</m2:accountNumber>
            <m2:sensitiveCustomer>Y</m2:sensitiveCustomer>
        </m2:custDetails>
    </m2:request>
</m2:InvokeWebService>

<?xml version="1.0"?>
<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform" xmlns:m2="http://www.w3.org/2001/XMLSchema-instance">
    <xsl:output method="xml" encoding="utf-8" indent="yes" omit-xml-declaration="no"/>
    <xsl:strip-space elements="*"/>

    <xsl:template match="@*|node()">
        <xsl:copy>
            <xsl:apply-templates select="@*|node()"/>
        </xsl:copy>
    </xsl:template>

    <xsl:template match="m2:custDetails">
        <m2:overrideScriptName>NewScript</m2:overrideScriptName>
        <xsl:copy-of select="."/>
    </xsl:template>
</xsl:stylesheet>

<?xml version='1.0' encoding='utf-8' ?>
<m2:InvokeWebService xmlns:m2="http://www.w3.org/2001/XMLSchema-instance">
  <m2:request>
    <m2:action>ADD</m2:action>
    <m2:commonDetails>
      <m2:needSupport>Y</m2:needSupport>
    </m2:commonDetails>
    <m2:overrideScriptName>NewScript</m2:overrideScriptName>
    <m2:custDetails>
      <m2:name>Tony,Hawk</m2:name>
      <m2:accountNumber>23232423566</m2:accountNumber>
      <m2:sensitiveCustomer>Y</m2:sensitiveCustomer>
    </m2:custDetails>
  </m2:request>
</m2:InvokeWebService>

-----------------------

<m2:InvokeWebService xmlns:m2="http://www.w3.org/2001/XMLSchema-instance">
    <m2:request>
        <m2:action>ADD</m2:action>
        <m2:commonDetails>
            <m2:needSupport>Y</m2:needSupport>
        </m2:commonDetails>
        <m2:custDetails>
            <m2:name>Tony,Hawk</m2:name>
            <m2:accountNumber>23232423566</m2:accountNumber>
            <m2:sensitiveCustomer>Y</m2:sensitiveCustomer>
        </m2:custDetails>
    </m2:request>
</m2:InvokeWebService>

<?xml version="1.0"?>
<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform" xmlns:m2="http://www.w3.org/2001/XMLSchema-instance">
    <xsl:output method="xml" encoding="utf-8" indent="yes" omit-xml-declaration="no"/>
    <xsl:strip-space elements="*"/>

    <xsl:template match="@*|node()">
        <xsl:copy>
            <xsl:apply-templates select="@*|node()"/>
        </xsl:copy>
    </xsl:template>

    <xsl:template match="m2:custDetails">
        <m2:overrideScriptName>NewScript</m2:overrideScriptName>
        <xsl:copy-of select="."/>
    </xsl:template>
</xsl:stylesheet>

<?xml version='1.0' encoding='utf-8' ?>
<m2:InvokeWebService xmlns:m2="http://www.w3.org/2001/XMLSchema-instance">
  <m2:request>
    <m2:action>ADD</m2:action>
    <m2:commonDetails>
      <m2:needSupport>Y</m2:needSupport>
    </m2:commonDetails>
    <m2:overrideScriptName>NewScript</m2:overrideScriptName>
    <m2:custDetails>
      <m2:name>Tony,Hawk</m2:name>
      <m2:accountNumber>23232423566</m2:accountNumber>
      <m2:sensitiveCustomer>Y</m2:sensitiveCustomer>
    </m2:custDetails>
  </m2:request>
</m2:InvokeWebService>

-----------------------

<m2:InvokeWebService xmlns:m2="http://www.w3.org/2001/XMLSchema-instance">
    <m2:request>
        <m2:action>ADD</m2:action>
        <m2:commonDetails>
            <m2:needSupport>Y</m2:needSupport>
        </m2:commonDetails>
        <m2:custDetails>
            <m2:name>Tony,Hawk</m2:name>
            <m2:accountNumber>23232423566</m2:accountNumber>
            <m2:sensitiveCustomer>Y</m2:sensitiveCustomer>
        </m2:custDetails>
    </m2:request>
</m2:InvokeWebService>

<?xml version="1.0"?>
<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform" xmlns:m2="http://www.w3.org/2001/XMLSchema-instance">
    <xsl:output method="xml" encoding="utf-8" indent="yes" omit-xml-declaration="no"/>
    <xsl:strip-space elements="*"/>

    <xsl:template match="@*|node()">
        <xsl:copy>
            <xsl:apply-templates select="@*|node()"/>
        </xsl:copy>
    </xsl:template>

    <xsl:template match="m2:custDetails">
        <m2:overrideScriptName>NewScript</m2:overrideScriptName>
        <xsl:copy-of select="."/>
    </xsl:template>
</xsl:stylesheet>

<?xml version='1.0' encoding='utf-8' ?>
<m2:InvokeWebService xmlns:m2="http://www.w3.org/2001/XMLSchema-instance">
  <m2:request>
    <m2:action>ADD</m2:action>
    <m2:commonDetails>
      <m2:needSupport>Y</m2:needSupport>
    </m2:commonDetails>
    <m2:overrideScriptName>NewScript</m2:overrideScriptName>
    <m2:custDetails>
      <m2:name>Tony,Hawk</m2:name>
      <m2:accountNumber>23232423566</m2:accountNumber>
      <m2:sensitiveCustomer>Y</m2:sensitiveCustomer>
    </m2:custDetails>
  </m2:request>
</m2:InvokeWebService>

How to add numbers to a two column tibble from a reference tibble?

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id_tibble$color_num <- NA
id_tibble$animals_num <- NA

#run the loop only for unique values in color
for(uq in unique(id_tibble$color)) {
  #get row position for this color value
  i <- which(id_tibble$color == uq)
  #assign the first available number
  id_tibble$color_num[i] <- available_numbers[1]
  #Get corresponding values of the number
  all_num <- compatible_numbers$main[compatible_numbers$key == available_numbers[1]]
  #Keep only the ones which are available
  all_num <- intersect(all_num, available_numbers)
  #Remove the color_num value
  available_numbers <- available_numbers[-1]
  #assign the animals_num value
  id_tibble$animals_num[i] <- all_num[seq_along(i)]
  #Drop the values which are assigned in animals_num
  available_numbers <- setdiff(available_numbers, id_tibble$animals_num[i])
}

#  color  animals  color_num animals_num
#  <chr>  <chr>        <int>       <int>
#1 blue   elephant         1           2
#2 orange tiger            3          25
#3 orange leon             3          26
#4 orange leopard          3          27
#5 yellow hawk             4           5
#6 black  hawk             6           7

See all related Code Snippets

Community Discussions

Trending Discussions on hawk

Is there an R function to help turn State abbreviations into full names? Or Vice Versa?
Error ' not supported between instances of float and str ' when I try to use .sort() on a Python list
My function isnt taking a string when its suppossed to
Retrieve value from IndexedDB using Dexie and Svelte
Decrypt message with random shift of letters
Adding a title to a plot that was made in an R function
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QUESTION

Is there an R function to help turn State abbreviations into full names? Or Vice Versa?

Asked 2022-Apr-18 at 03:52

I have two large-ish data frames I am trying to append...

In df1, I have state codes, county codes, state names (Alabama, Alaska, etc.), county names, and years from 2010:2020.

In df2, I have county names, state abbreviations (AL, AK), and data for the year 2010 (which I am trying to merge into df1. The issue lies in that without specifying the state name and simply merging df1 and df2, some of the data which I am trying to get into df1 is duplicated due to there being some counties with the same name...hence, I am trying to also join by state to prevent this, but I have state abbreviations, and state names.

Is there any way in which I can make either the state names in df1 abbreviations, or the state names in df2 full names? Please let me know! Thank you for the help.

Edit: dput(df2)

dput(birthdata2)
structure(list(County = c("Ada County", "Adams County", "Adams County", 
"Aiken County", "Alachua County", "Alamance County", "Alameda County", 
"Albany County", "Alexandria city", "Allegan County", "Allegheny County", 
"Allen County", "Allen County", "Anchorage Borough", "Anderson County", 
"Androscoggin County", "Anne Arundel County", "Anoka County", 
"Arapahoe County", "Arlington County", "Ascension Parish", "Ashtabula County", 
"Atlantic County", "Baldwin County", "Baltimore city", "Baltimore County", 
"Barnstable County", "Bartow County", "Bay County", "Bay County", 
"Beaufort County", "Beaver County", "Bell County", "Benton County", 
"Benton County", "Bergen County", "Berkeley County", "Berkeley County", 
"Berks County", "Berkshire County", "Bernalillo County", "Berrien County", 
"Bexar County", "Bibb County", "Black Hawk County", "Blair County", 
"Blount County", "Bonneville County", "Boone County", "Boone County", 
"Bossier Parish", "Boulder County", "Brazoria County", "Brazos County", 
"Brevard County", "Bristol County", "Bronx County", "Broome County", 
"Broward County", "Brown County", "Brunswick County", "Bucks County", 
"Buncombe County", "Burlington County", "Butler County", "Butler County", 
"Butte County", "Cabarrus County", "Cache County", "Caddo Parish", 
"Calcasieu Parish", "Calhoun County", "Calhoun County", "Cambria County", 
"Camden County", "Cameron County", "Canadian County", "Canyon County", 
"Cape May County", "Carroll County", "Carroll County", "Cass County", 
"Catawba County", "Cecil County", "Centre County", "Champaign County", 
"Charles County", "Charleston County", "Charlotte County", "Chatham County", 
"Chautauqua County", "Cherokee County", "Chesapeake city", "Chester County", 
"Chesterfield County", "Chittenden County", "Citrus County", 
"Clackamas County", "Clark County", "Clark County", "Clark County", 
"Clark County", "Clarke County", "Clay County", "Clay County", 
"Clayton County", "Clermont County", "Cleveland County", "Cobb County", 
"Cochise County", "Coconino County", "Collier County", "Collin County", 
"Columbia County", "Columbiana County", "Comal County", "Comanche County", 
"Contra Costa County", "Cook County", "Coweta County", "Cowlitz County", 
"Craven County", "Cumberland County", "Cumberland County", "Cumberland County", 
"Cumberland County", "Cuyahoga County", "Dakota County", "Dallas County", 
"Dane County", "Dauphin County", "Davidson County", "Davidson County", 
"Davis County", "DeKalb County", "DeKalb County", "Delaware County", 
"Delaware County", "Delaware County", "Denton County", "Denver County", 
"Deschutes County", "DeSoto County", "District of Columbia", 
"Dona Ana County", "Dorchester County", "Douglas County", "Douglas County", 
"Douglas County", "Douglas County", "Douglas County", "DuPage County", 
"Durham County", "Dutchess County", "Duval County", "East Baton Rouge Parish", 
"Eaton County", "Ector County", "El Dorado County", "El Paso County", 
"El Paso County", "Elkhart County", "Ellis County", "Erie County", 
"Erie County", "Escambia County", "Essex County", "Essex County", 
"Etowah County", "Fairfax County", "Fairfield County", "Fairfield County", 
"Faulkner County", "Fayette County", "Fayette County", "Fayette County", 
"Florence County", "Fond du Lac County", "Forsyth County", "Forsyth County", 
"Fort Bend County", "Franklin County", "Franklin County", "Franklin County", 
"Frederick County", "Fresno County", "Fulton County", "Galveston County", 
"Gaston County", "Genesee County", "Gloucester County", "Grayson County", 
"Greene County", "Greene County", "Greenville County", "Gregg County", 
"Guadalupe County", "Guilford County", "Gwinnett County", "Hall County", 
"Hamilton County", "Hamilton County", "Hamilton County", "Hampden County", 
"Hampshire County", "Hampton city", "Hardin County", "Harford County", 
"Harnett County", "Harris County", "Harrison County", "Hartford County", 
"Hawaii County", "Hays County", "Henderson County", "Hendricks County", 
"Hennepin County", "Henrico County", "Henry County", "Hernando County", 
"Hidalgo County", "Hillsborough County", "Hillsborough County", 
"Hinds County", "Honolulu County", "Horry County", "Houston County", 
"Houston County", "Howard County", "Hudson County", "Humboldt County", 
"Hunterdon County", "Imperial County", "Indian River County", 
"Ingham County", "Iredell County", "Jackson County", "Jackson County", 
"Jackson County", "Jackson County", "Jasper County", "Jefferson County", 
"Jefferson County", "Jefferson County", "Jefferson County", "Jefferson County", 
"Jefferson County", "Jefferson Parish", "Johnson County", "Johnson County", 
"Johnson County", "Johnson County", "Johnston County", "Kalamazoo County", 
"Kanawha County", "Kane County", "Kankakee County", "Kaufman County", 
"Kendall County", "Kennebec County", "Kenosha County", "Kent County", 
"Kent County", "Kent County", "Kenton County", "Kern County", 
"King County", "Kings County", "Kings County", "Kitsap County", 
"Knox County", "Kootenai County", "La Crosse County", "La Porte County", 
"Lackawanna County", "Lafayette Parish", "Lake County", "Lake County", 
"Lake County", "Lake County", "Lancaster County", "Lancaster County", 
"Lane County", "Larimer County", "LaSalle County", "Lebanon County", 
"Lee County", "Lee County", "Lehigh County", "Leon County", "Lexington County", 
"Licking County", "Linn County", "Linn County", "Litchfield County", 
"Livingston County", "Livingston Parish", "Lorain County", "Los Angeles County", 
"Loudoun County", "Lowndes County", "Lubbock County", "Lucas County", 
"Luzerne County", "Lycoming County", "Macomb County", "Macon County", 
"Madera County", "Madison County", "Madison County", "Madison County", 
"Mahoning County", "Manatee County", "Marathon County", "Maricopa County", 
"Marin County", "Marion County", "Marion County", "Marion County", 
"Martin County", "Maui County", "McHenry County", "McLean County", 
"McLennan County", "Mecklenburg County", "Medina County", "Merced County", 
"Mercer County", "Mercer County", "Merrimack County", "Mesa County", 
"Miami County", "Miami-Dade County", "Middlesex County", "Middlesex County", 
"Middlesex County", "Midland County", "Milwaukee County", "Minnehaha County", 
"Missoula County", "Mobile County", "Mohave County", "Monmouth County", 
"Monroe County", "Monroe County", "Monroe County", "Monroe County", 
"Monterey County", "Montgomery County", "Montgomery County", 
"Montgomery County", "Montgomery County", "Montgomery County", 
"Montgomery County", "Morgan County", "Morris County", "Multnomah County", 
"Muscogee County", "Muskegon County", "Napa County", "Nassau County", 
"Navajo County", "New Castle County", "New Hanover County", "New Haven County", 
"New London County", "New York County", "Newport News city", 
"Niagara County", "Norfolk city", "Norfolk County", "Northampton County", 
"Nueces County", "Oakland County", "Ocean County", "Okaloosa County", 
"Oklahoma County", "Olmsted County", "Oneida County", "Onondaga County", 
"Onslow County", "Ontario County", "Orange County", "Orange County", 
"Orange County", "Orange County", "Orleans Parish", "Osceola County", 
"Oswego County", "Ottawa County", "Ouachita Parish", "Outagamie County", 
"Palm Beach County", "Parker County", "Pasco County", "Passaic County", 
"Paulding County", "Pennington County", "Penobscot County", "Peoria County", 
"Philadelphia County", "Pickens County", "Pierce County", "Pima County", 
"Pinal County", "Pinellas County", "Pitt County", "Placer County", 
"Plymouth County", "Polk County", "Polk County", "Portage County", 
"Porter County", "Portsmouth city", "Potter County", "Prince George's County", 
"Prince William County", "Providence County", "Pueblo County", 
"Pulaski County", "Queens County", "Racine County", "Ramsey County", 
"Randall County", "Randolph County", "Rankin County", "Rapides Parish", 
"Rensselaer County", "Richland County", "Richland County", "Richmond city", 
"Richmond County", "Richmond County", "Riverside County", "Robeson County", 
"Rock County", "Rock Island County", "Rockingham County", "Rockland County", 
"Rowan County", "Rutherford County", "Sacramento County", "Saginaw County", 
"Saline County", "Salt Lake County", "San Bernardino County", 
"San Diego County", "San Francisco County", "San Joaquin County", 
"San Juan County", "San Luis Obispo County", "San Mateo County", 
"Sandoval County", "Sangamon County", "Santa Barbara County", 
"Santa Clara County", "Santa Cruz County", "Santa Fe County", 
"Santa Rosa County", "Sarasota County", "Saratoga County", "Sarpy County", 
"Schenectady County", "Schuylkill County", "Scott County", "Scott County", 
"Sebastian County", "Sedgwick County", "Seminole County", "Shasta County", 
"Shawnee County", "Sheboygan County", "Shelby County", "Shelby County", 
"Skagit County", "Smith County", "Snohomish County", "Solano County", 
"Somerset County", "Sonoma County", "Spartanburg County", "Spokane County", 
"Spotsylvania County", "St. Charles County", "St. Clair County", 
"St. Clair County", "St. Johns County", "St. Joseph County", 
"St. Lawrence County", "St. Louis city", "St. Louis County", 
"St. Louis County", "St. Lucie County", "St. Mary's County", 
"St. Tammany Parish", "Stafford County", "Stanislaus County", 
"Stark County", "Stearns County", "Strafford County", "Suffolk County", 
"Suffolk County", "Sullivan County", "Summit County", "Sumner County", 
"Sumter County", "Sussex County", "Sussex County", "Tangipahoa Parish", 
"Tarrant County", "Taylor County", "Tazewell County", "Terrebonne Parish", 
"Thurston County", "Tippecanoe County", "Tolland County", "Tom Green County", 
"Tompkins County", "Travis County", "Trumbull County", "Tulare County", 
"Tulsa County", "Tuscaloosa County", "Ulster County", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Unidentified Counties", 
"Unidentified Counties", "Unidentified Counties", "Union County", 
"Union County", "Utah County", "Vanderburgh County", "Ventura County", 
"Vigo County", "Virginia Beach city", "Volusia County", "Wake County", 
"Walworth County", "Warren County", "Warren County", "Warren County", 
"Washington County", "Washington County", "Washington County", 
"Washington County", "Washington County", "Washington County", 
"Washington County", "Washington County", "Washington County", 
"Washoe County", "Washtenaw County", "Waukesha County", "Wayne County", 
"Wayne County", "Wayne County", "Webb County", "Weber County", 
"Weld County", "Westchester County", "Westmoreland County", "Whatcom County", 
"Whitfield County", "Wichita County", "Will County", "Williamson County", 
"Williamson County", "Wilson County", "Windham County", "Winnebago County", 
"Winnebago County", "Wood County", "Woodbury County", "Worcester County", 
"Wright County", "Wyandotte County", "Yakima County", "Yavapai County", 
"Yellowstone County", "Yolo County", "York County", "York County", 
"York County", "Yuma County"), State = c(" ID", " CO", " PA", 
" SC", " FL", " NC", " CA", " NY", " VA", " MI", " PA", " IN", 
" OH", " AK", " SC", " ME", " MD", " MN", " CO", " VA", " LA", 
" OH", " NJ", " AL", " MD", " MD", " MA", " GA", " FL", " MI", 
" SC", " PA", " TX", " AR", " WA", " NJ", " SC", " WV", " PA", 
" MA", " NM", " MI", " TX", " GA", " IA", " PA", " TN", " ID", 
" KY", " MO", " LA", " CO", " TX", " TX", " FL", " MA", " NY", 
" NY", " FL", " WI", " NC", " PA", " NC", " NJ", " OH", " PA", 
" CA", " NC", " UT", " LA", " LA", " AL", " MI", " PA", " NJ", 
" TX", " OK", " ID", " NJ", " GA", " MD", " ND", " NC", " MD", 
" PA", " IL", " MD", " SC", " FL", " GA", " NY", " GA", " VA", 
" PA", " VA", " VT", " FL", " OR", " IN", " NV", " OH", " WA", 
" GA", " FL", " MO", " GA", " OH", " OK", " GA", " AZ", " AZ", 
" FL", " TX", " GA", " OH", " TX", " OK", " CA", " IL", " GA", 
" WA", " NC", " ME", " NC", " NJ", " PA", " OH", " MN", " TX", 
" WI", " PA", " NC", " TN", " UT", " GA", " IL", " IN", " OH", 
" PA", " TX", " CO", " OR", " MS", " DC", " NM", " SC", " CO", 
" GA", " KS", " NE", " OR", " IL", " NC", " NY", " FL", " LA", 
" MI", " TX", " CA", " CO", " TX", " IN", " TX", " NY", " PA", 
" FL", " MA", " NJ", " AL", " VA", " CT", " OH", " AR", " GA", 
" KY", " PA", " SC", " WI", " GA", " NC", " TX", " MO", " OH", 
" PA", " MD", " CA", " GA", " TX", " NC", " MI", " NJ", " TX", 
" MO", " OH", " SC", " TX", " TX", " NC", " GA", " GA", " IN", 
" OH", " TN", " MA", " MA", " VA", " KY", " MD", " NC", " TX", 
" MS", " CT", " HI", " TX", " NC", " IN", " MN", " VA", " GA", 
" FL", " TX", " FL", " NH", " MS", " HI", " SC", " AL", " GA", 
" MD", " NJ", " CA", " NJ", " CA", " FL", " MI", " NC", " MI", 
" MO", " MS", " OR", " MO", " AL", " CO", " KY", " MO", " NY", 
" TX", " LA", " IA", " IN", " KS", " TX", " NC", " MI", " WV", 
" IL", " IL", " TX", " IL", " ME", " WI", " DE", " MI", " RI", 
" KY", " CA", " WA", " CA", " NY", " WA", " TN", " ID", " WI", 
" IN", " PA", " LA", " FL", " IL", " IN", " OH", " NE", " PA", 
" OR", " CO", " IL", " PA", " AL", " FL", " PA", " FL", " SC", 
" OH", " IA", " OR", " CT", " MI", " LA", " OH", " CA", " VA", 
" GA", " TX", " OH", " PA", " PA", " MI", " IL", " CA", " AL", 
" IL", " IN", " OH", " FL", " WI", " AZ", " CA", " FL", " IN", 
" OR", " FL", " HI", " IL", " IL", " TX", " NC", " OH", " CA", 
" NJ", " PA", " NH", " CO", " OH", " FL", " CT", " MA", " NJ", 
" TX", " WI", " SD", " MT", " AL", " AZ", " NJ", " IN", " MI", 
" NY", " PA", " CA", " AL", " MD", " OH", " PA", " TN", " TX", 
" AL", " NJ", " OR", " GA", " MI", " CA", " NY", " AZ", " DE", 
" NC", " CT", " CT", " NY", " VA", " NY", " VA", " MA", " PA", 
" TX", " MI", " NJ", " FL", " OK", " MN", " NY", " NY", " NC", 
" NY", " CA", " FL", " NC", " NY", " LA", " FL", " NY", " MI", 
" LA", " WI", " FL", " TX", " FL", " NJ", " GA", " SD", " ME", 
" IL", " PA", " SC", " WA", " AZ", " AZ", " FL", " NC", " CA", 
" MA", " FL", " IA", " OH", " IN", " VA", " TX", " MD", " VA", 
" RI", " CO", " AR", " NY", " WI", " MN", " TX", " NC", " MS", 
" LA", " NY", " OH", " SC", " VA", " GA", " NY", " CA", " NC", 
" WI", " IL", " NH", " NY", " NC", " TN", " CA", " MI", " AR", 
" UT", " CA", " CA", " CA", " CA", " NM", " CA", " CA", " NM", 
" IL", " CA", " CA", " CA", " NM", " FL", " FL", " NY", " NE", 
" NY", " PA", " IA", " MN", " AR", " KS", " FL", " CA", " KS", 
" WI", " AL", " TN", " WA", " TX", " WA", " CA", " NJ", " CA", 
" SC", " WA", " VA", " MO", " IL", " MI", " FL", " IN", " NY", 
" MO", " MN", " MO", " FL", " MD", " LA", " VA", " CA", " OH", 
" MN", " NH", " MA", " NY", " TN", " OH", " TN", " SC", " DE", 
" NJ", " LA", " TX", " TX", " IL", " LA", " WA", " IN", " CT", 
" TX", " NY", " TX", " OH", " CA", " OK", " AL", " NY", " AK", 
" AL", " AR", " AZ", " CA", " CO", " FL", " GA", " HI", " IA", 
" ID", " IL", " IN", " KS", " KY", " LA", " MA", " MD", " ME", 
" MI", " MN", " MO", " MS", " MT", " NC", " ND", " NE", " NH", 
" NJ", " NM", " NV", " NY", " OH", " OK", " OR", " PA", " RI", 
" SC", " SD", " TN", " TX", " UT", " VA", " VT", " WA", " WI", 
" WV", " WY", " NC", " NJ", " UT", " IN", " CA", " IN", " VA", 
" FL", " NC", " WI", " KY", " NJ", " OH", " AR", " MD", " MN", 
" OR", " PA", " RI", " TN", " UT", " WI", " NV", " MI", " WI", 
" MI", " NC", " OH", " TX", " UT", " CO", " NY", " PA", " WA", 
" GA", " TX", " IL", " TN", " TX", " TN", " CT", " IL", " WI", 
" OH", " IA", " MA", " MN", " KS", " WA", " AZ", " MT", " CA", 
" ME", " PA", " SC", " AZ"), x = c(358, 549, NA, 149, 219, 178, 
1367, 257, 194, 76, 1032, 432, 114, 278, 177, 69, 574, 265, 701, 
208, NA, 77, 267, 176, 1044, 826, 107, NA, 179, 78, 193, 119, 
538, 246, 162, 765, 202, NA, 409, 84, 778, 177, 2441, 321, 123, 
101, 91, NA, NA, 173, NA, 228, 390, 192, 372, 472, 2190, 173, 
1930, 209, NA, 469, 194, 379, 340, 112, 146, 225, NA, 573, 258, 
103, 87, 119, 531, 622, NA, 187, 56, NA, 78, 141, 155, NA, 76, 
198, 164, 419, 74, 378, 99, 240, 222, 371, 298, 95, 77, 243, 
NA, 2217, 101, 340, 116, 170, 206, 437, 202, 228, 790, 140, 129, 
248, 844, NA, 73, NA, 155, 851, 6598, NA, NA, NA, 164, 575, 219, 
152, 1565, 319, 3336, 388, 335, 179, 825, 374, 1104, NA, 97, 
131, 537, 621, 915, 83, 141, 934, 240, NA, 353, NA, NA, 680, 
76, 789, 406, 187, 1171, 732, 72, 249, 115, 857, 1265, 235, 132, 
788, 305, 412, 569, 1087, 93, 1060, 776, 87, NA, NA, 319, 137, 
220, NA, NA, 477, 701, NA, 1676, 138, 237, 1266, 1467, 338, 248, 
523, 253, 109, 264, 137, 501, 167, NA, 562, 863, 221, 256, 1043, 
436, 451, 74, 177, NA, 202, NA, 6004, 295, 835, 180, NA, NA, 
111, 1167, 352, 221, 110, 1284, 1484, 317, 553, 1181, 311, NA, 
185, 259, 842, 76, 70, 182, 89, 257, 148, 156, 795, 159, 150, 
109, 998, 486, 972, 179, 130, 368, 551, 114, 100, 463, 169, 167, 
245, 182, 518, 99, NA, NA, 70, 155, 173, 654, 102, 199, 1010, 
1602, 158, 3530, 189, 449, 105, 65, 123, 177, 289, 230, 644, 
618, 147, 302, 517, 215, 257, 73, 97, 117, 488, 333, 267, 274, 
157, 178, 79, 118, 108, NA, 231, 9714, 345, NA, 399, 562, 272, 
73, 766, 118, 164, 446, 255, 112, 242, 262, 68, 3848, 141, 267, 
1403, 279, 77, 144, 263, 155, 255, 1340, 87, 285, 403, 72, 93, 
110, NA, 2862, 72, 1389, 853, 167, 1329, 200, NA, 694, 160, 472, 
79, 99, 722, 145, 370, 348, 1026, 634, 612, 257, 474, 134, 355, 
629, 348, 164, 82, 1137, NA, 631, 176, 833, 190, 1737, 266, 166, 
379, 479, 281, 377, 1045, 530, 185, 1095, 115, 186, 429, 304, 
76, 2459, 1373, 108, 345, 571, 301, 95, 183, 253, 157, 1238, 
NA, 396, 590, NA, NA, 78, 198, 2523, 65, 633, 853, 328, 771, 
197, 206, 369, 601, 504, 119, 74, 155, 175, 1243, 497, 602, 174, 
537, 2557, 174, 516, 108, 131, 225, 156, 111, 109, 566, 336, 
336, 466, 1989, 235, 165, 143, 170, 309, 150, 307, 1419, 202, 
NA, 1323, 2224, 2895, 622, 746, 109, 121, 655, NA, 237, 325, 
1648, 178, 135, 117, 213, 144, 147, 153, 100, 142, NA, 137, 688, 
343, 104, 177, 59, 203, 1526, 64, 238, 536, 320, 279, 307, 390, 
387, NA, 326, 320, 133, 106, 294, 70, 597, 118, 1005, 271, NA, 
204, NA, 464, 343, 93, 81, 860, 1301, 151, 552, 129, 176, 201, 
103, 202, 2341, 146, 92, 173, 149, 135, 68, 113, NA, 1250, 167, 
508, 809, 283, 115, 369, 2516, 2223, 401, 599, 849, 1237, 5841, 
57, 1503, 911, 1908, 2311, 1294, 3545, 2704, 51, 625, 268, 1538, 
1605, 2599, 3450, 744, 3550, 464, 696, 201, 45, 1143, 297, 1472, 
2483, 2155, 643, 1334, 61, 1797, 604, 2718, 5530, 868, 3426, 
276, 944, 1483, 1696, 673, 202, 570, 752, 184, 686, 71, 485, 
389, 1018, NA, NA, 53, 178, 232, 155, 157, 390, 163, 56, 106, 
NA, 59, 448, 318, 284, 2572, 121, 100, 398, 328, 267, 965, 251, 
103, NA, 150, 643, 125, 458, NA, 68, 321, 142, 75, 106, 653, 
NA, 239, 292, 110, 141, 125, 113, 425, 256, 197), year = c(2010, 
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
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2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
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2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
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))

ANSWER

Answered 2022-Apr-18 at 03:52

Here's one way you could turn state abbreviations into state names using R's built in state vectors:

a <- c('NJ', 'MA', 'FL')
state.name[sapply(a, \(x) which(x == state.abb))]

[1] "New Jersey"    "Massachusetts" "Florida"

Applying this to birthdata2, we might create a column called state_name. But first we need to trim whitespace from the State column:

birthdata2$State <- trimws(birthdata2$State)
birthdata2$state_name <- state.name[sapply(birthdata2$State, \(x) which(x == state.abb)[1])]

   County          State     x  year state_name    
   <chr>           <chr> <dbl> <dbl> <chr>         
 1 Ada County      ID      358  2010 Idaho         
 2 Adams County    CO      549  2010 Colorado      
 3 Adams County    PA       NA  2010 Pennsylvania  
 4 Aiken County    SC      149  2010 South Carolina
 5 Alachua County  FL      219  2010 Florida       
 6 Alamance County NC      178  2010 North Carolina
 7 Alameda County  CA     1367  2010 California    
 8 Albany County   NY      257  2010 New York      
 9 Alexandria city VA      194  2010 Virginia      
10 Allegan County  MI       76  2010 Michigan      
# … with 618 more rows

Source https://stackoverflow.com/questions/71907258

QUESTION

Error ' not supported between instances of float and str ' when I try to use .sort() on a Python list

Asked 2022-Apr-01 at 04:45

The error is occurring when I try to sort this data list:

    para_planilha = [['2022-04-01T17:00:00Z', '2022/04/01 00:58:25', '-', 'ATSV Erlangen', 'FC Eintracht Bamberg', '-', '-', '-', '-', '-'], ['2022-04-02T03:05:00Z', '2022/04/01 00:58:27', '-', 'Western United', 'Central Coast Mariners', 2.08, 3.75, 3.7, 2.06, 1.78], ['2022-04-01T18:45:00Z', '2022/04/01 00:58:28', '-', 'Kfco Beerschot Wilrijk', 'Club Brugge', 11.0, 1.33, 6.4, 2.5, 1.58], ['2022-04-01T18:30:00Z', '2022/04/01 00:58:29', '-', 'CA Fenix', 'Deportivo Merlo', 3.4, 2.32, 3.85, '-', '-'], ['2022-04-01T23:30:00Z', '2022/04/01 00:58:30', '-', 'CA Ituzaingo', 'CA Colegiales', '-', '-', '-', '-', 3.4], ['2022-04-01T18:45:00Z', '2022/04/01 00:58:31', '-', 'Edinburgh City', 'Kelty Hearts', 4.6, 1.72, 5.5, 2.02, '-'], ['2022-04-01T11:00:00Z', '2022/04/01 00:58:32', '-', 'Navy FC', 'Khonkaen', 4.3, 2.02, 3.7, 1.85, 1.82], ['2022-04-01T16:00:00Z', '2022/04/01 00:58:33', '-', 'Samger FC', 'Banjul United', '-', '-', '-', '-', '-'], ['2022-04-01T18:45:00Z', '2022/04/01 00:58:34', '-', 'Connahs Quay', 'Aberystwyth', '-', '-', '-', '-', '-'], ['2022-04-01T18:45:00Z', '2022/04/01 00:58:35', '-', 'Caernarfon Town', 'The New Saints', '-', '-', '-', '-', '-'], ['2022-04-01T17:00:00Z', '2022/04/01 00:58:36', '-', 'Tuzlaspor', 'Umraniyespor', 4.3, 1.96, 3.85, 1.78, 2.26], ['2022-04-01T17:30:00Z', '2022/04/01 00:58:37', '-', 'Yverdon Sport', 'FC Vaduz', 2.22, 3.45, 3.8, 2.06, 1.95], ['2022-04-01T17:30:00Z', '2022/04/01 00:58:38', '-', 'Rot-Weiss Essen', 'FC Koln II', '-', '-', '-', 4.3, 1.3], ['2022-04-01T15:45:00Z', '2022/04/01 00:58:38', '-', 'Herstedoester IC', 'Avarta', '-', '-', '-', '-', '-'], ['2022-04-01T17:00:00Z', '2022/04/01 00:58:39', '-', 'SV Tasmania Berlin', 'Berliner AK', '-', 1.57, 4.8, 2.1, 1.86], ['2022-04-01T16:00:00Z', '2022/04/01 00:58:40', '-', 'Tallinna Kalev', 'Paide Linnameeskond', 80.0, '-', '-', 3.35, 1.33], ['2022-04-01T12:00:00Z', '2022/04/01 00:58:41', '-', 'Millwall U23', 'Peterborough United U23', 2.18, 2.96, 3.5, 2.56, 1.48], ['2022-04-01T18:45:00Z', '2022/04/01 00:58:42', '-', 'Hull', 'Huddersfield', 2.88, 2.84, 3.35, 1.74, 2.34], ['2022-04-01T23:00:00Z', '2022/04/01 00:58:43', '-', 'Palmeiras (W)', 'CR Flamengo (W)', 1.56, 6.0, 4.1, '-', 1.88], ['2022-04-01T16:00:00Z', '2022/04/01 00:58:44', '-', 'SFC Opava', 'Vysocina Jihlava', 1.71, 4.6, '-', '-', 1.75], ['2022-04-02T00:10:00Z', '2022/04/01 00:58:45', '-', 'Belgrano', 'All Boys', '-', '-', 3.75, '-', '-'], ['2022-04-01T16:00:00Z', '2022/04/01 00:58:46', '-', 'FC Zbrojovka Brno', 'Pribram', 1.45, 10.5, 4.3, '-', 1.64], ['2022-04-01T14:30:00Z', '2022/04/01 00:58:47', '-', 'FK Nevezis', 'BFA Vilnius', '-', '-', '-', 4.5, '-'], ['2022-04-02T00:15:00Z', '2022/04/01 00:58:48', '-', 'Resistencia SC', 'Olimpia', '-', 1.84, '-', '-', 1.78], ['2022-04-01T14:00:00Z', '2022/04/01 00:58:49', '-', 'FCV Farul Constanta II', 'CS Gloria Albesti', '-', '-', '-', '-', '-'], ['2022-04-01T14:00:00Z', '2022/04/01 00:58:51', '-', 'CSM Deva', 'Vointa Lupac', '-', '-', '-', 2.14, '-'], ['2022-04-01T22:00:00Z', '2022/04/01 00:58:52', '-', 'Cerro Porteno', '12 de Octubre de Itaugua', 1.42, '-', '-', '-', 1.7], ['2022-04-01T14:00:00Z', '2022/04/01 00:58:53', '-', 'Csm Slatina', 'ACS Vedita Colonesti MS', '-', '-', '-', '-', '-'], ['2022-04-01T14:00:00Z', '2022/04/01 00:58:53', '-', 'CSM Flacara Moreni', 'ACS FC Academica Clinceni 2', '-', '-', '-', '-', '-'], [2.36, '-'], ['2022-04-01T14:00:00Z', '2022/04/01 00:58:55', '-', 'Dinamo Bucuresti II', 'CS Real Bradu', 1.93, '-', '-', '-', '-'], ['2022-04-01T14:00:00Z', '2022/04/01 00:58:56', '-', 'FCSB II', 'FC Arges II', '-', '-', '-', '-', '-'], ['2022-04-01T18:00:00Z', '2022/04/01 00:58:57', '-', 'Mezokovesd-Zsory', 'Gyirmot', 2.16, 4.6, 3.35, 1.78, 2.26], ['2022-04-01T14:00:00Z', '2022/04/01 00:58:58', '-', 'CFR Cluj SA II', 'CSM Satu Mare', '-', '-', '-', 2.12, '-'], ['2022-04-02T00:00:00Z', '2022/04/01 00:58:59', '-', 'Estudiantes Rio Cuarto', 'Atletico Rafaela', 2.34, '-', 3.0, '-', '-'], ['2022-04-02T00:30:00Z', '2022/04/01 00:59:00', '-', 'Gimnasia Jujuy', 'Chaco For Ever', '-', 2.96, 2.8, '-', '-'], ['2022-04-01T18:00:00Z', '2022/04/01 00:59:01', '-', 'Fulham U23', 'Birmingham U23', 1.41, '-', '-', 2.8, '-'], ['2022-04-01T19:00:00Z', '2022/04/01 00:59:02', '-', 'Girona', 'Malaga', 1.58, 7.4, 4.5, 1.76, '-'], ['2022-04-01T18:00:00Z', '2022/04/01 00:59:04', '-', 'Boca Juniors (W)', 'Gimnasia de La Plata (W)', '-', '-', '-', '-', '-'], ['2022-04-01T22:00:00Z', '2022/04/01 00:59:05', '-', 'Patronato', 'Union Santa Fe', 3.95, 2.28, 3.4, '-', '-'], ['2022-04-01T17:30:00Z', '2022/04/01 00:59:06', '-', 'Schaffhausen', 'Neuchatel Xamax', 1.86, 4.6, 3.75, 2.12, '-'], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:07', '-', 'Krka', 'Krsko', '-', '-', '-', '-', '-'], ['2022-04-01T18:15:00Z', '2022/04/01 00:59:08', '-', 'FC Wil', 'Aarau', 3.75, 1.96, 4.5, 2.74, 1.57], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:09', '-', 'BK Hacken (W)', 'Hammarby (W)', 1.41, 8.6, 5.6, 2.48, '-'], ['2022-04-01T15:00:00Z', '2022/04/01 00:59:10', '-', 'FK Spartaks', 'Liepajas Metalurgs', '-', '-', '-', '-', '-'], ['2022-04-01T16:30:00Z', '2022/04/01 00:59:11', '-', 'Triglav', 'Drava (Slovenia)', '-', 24.0, '-', 3.2, 1.49], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:12', '-', 'Assyriska BK', 'Varbergs GIF', 2.34, '-', '-', '-', '-'], ['2022-04-02T01:00:00Z', '2022/04/01 00:59:13', '-', 'San Francisco FC', 'Plaza Amador', '-', '-', 2.86, '-', '-'], ['2022-04-01T14:15:00Z', '2022/04/01 00:59:14', '-', 'Khaitan', 'Al Yarmouk', 2.16, '-', 3.65, '-', '-'], ['2022-04-01T10:45:00Z', '2022/04/01 00:59:15', '-', 'Maccabi Kabilio Jaffa', 'AS Nordia Jerusalem', '-', 15.0, 6.2, 1.89, 1.93], ['2022-04-01T15:00:00Z', '2022/04/01 00:59:16', '-', 'FC Viktoria Otrokovice', 'H Slavia Kromeriz', '-', '-', '-', '-', '-'], ['2022-04-01T10:45:00Z', '2022/04/01 00:59:17', '-', 'Hapoel Azor', 'Ironi Kuseife', 1.9, 3.55, 3.25, 1.78, '-'], ['2022-04-01T14:30:00Z', '2022/04/01 00:59:18', '-', 'FC U Craiova 1948', 'CS Mioveni', 2.06, 4.8, 3.2, 1.45, '-'], ['2022-04-01T09:00:00Z', '2022/04/01 00:59:19', '-', 'Hapoel Bikat Hayarden', 'Hapoel Bnei Lod', '-', 7.0, 5.1, 1.97, '-'], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:20', '-', 'Toolon Taisto', 'Sexy Poxyt', '-', '-', '-', '-', '-'], ['2022-04-01T17:30:00Z', '2022/04/01 00:59:21', '-', 'Chindia Targoviste', 'Dinamo Bucharest', 2.04, 5.3, 3.35, '-', 2.8], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:22', '-', 'PuiU Helsinki', 'HIFK 2', '-', '-', '-', '-', '-'], ['2022-04-01T18:15:00Z', '2022/04/01 00:59:22', '-', 'Sporting Cristal', 'Uni San Martin', '-', 9.8, 6.2, 2.5, 1.57], ['2022-04-01T18:00:00Z', '2022/04/01 00:59:23', '-', 'Siegburger SV\xa004', 'Borussia Freialdenhoven', '-', '-', '-', '-', '-'], ['2022-04-01T16:00:00Z', '2022/04/01 00:59:25', '-', 'Markez Tulkrem', 'Hilal Al Quds', 7.8, 1.55, 4.7, 2.5, 1.5], ['2022-04-01T16:30:00Z', '2022/04/01 00:59:25', '-', 'Kolubara Lazarevac', 'Mladost Lucani', '-', '-', '-', 1.89, 1.96], ['2022-04-01T16:30:00Z', '2022/04/01 00:59:27', '-', 'Fredericia', 'Hvidovre', 2.26, 3.3, 3.8, '-', 1.84], ['2022-04-01T16:00:00Z', '2022/04/01 00:59:27', '-', 'Thisted (W)', 'Fortuna Hjorring (W)', '-', '-', '-', '-', '-'], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:29', '-', 'Fremad Amager', 'Jammerbught FC', 1.57, 7.0, 4.5, 2.1, 1.9], ['2022-04-01T13:00:00Z', '2022/04/01 00:59:30', '-', 'Maccabi Ahi Nazareth', 'Beitar Tel Aviv Ramla', 2.56, 3.55, 3.1, '-', '-'], ['2022-04-02T00:40:00Z', '2022/04/01 00:59:31', '-', 'La Equidad', 'Deportivo Cali', '-', 3.8, 2.94, 1.54, '-'], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:31', '-', 'Viborg', 'SonderjyskE', 1.78, 5.5, 3.85, 1.99, 2.0], ['2022-04-01T18:30:00Z', '2022/04/01 00:59:32', '-', 'Podbeskidzie B-B', 'LKS Lodz', 2.32, 3.85, 3.3, '-', '-'], ['2022-04-01T18:45:00Z', '2022/04/01 00:59:34', '-', 'Sligo Rovers', 'Shelbourne', 1.82, 5.6, 3.7, '-', '-'], ['2022-04-01T17:30:00Z', '2022/04/01 00:59:34', '-', 'ASV Drassburg', 'SC Mannsdorf', '-', '-', '-', '-', '-'], ['2022-04-01T19:00:00Z', '2022/04/01 00:59:35', '-', 'Alafoss', 'UMF Tindstoll', '-', '-', '-', '-', '-'], ['2022-04-01T19:15:00Z', '2022/04/01 00:59:36', '-', 'IR Reykjavik', 'Throttur', '-', '-', '-', '-', '-'], ['2022-04-01T16:00:00Z', '2022/04/01 00:59:37', '-', 'Gambia Ports Authority', 'Banjul Hawks FC', '-', '-', '-', '-', '-'], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:38', '-', 'Red Star', 'Creteil', 1.63, '-', '-', '-', 2.2], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:39', '-', 'FC Bastia-Borgo', 'Stade Briochin', '-', 2.72, 3.05, 1.62, 1.45], ['2022-04-01T15:30:00Z', '2022/04/01 00:59:40', '-', 'Moghreb Tetouan', 'Raja Beni Mellal', '-', '-', '-', '-', '-'], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:41', '-', 'Orleans', 'Le Mans', 2.42, '-', 3.25, 1.78, 2.28], ['2022-04-01T19:15:00Z', '2022/04/01 00:59:42', '-', 'Braga', 'Benfica', 3.4, 2.24, 3.6, 2.06, 1.84], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:43', '-', 'Annecy', 'Avranches', 1.58, 7.4, 3.75, 1.71, 2.26], ['2022-04-01T18:00:00Z', '2022/04/01 00:59:44', '-', 'Lierse', 'Waasland-Beveren', 3.2, 2.28, 3.8, '-', 1.75], ['2022-04-01T15:30:00Z', '2022/04/01 00:59:45', '-', 'Stade Marocain du Rabat', 'Waf Widad Fes', '-', 5.6, 3.2, '-', '-'], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:45', '-', 'Boulogne', 'Sete', 2.46, 3.8, 2.94, 1.56, 2.56], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:46', '-', 'Chambly Oise', 'Chateauroux', 4.3, 2.2, 3.3, 1.63, 2.36], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:47', '-', 'Concarneau', 'Bourg-en-Bresse', '-', '-', 3.1, 1.55, '-'], ['2022-04-01T17:00:00Z', '2022/04/01 00:59:48', '-', 'Villefranche Beaujolais', 'Cholet SO', 1.63, 7.0, '-', 1.92, 2.02], ['2022-04-01T16:00:00Z', '2022/04/01 00:59:49', '-', 'Sandecja Nowy Sacz', 'Korona Kielce', '-', '-', 3.05, 1.68, '-'], ['2022-04-01T17:30:00Z', '2022/04/01 00:59:50', '-', 'Alkmaar (W)', 'Feyenoord (W)', '-', '-', '-', 2.9, 1.55], ['2022-04-01T17:30:00Z', '2022/04/01 00:59:51', '-', 'ADO Den Haag (W)', 'FC Zwolle (W)', 1.74, '-', '-', '-', '-'], ['2022-04-01T18:45:00Z', '2022/04/01 00:59:52', '-', 'Wexford F.C', 'Cork City', '-', 1.89, '-', '-', '-'], ['2022-04-01T17:30:00Z', '2022/04/01 00:59:53', '-', 'PSV Eindhoven (W)', 'Heerenveen (W)', 1.32, '-', '-', 3.5, '-'], ['2022-04-01T18:45:00Z', '2022/04/01 00:59:54', '-', 'Waterford', 'Longford', 1.59, 5.9, 4.4, '-', 1.9], ['2022-04-01T17:30:00Z', '2022/04/01 00:59:55', '-', 'Excelsior (W)', 'Twente (W)', '-', 1.13, '-', '-', 1.17], ['2022-04-02T00:00:00Z', '2022/04/01 00:59:56', '-', 'Univ Catolica (Ecu)', 'Barcelona (Ecu)', 2.54, 3.0, 3.3, '-', '-'], ['2022-04-02T02:00:00Z', '2022/04/01 00:59:57', '-', 'Emelec', 'LDU', '-', 3.65, '-', 2.34, '-'], ['2022-04-01T18:45:00Z', '2022/04/01 00:59:58', '-', 'St Patricks', 'Drogheda', 1.46, 9.0, 4.8, '-', '-'], ['2022-04-01T18:45:00Z', '2022/04/01 00:59:59', '-', 'Ayr', 'Hamilton', 2.78, 2.94, 3.25, '-', '-'], ['2022-04-01T12:15:00Z', '2022/04/01 01:00:00', '-', 'Shimshon Tel Aviv', 'Maccabi Ashdod B.C.', '-', '-', 3.2, 1.8, '-'], ['2022-04-01T14:00:00Z', '2022/04/01 01:00:01', '-', 'Shabab Al Khaleel', 'Shabab Al Dharia', 1.5, '-', '-', 1.99, '-'], ['2022-04-01T17:00:00Z', '2022/04/01 01:00:02', '-', 'SV Meppen', 'Wehen Wiesbaden', 3.9, 2.04, 3.9, 2.08, 1.94], ['2022-04-01T18:30:00Z', '2022/04/01 01:00:03', '-', 'Union Berlin', 'FC Koln', 2.44, 3.35, 3.5, 1.92, 2.04], ['2022-04-01T18:45:00Z', '2022/04/01 01:00:04', '-', 'Morton', 'Kilmarnock', 4.4, 2.0, 3.4, '-', '-'], ['2022-04-02T00:00:00Z', '2022/04/01 01:00:05', '-', 'Universidad Guadalajara', 'Tampico Madero', 1.9, 5.0, 3.45, 1.77, 2.14], ['2022-04-01T14:00:00Z', '2022/04/01 01:00:06', '-', 'CS Constantine', 'Paradou', 1.86, 4.4, '-', '-', 2.62], ['2022-04-01T17:30:00Z', '2022/04/01 01:00:07', '-', 'FCM TQS Traiskirchen', 'SC Wiener Neustadt', '-', '-', '-', '-', '-'], ['2022-04-01T17:00:00Z', '2022/04/01 01:00:08', '-', 'SC Kalsdorf', 'SC Weiz', 2.12, '-', '-', '-', '-'], ['2022-04-01T17:30:00Z', '2022/04/01 01:00:09', '-', 'Wiener SK', 'Ask-Bsc Bruck/Leitha', 1.64, '-', '-', '-', '-'], ['2022-04-01T10:30:00Z', '2022/04/01 01:00:10', '-', 'Hapoel Marmorek', 'Bnei Eilat', 1.57, 5.6, '-', 2.06, 1.85], ['2022-04-01T18:45:00Z', '2022/04/01 01:00:11', '-', 'Dundalk', 'UCD', 1.43, 9.0, '-', 2.02, 1.76], ['2022-04-01T19:00:00Z', '2022/04/01 01:00:12', '-', 'Finn Harps', 'Shamrock Rovers', 6.0, 1.66, '-', 1.75, 2.34], ['2022-04-01T20:00:00Z', '2022/04/01 01:00:13', '-', 'Deportivo Santani', 'Club F de la Mora', '-', '-', '-', '-', '-'], ['2022-04-01T18:45:00Z', '2022/04/01 01:00:14', '-', 'Bohemians', 'Derry City', 2.8, 2.88, 3.35, '-', '-'], ['2022-04-01T17:00:00Z', '2022/04/01 01:00:15', '-', 'FC Gleisdorf 09', 'RZ Pellets WAC (Am)', '-', '-', '-', '-', '-'], ['2022-04-01T16:00:00Z', '2022/04/01 01:00:16', '-', 'FK Velez Mostar', 'Siroki Brijeg', 2.24, 4.3, '-', 1.72, '-'], ['2022-04-01T14:00:00Z', '2022/04/01 01:00:16', '-', 'ASO Chlef', 'US Biskra', 1.8, 7.2, 3.15, '-', 2.86], ['2022-04-01T16:30:00Z', '2022/04/01 01:00:17', '-', 'FC Wacker Innsbruck', 'Floridsdorfer Ac', 2.34, 3.25, 3.8, '-', 1.95], ['2022-04-01T16:30:00Z', '2022/04/01 01:00:18', '-', 'SK Vorwarts Steyr', 'Austria Wien (A)', 2.48, 2.82, 3.9, '-', 1.88], ['2022-04-01T14:30:00Z', '2022/04/01 01:00:19', '-', 'MC Alger', 'Medea', 1.46, '-', '-', 1.72, 2.3], ['2022-04-01T16:30:00Z', '2022/04/01 01:00:20', '-', 'FC Juniors OO', 'SV Horn', 2.76, 2.58, 3.55, 2.1, 1.79], ['2022-04-01T17:30:00Z', '2022/04/01 01:00:21', '-', 'SV Worgl', 'SV Telfs I', '-', '-', '-', '-', '-'], ['2022-04-01T18:25:00Z', '2022/04/01 01:00:22', '-', 'Grazer AK', 'FC Blau Weiss Linz', '-', 2.1, 3.7, 1.99, '-'], ['2022-04-02T02:00:00Z', '2022/04/01 01:00:23', '-', 'San Luis', 'Mazatlan FC', 1.9, 5.3, 3.55, 1.6, 2.42], ['2022-04-01T14:30:00Z', '2022/04/01 01:00:24', '-', 'NC Magra', 'NA Hussein Dey', 2.3, '-', 2.72, 1.48, '-'], ['2022-04-01T16:30:00Z', '2022/04/01 01:00:25', '-', 'SKU Amstetten', 'SC AUSTRIA LUSTENAU', 3.45, 2.08, 4.1, '-', 1.63], ['2022-04-01T18:30:00Z', '2022/04/01 01:00:26', '-', 'Gent (W)', 'Femina WS (W)', '-', '-', '-', 3.9, '-'], ['2022-04-01T16:30:00Z', '2022/04/01 01:00:27', '-', 'SV Lafnitz', 'Ksv 1919', 1.53, 9.2, 4.9, '-', 1.76], ['2022-04-01T17:00:00Z', '2022/04/01 01:00:28', '-', 'Kabylie', 'USM Alger', 1.86, 7.4, 3.25, 1.48, 2.62], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:29', '-', 'FC Dordrecht', 'FC Eindhoven', 5.5, 1.72, 4.3, 2.42, 1.69], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:30', '-', 'Helmond Sport', 'MVV Maastricht', 2.06, 3.55, 4.1, 2.5, 1.59], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:31', '-', 'FC Volendam', 'FC Oss', 1.54, 6.8, 4.8, 2.68, 1.52], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:32', '-', 'Roda JC', 'Jong FC Utrecht', 1.31, 13.5, 6.6, 2.94, '-'], ['2022-04-01T14:15:00Z', '2022/04/01 01:00:33', '-', 'Fk Novi Pazar', 'FK Napredak', 2.68, 2.9, 3.3, 1.92, 2.16], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:34', '-', 'Jong AZ Alkmaar', 'Jong PSV Eindhoven', 3.65, 2.18, 3.9, 2.48, 1.67], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:35', '-', 'Almere City', 'Excelsior', 2.56, 2.72, 4.2, 2.62, 1.57], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:36', '-', 'NAC Breda', 'Jong Ajax Amsterdam', 2.42, 2.78, 4.5, 3.0, 1.44], ['2022-04-01T13:00:00Z', '2022/04/01 01:00:38', '-', 'Agudat Sport Ashdod', 'Bnei Yehuda', 2.58, 3.65, 3.0, '-', '-'], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:39', '-', 'De Graafschap', 'Emmen', 2.86, 2.8, 3.4, 1.79, 2.14], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:40', '-', 'VVV Venlo', 'ADO Den Haag', 2.9, 2.42, 4.0, 2.32, 1.76], ['2022-04-01T13:00:00Z', '2022/04/01 01:00:41', '-', 'Hapoel Akko', 'Hapoel Afula', '-', 3.45, 3.25, '-', '-'], ['2022-04-01T15:00:00Z', '2022/04/01 01:00:43', '-', 'Wolkite Ketema', 'Adama City FC', '-', 2.24, 3.0, '-', '-'], ['2022-04-01T12:00:00Z', '2022/04/01 01:00:44', '-', 'Defence Force SC', 'Arba Minch', '-', '-', 2.86, 1.46, '-'], ['2022-04-01T22:00:00Z', '2022/04/01 01:00:44', '-', 'Nacional Potosi', 'Aurora', 1.53, 9.2, 4.7, '-', 1.67], ['2022-04-02T00:30:00Z', '2022/04/01 01:00:46', '-', 'Blooming Santa Cruz', 'Always Ready', 2.24, 3.25, 3.75, 2.34, '-'], ['2022-04-01T18:45:00Z', '2022/04/01 01:00:47', '-', 'Galway Utd', 'Cobh Ramblers', 1.61, 8.0, '-', 2.12, 1.75], ['2022-04-01T18:45:00Z', '2022/04/01 01:00:48', '-', 'Athlone Town', 'Bray Wanderers', '-', 1.82, '-', 2.54, 1.64], ['2022-04-01T23:00:00Z', '2022/04/01 01:00:49', '-', 'CA Rentistas', 'Penarol', '-', 1.55, '-', 1.95, '-'], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:50', '-', 'Middlesbrough U23', 'Newcastle U23', '-', '-', '-', '-', '-'], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:51', '-', 'Nottm Forest U23', 'Wolves U23', 2.2, 3.6, 4.3, 2.66, '-'], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:52', '-', 'Reading U23', 'West Brom U23', '-', 1.91, '-', '-', '-'], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:53', '-', 'Norwich U23', 'Sunderland U23', 1.74, '-', '-', 2.88, '-'], ['2022-04-01T13:00:00Z', '2022/04/01 01:00:54', '-', 'Hapoel Umm AL Fahm', 'Sektzia Nes Tziona', '-', 2.56, 3.4, '-', '-'], ['2022-04-01T13:00:00Z', '2022/04/01 01:00:55', '-', 'Hapoel Rishon Lezion', 'Ironi Ramat HaSharon', 2.38, '-', 2.9, '-', '-'], ['2022-04-01T18:00:00Z', '2022/04/01 01:00:56', '-', 'Aston Villa U23', 'Stoke U23', '-', 2.46, 3.55, '-', '-'], ['2022-04-01T14:30:00Z', '2022/04/01 01:00:57', '-', 'NK Sesvete', 'Kustosija', 1.89, 4.7, '-', 1.79, '-'], ['2022-04-01T18:15:00Z', '2022/04/01 01:00:58', '-', 'Swieqi United FC', 'Marsa FC', '-', 1.9, 4.4, 2.2, 1.75], ['2022-04-01T17:00:00Z', '2022/04/01 01:00:59', '-', 'Leixoes', 'Penafiel', 2.06, 4.2, 3.4, '-', 2.22], ['2022-04-01T18:45:00Z', '2022/04/01 01:01:00', '-', 'Cliftonville', 'Crusaders', 2.32, '-', '-', '-', 1.98], ['2022-04-01T23:30:00Z', '2022/04/01 01:01:01', '-', 'OHiggins', 'Coquimbo Unido', 2.16, 4.1, '-', 1.77, '-'], ['2022-04-01T15:00:00Z', '2022/04/01 01:01:02', '-', 'San Gwann', 'Pembroke Athleta', '-', 1.75, 3.8, '-', '-'], ['2022-04-01T20:30:00Z', '2022/04/01 01:01:03', '-', 'La Serena', 'Everton De Vina', 3.4, 2.54, 3.2, 1.66, 2.26], ['2022-04-01T18:00:00Z', '2022/04/01 01:01:04', '-', 'SC Telstar', 'Den Bosch', 1.88, '-', '-', '-', 1.68], ['2022-04-01T12:00:00Z', '2022/04/01 01:01:05', '-', 'NK Dubrava Zagreb', 'Cibalia Vinkovci', 1.88, 5.2, '-', 1.78, '-'], ['2022-04-01T16:00:00Z', '2022/04/01 01:01:06', '-', 'Zamalek', 'G.D. Sagrada Esperanca', '-', '-', '-', 1.94, '-'], ['2022-04-01T19:00:00Z', '2022/04/01 01:01:07', '-', 'SS Reyes', 'Racing Santander', 4.2, 2.28, '-', 1.57, '-'], ['2022-04-01T16:00:00Z', '2022/04/01 01:01:08', '-', 'FC Kosice', 'Samorin', 1.6, '-', '-', 2.34, '-'], ['2022-04-01T18:00:00Z', '2022/04/01 01:01:09', '-', 'Anderlecht (W)', 'Club Brugge (W)', 1.26, '-', '-', '-', '-'], ['2022-04-01T15:00:00Z', '2022/04/01 01:01:10', '-', 'Podbrezova', 'FK Puchov', 1.41, 8.6, 5.6, 2.38, 1.6], ['2022-04-01T21:15:00Z', '2022/04/01 01:01:11', '-', 'Zamora FC', 'Mineros Guayana', 1.99, '-', 3.5, 1.73, '-'], ['2022-04-01T21:45:00Z', '2022/04/01 01:01:12', '-', 'CF America (W)', 'FC Juarez (W)', '-', '-', '-', '-', '-'], ['2022-04-01T23:30:00Z', '2022/04/01 01:01:13', '-', 'Deportivo La Guaira', 'Estudiantes de Merida', 2.18, 4.4, 3.25, '-', '-'], ['2022-04-01T19:00:00Z', '2022/04/01 01:01:14', '-', 'CD Castellon', 'Alcoyano', 2.16, '-', 3.15, '-', '-'], ['2022-04-01T19:00:00Z', '2022/04/01 01:01:15', '-', 'Metropolitanos', 'Carabobo FC', 2.3, 4.1, 3.0, 1.68, 2.56], ['2022-04-01T23:00:00Z', '2022/04/01 01:01:16', '-', 'Queretaro (W)', 'Club Tijuana (W)', '-', '-', '-', '-', 1.65], ['2022-04-02T02:30:00Z', '2022/04/01 01:01:17', '-', 'Deportivo Saprissa', 'Municipal Grecia', 1.63, '-', '-', 1.97, 1.92], ['2022-04-01T17:00:00Z', '2022/04/01 01:01:18', '-', 'Roskilde', 'Vanlose IF', 1.39, 9.0, 6.0, 2.92, 1.53], ['2022-04-01T17:00:00Z', '2022/04/01 01:01:19', '-', 'Hellerup IK', 'Akademisk Boldklub', '-', '-', '-', '-', '-'], ['2022-04-01T22:30:00Z', '2022/04/01 01:01:20', '-', 'Real Cartagena', 'Llaneros FC', 2.26, 3.7, 3.3, 1.72, 2.38], ['2022-04-01T12:30:00Z', '2022/04/01 01:01:21', '-', 'NK Orijent U19', 'NK Istra 1961 U19', '-', '-', '-', 2.2, '-'], ['2022-04-01T16:00:00Z', '2022/04/01 01:01:22', '-', 'Stal Mielec', 'Cracovia Krakow', 3.65, 2.36, 3.4, 1.68, '-'], ['2022-04-01T13:00:00Z', '2022/04/01 01:01:23', '-', 'Etoile Sportive Sahel', 'Jwaneng Galaxy FC', 1.11, 19.0, '-', 2.34, 1.68], ['2022-04-01T13:30:00Z', '2022/04/01 01:01:24', '-', 'HNK Sibenik U19', 'NK Varazdin U19', '-', '-', '-', 2.7, '-'], ['2022-04-01T16:00:00Z', '2022/04/01 01:01:25', '-', 'Horoya AC', 'AmaZulu', '-', '-', '-', 1.66, '-'], ['2022-04-01T18:30:00Z', '2022/04/01 01:01:26', '-', 'Slask Wroclaw', 'Lech Poznan', 6.2, 1.66, 3.85, 1.79, '-'], ['2022-04-01T16:30:00Z', '2022/04/01 01:01:27', '-', 'Riteriai', 'FK Jonava', '-', 36.0, 8.6, 2.4, 1.63], ['2022-04-01T11:45:00Z', '2022/04/01 01:01:28', '-', 'Hougang Utd', 'Albirex Niigata (SIN)', 8.2, 1.4, 7.6, 3.35, 1.36], ['2022-04-01T19:30:00Z', '2022/04/01 01:01:29', '-', 'Gimnasia La Plata', 'Talleres', 2.68, 3.0, 3.1, '-', 2.26], ['2022-04-01T17:00:00Z', '2022/04/01 01:01:30', '-', 'Babelsberg', 'Union Furstenwalde', 1.44, '-', '-', 2.44, '-'], ['2022-04-01T17:00:00Z', '2022/04/01 01:01:31', '-', 'VfB Homberg', 'Schalke 04 II', 4.9, 1.82, 3.9, 2.02, 1.88], ['2022-04-01T17:00:00Z', '2022/04/01 01:01:32', '-', 'Hertha Berlin II', 'VfB Auerbach', 1.49, '-', '-', 2.66, 1.58], ['2022-04-01T16:00:00Z', '2022/04/01 01:01:33', '-', 'FK Babrungas', 'FK Banga II', 1.75, '-', '-', 3.15, 1.47], ['2022-04-01T11:45:00Z', '2022/04/01 01:01:34', '-', 'Home Utd', 'Balestier Khalsa', 1.18, 23.0, 9.8, 2.2, 1.26], ['2022-04-01T10:00:00Z', '2022/04/01 01:01:35', '-', 'Fukuoka', 'Tosu', 2.62, 3.4, 3.0, 1.52, 2.92], ['2022-04-01T17:30:00Z', '2022/04/01 01:01:37', '-', 'Havnar Boltfelag II', 'Klaksvikar Itrottarfelag II', '-', 2.02, '-', '-', '-'], ['2022-04-01T19:15:00Z', '2022/04/01 01:01:38', '-', 'Stjarnan (W)', 'Breidablik (W)', '-', 1.92, '-', '-', '-'], ['2022-04-01T17:30:00Z', '2022/04/01 01:01:38', '-', 'Barmbek-Uhlenhorst', 'FC Suderelbe', '-', 1.95, '-', '-', 1.34], ['2022-04-01T22:00:00Z', '2022/04/01 01:01:39', '-', 'Central Cordoba (SdE)', 'Huracan', '-', '-', 3.15, 1.62, '-'], ['2022-04-02T00:30:00Z', '2022/04/01 01:01:41', '-', 'Banfield', 'Argentinos Juniors', 2.56, '-', '-', '-', '-'], ['2022-04-01T16:30:00Z', '2022/04/01 01:01:42', '-', 'Ingolstadt', 'Erzgebirge', 2.52, 2.94, 3.6, 2.1, 1.87], ['2022-04-02T00:00:00Z', '2022/04/01 01:01:43', '-', 'LD Alajuelense', 'Santos de Guapiles', 1.42, '-', '-', 2.2, '-'], ['2022-04-01T20:00:00Z', '2022/04/01 01:01:43', '-', 'CF Union Viera', 'Las Palmas C', 4.3, 2.48, 3.35, 1.78, '-'], ['2022-04-01T20:30:00Z', '2022/04/01 01:01:44', '-', 'Ayacucho Futbol Club', 'UTC Cajamarca', 2.08, 3.9, 3.8, 1.99, '-'], ['2022-04-01T03:01:19Z', '2022/04/01 01:01:45', '-', 'Santos Laguna (W)', 'Puebla FC (W)', 2.88, 4.5, 2.26, 1.12, 8.8], ['2022-04-01T18:00:00Z', '2022/04/01 01:01:46', '-', 'CD Pamplona', 'CD Huarte', 2.94, 2.64, 3.6, '-', '-'], ['2022-04-01T03:01:51Z', '2022/04/01 01:01:47', '-', 'Club Leon FC (W)', 'Cruz Azul (W)', 4.2, 3.15, 2.3, 3.2, 1.42], ['2022-04-01T02:33:13Z', '2022/04/01 01:01:48', '-', 'CS Cartagines', 'ADR Jicaral', 2.1, 11.5, 2.24, 1.07, 17.0], ['2022-04-01T18:00:00Z', '2022/04/01 01:01:49', '-', 'Birmingham (W)', 'Everton (W)', 5.3, 1.7, 3.95, '-', '-'], ['2022-04-01T17:00:00Z', '2022/04/01 01:01:50', '-', 'Illertissen', 'Unterhaching', 2.56, 2.84, 3.45, '-', '-'], ['2022-04-01T21:00:00Z', '2022/04/01 01:01:51', '-', 'AD Guanacasteca', 'Guadalupe F.C', '-', '-', '-', '-', '-'], ['2022-04-01T17:00:00Z', '2022/04/01 01:01:52', '-', 'SC Eltersdorf', 'TSV Rain/Lech', 2.32, 3.25, 4.0, 2.7, '-'], ['2022-04-01T17:30:00Z', '2022/04/01 01:01:53', '-', 'FC Memmingen', 'Viktoria Aschaffenburg', 2.84, 2.6, '-', 2.24, '-'], ['2022-04-01T18:00:00Z', '2022/04/01 01:01:54', '-', 'CD Gares', 'UDC Txantrea', '-', 1.46, '-', 2.42, '-'], ['2022-04-01T15:00:00Z', '2022/04/01 01:01:55', '-', 'Zilina', 'Ruzomberok', 2.48, 3.3, 3.75, 2.08, 1.83], ['2022-04-01T16:30:00Z', '2022/04/01 01:01:56', '-', 'Dynamo Dresden', 'Schalke 04', 4.9, 1.86, 3.85, 2.06, 1.82]]

    para_planilha.sort()

I've seen some people indicating something like:

para_planilha = para_planilha.astype('str')
para_planilha.sort()

But this error appears:

AttributeError: 'list' object has no attribute 'astype'

How should I proceed to solve this problem and get sort this list?

ANSWER

Answered 2022-Apr-01 at 04:28

In base Python, we can try sorting using a lambda expression:

para_planilha.sort(key=lambda row: row[0])

But note that you have some 1D lists in there whose first element is not a datetime string, such as [2.36, '-'], which appears first in the sort.

Source https://stackoverflow.com/questions/71701739

QUESTION

My function isnt taking a string when its suppossed to

Asked 2022-Mar-29 at 01:57

So I'm trying to have my Actor function work. However when I input a string into the parameter I get these errors

Line 100: error: cannot convert ‘Vector<std::__cxx11::basic_string<char> >’ to ‘std::__cxx11::basic_string<char>’

Line 100: Actors.push_back((split(actorData, "\t"))); Line 118: error: no match for call to ‘(Vector<std::__cxx11::basic_string<char> >) (std::__cxx11::basic_string<char>&)’

Line 118: Actors = Actor(ActorData[3]);

vector.h library

Vector.h is practically the same as the standard vector class

txtfile

#include <iostream>
#include <string>
#include <fstream>
#include "vector.h" //You can also use <vector>

using namespace std;



Vector<string> split (string s, string delimiter)
{
  size_t pos_start = 0, pos_end, delim_len = delimiter.length();
  string token;
  Vector<string> res;

  while ((pos_end = s.find (delimiter, pos_start)) != string::npos)
  {
    token = s.substr (pos_start, pos_end - pos_start);
    pos_start = pos_end + delim_len;
    res.push_back (token);
  }

  res.push_back (s.substr (pos_start));
  return res;
}
/*
  movie Data
    - returns data from the txt file and stores them in a vector
  Returns:
    - a vector with movie names and actors
*/
Vector<string> movieData(string &filename)
{
  fstream myFile;
  Vector<string> data;
  string content; //All the content casts and movie title
  myFile.open(filename);
  if(!myFile.good()) //If the file failed
  {
    cout << "File Not Found" << endl;
    exit(1);
  }
  //---------------------------------------
  while(getline(myFile, content, '\n')) //seperates each movie by the line
  {
    data.push_back(content);
  }
  //---------------------------------------
  myFile.close();
  return data;
}
/*
  movie Title
    - a Vector with Movie Titles
  Returns:
    - a Vector only including the movie titles from each
      distinct line
*/
Vector<string> movieTitle(Vector<string> &data)
{
  Vector<string> Titles;
  for(int i = 0; i<data.size(); i++)
  {
    int pos = data[i].find("\t");
    Titles.push_back(data[i].substr(0, pos));
  }
  return Titles;
}

/*
  actor Data
    - a Vector with the Actors that are unsliced
      ex: Oscar Issac\tEthan Hawke
  Returns:
    - a Vector only including the actors and not the title
      from each distinct line
*/
Vector<string> actorData(Vector<string> &data)
{
  Vector<string> actorData;
  string word;
  for(int i = 0; i<data.size(); i++)
  {
    int pos = data[i].find(')');
    data[i].erase(0, pos+2);
    actorData.push_back(data[i].substr(0, data[i].length()-1));
  }
  return actorData;
}
/*
  Actor
    - a Vector with the Actors that are sliced
  Returns:
    - a Vector only including the actors and not the title
      from each distinct line
*/
Vector<string> Actor(string &actorData)
{
  Vector<string> Actors;
  Actors.push_back((split(actorData, "\t"))); //Trying to remove tab from the
  return Actors;                               //previous vector
}

int main()
{
  Vector<string> data; //contains titles and cast seperated by \t
                      //ex: Moon Knight (2022)\tOscar Issac\tEthan Hawke
  Vector<string> Title; //Contains Just the Title
                        //ex: Moon Knight (2022)
  Vector<string> ActorData; //Contais the cast that are unscliced
                            //ex: Oscar Issac\tEthan Hawke
  Vector<string> Actor; //contains the actors that are sliced
                        //ex: "Oscar Issac", "Ethan Hawke"
  string filename = "movies_mpaa.txt";
  data = movieData(filename);
  Title = movieTitle(data);
  ActorData = actorData(data);
  Actors = Actor(ActorData[3]);
  //-------------------------------//
  cout << "The Cast Of: "<< Title[3] << " is:" << endl;
  cout << "Actor: " << Actors << endl;
  //cout << "//--------------------//" << endl;
  //cout << "Data: " << data[2] << endl;


  return 0;
}

I used the debugger and something is wrong within my Actor function, but how come? Everything should be fine since it logically makes sense.

Actor accepts a string
ActorData is a vector with strings inside
Actor(ActorData[3]) should work
The split function is also good because it just splices strings

ANSWER

Answered 2022-Mar-29 at 01:19

You do this

Vector<string> Actor(string &actorData)
{
  Vector<string> Actors;
  Actors.push_back((split(actorData, "\t"))); //Trying to remove tab from the
  return Actors;                               //previous vector
}

splittig the code up a bit gives

Vector<string> Actor(string &actorData)
{
  Vector<string> Actors;
  Vector<string> splitStrs = split(actorData, "\t");
  Actors.push_back(splitStrs); //Trying to remove tab from the
  return Actors;                               //previous vector
}

You cannot push a vector of strings into a vector of strings. I suspect you want the first one (your comment says you are trying to remove a tab)

So you need

Actors.push_back(splitStrs[0]);

or maybe you mean to add them all

Actors.insert(Actors.begin(), splitStrs.begin(), stritStes.end());

Source https://stackoverflow.com/questions/71655317

QUESTION

Retrieve value from IndexedDB using Dexie and Svelte

Asked 2022-Mar-22 at 07:53

I don't understand how I can get a value from IndexedDB using Dexie. Database is all good in 'application' tab in inspect tool. Total newbie, so please be understanding.

My db.js

import Dexie from "dexie";

export const db = new Dexie("myDatabase");
db.version(2).stores({
  history: "++id, daterange, days",
  storage: "id, name, value"
});

db.on("populate", function () {
  db.storage.add({
    id: 0,
    name: "total",
    value: 20
  });
  db.storage.add({
    id: 1,
    name: "left",
    value: 20
  });
});
db.open();

App.svelte

<script>
  import Counter from "./src/Counter.svelte";
  import New from "./src/New.svelte";
  import History from "./src/History.svelte";
  import { liveQuery } from "dexie";
  import { db } from "./src/db";

  let total = liveQuery(() =>
    db.storage
      .where("name")
      .equals("total")
      .value.then(function(a) {
        totals = a;
      })
  );

  let left = 25;
</script>

<style>
  main {
    width: 100%;
  }
</style>

<main>
    <Counter daysLeft={left} daysMax={total}/>
    <New />
    <History />
</main>

Whatever I try, object with daysMax={total} outputs undefined, [object Object] or something like [Dexie object Object]. I just want to get 20 from db, as seen in db.js:

db.on("populate", function () {
  db.storage.add({
    id: 0,
    name: "total",
    value: 20
  });

(This all works and is visible in indexedDb) I also tried daysMax={$total}

CodeSandbox

ANSWER

Answered 2022-Mar-22 at 07:53

As you stated, your DB setup and initial write operations are done correctly. The issue is with your query:

  let total = liveQuery(() =>
    db.storage
      .where("name")
      .equals("total")
      .value.then(function(a) {
        totals = a;
      })
  );

First of all, the WhereClause part of your query (db.storage.where("name").equals("total")) returns a Collection, which, to quote the documentation:

Represents a collection of database objects. Note that it will not contain any objects by itself. Instead, it yields a preparation for how to execute a DB query. A query will be executed when calling methods that returns a Promise, such as toArray(), keys(), count() or each().

So you cannot simply dereference .value as you do in your code. You must instead use one of the methods provided by Collection. Since in your use case you only ever expect a single match to be found in the database, I would recommend Collection.first() here, which returns a Promise that eventually resolves to the data you're querying.

So your query should instead look like this:

  let total = liveQuery(() =>
    db.storage
      .where("name")
      .equals("total")
      .first()
  );

Next is the way you use liveQuery. What liveQuery() does is it turns a Promise (which you will now receive from the previous step) into an Observable. As stated in the documentation:

The Svelte Store Contract is a subset of the Ecmascript Observable specification draft which makes the return value of liveQuery() a fully valid Svelte Store by itself.

Meaning here that total will in fact behave like a Svelte (readable) store, that can be subscribed to, or accessed with the shorthand $ syntax. This means that $total will hold - once the query completes and the promise resolves - the database object that matches your query.

And because it is an Observable (and thus a Svelte store), any updates made to the database record will be immediately reflected in the value of $total.

The last thing to be done is to access the value property of the database object. ~~Because $total will initially be undefined while the query runs and the promise resolves, I would also add a conditional.~~ For this I would recommend using a small reactive declaration to keep things tidy, picking a variable name that will allow you to use shorthand prop notation, combined with a conditional to only display the counter when the query has resolved:

<script>
    // ...your existing script code with updated query...
    $: daysMax = $total?.value // undefined until the query resolves, actual value once the query has resolved
</script>

<main>
    {#if daysMax !== undefined}
        <Counter daysLeft={left} {daysMax} />
    {/if}
    <New />
    <History />
</main>

Forked, working CodeSandbox

Source https://stackoverflow.com/questions/71557580

QUESTION

Decrypt message with random shift of letters

Asked 2022-Mar-11 at 04:15

I am writing a program to decrypt a message and only given assumption that the maximum occur letter of decrypted message is "e". No shift number is given. Below code are my workdone. I can only hardcode the shift number to decrypt the given message, but if the message changed my code of course didn't work.

from collections import Counter
import string

message = "Zyp cpxpxmpc ez wzzv fa le esp delcd lyo yze ozhy le jzfc qppe Ehz ypgpc rtgp fa hzcv Hzcv rtgpd jzf xplytyr lyo afcazdp lyo wtqp td pxaej hteszfe te Escpp tq jzf lcp wfnvj pyzfrs ez qtyo wzgp cpxpxmpc te td espcp lyo ozye esczh te lhlj Depaspy Slhvtyr" 

#frequency of each letter
letter_counts = Counter(message)
print(letter_counts)    # Print the count of each element in string

#find max letter
maxFreq = -1
maxLetter = None
letter_counts[' '] = 0  # Don't count spaces zero count
for letter, freq in letter_counts.items(): 
    print(letter, ":", freq) 
    maxLetter = max(letter_counts, key = letter_counts.get)  # Find max freq letter in the string 
print("Max Ocurring Letter:", maxLetter)


#right shift for encrypting and left shift for descripting.
#predict shift
#assume max letter is 'e'
letters = string.ascii_letters #contains 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
shift = 15  #COMPUTE SHIFT HERE (hardcode)
print("Predicted Shift:", shift)

totalLetters = 26
keys = {} #use dictionary for letter mapping
invkeys = {} #use dictionary for inverse letter mapping, you could use inverse search from original dict

for index, letter in enumerate(letters):
    # cypher setup
    if index < totalLetters: #lowercase
        # Dictionary for encryption 
        letter = letters[index]
        keys[letter] = letters[(index + shift) % 26]
        # Dictionary for decryption 
        invkeys = {val: key for key, val in keys.items()}
    else: #uppercase
        # Dictionary for encryption 
        keys[letter] = letters[(index + shift) % 26 + 26]
        # Dictionary for decryption
        invkeys = {val: key for key, val in keys.items()}
print("Cypher Dict", keys)

#decrypt
decryptedMessage = []
for letter in message:
    if letter == ' ': #spaces
        decryptedMessage.append(letter)
    else:
        decryptedMessage.append(keys[letter])
print("Decrypted Message:", ''.join(decryptedMessage)) #join is used to put list inot string

# Checking if message is the same as the encrypt message provided 
#Encrypt
encryptedMessage = []
for letter in decryptedMessage:
    if letter == ' ': #spaces
        encryptedMessage.append(letter)
    else:
        encryptedMessage.append(invkeys[letter])
print("Encrypted Message:", ''.join(encryptedMessage)) #join is used to put list inot string

The encrypt part of code is not necessary to exist, it is for checking only. It would be great if someone could help to modify my code/ give me some hints for the predict shift part. Thanks!

Output of the code:

Cypher Dict {'a': 'p', 'b': 'q', 'c': 'r', 'd': 's', 'e': 't', 'f': 'u', 'g': 'v', 'h': 'w', 'i': 'x', 'j': 'y', 'k': 'z', 'l': 'a', 'm': 'b', 'n': 'c', 'o': 'd', 'p': 'e', 'q': 'f', 'r': 'g', 's': 'h', 't': 'i', 'u': 'j', 'v': 'k', 'w': 'l', 'x': 'm', 'y': 'n', 'z': 'o', 'A': 'P', 'B': 'Q', 'C': 'R', 'D': 'S', 'E': 'T', 'F': 'U', 'G': 'V', 'H': 'W', 'I': 'X', 'J': 'Y', 'K': 'Z', 'L': 'A', 'M': 'B', 'N': 'C', 'O': 'D', 'P': 'E', 'Q': 'F', 'R': 'G', 'S': 'H', 'T': 'I', 'U': 'J', 'V': 'K', 'W': 'L', 'X': 'M', 'Y': 'N', 'Z': 'O'}
Decrypted Message: One remember to look up at the stars and not down at your feet Two never give up work Work gives you meaning and purpose and life is empty without it Three if you are lucky enough to find love remember it is there and dont throw it away Stephen Hawking
Encrypted Message: Zyp cpxpxmpc ez wzzv fa le esp delcd lyo yze ozhy le jzfc qppe Ehz ypgpc rtgp fa hzcv Hzcv rtgpd jzf xplytyr lyo afcazdp lyo wtqp td pxaej hteszfe te Escpp tq jzf lcp wfnvj pyzfrs ez qtyo wzgp cpxpxmpc te td espcp lyo ozye esczh te lhlj Depaspy Slhvtyr

ANSWER

Answered 2022-Mar-11 at 04:15

Something like this should allow you to calculate the shift based on the assumption that the letter in the original message with the highest frequency is 'e':

letter_counts = Counter(message)
e_encrypted = [k for k, v in letter_counts.items() if v == max(count for c, count in letter_counts.items() if c != ' ')][0]
shift = (ord('e') - ord(e_encrypted)) % 26

Or, to unroll the comprehensions for ease of understanding:

letter_counts = Counter(message)
e_encrypted, max_v = None, 0
for k, v in letter_counts.items():
    if v > max_v and k != ' ':
        e_encrypted, max_v = k, v
shift = (ord('e') - ord(e_encrypted)) % 26

It does the following:

take frequency counts of characters in message using the Counter class
find the maximum frequency, and the character with that maximum frequency
set the shift equal to the difference between the ascii value of that character and the letter 'e' (modulo 26)

Source https://stackoverflow.com/questions/71433330

QUESTION

Android build failed. showing "Resource compilation failed. Check logs for details."

Asked 2022-Feb-28 at 05:46

<string-array name="sports_detail">
        <item>Baseball is a bat-and-ball game played between two opposing teams, of nine players each, that take turns batting and fielding. The game proceeds when a player on the fielding team, called the pitcher, throws a ball which a player on the batting team tries to hit with a bat. The objective of the offensive team (batting team) is to hit the ball into the field of play, allowing its players to run the bases, having them advance counter-clockwise around four bases to score what are called "runs". The objective of the defensive team (fielding team) is to prevent batters from becoming runners, and to prevent runners' advance around the bases.[2] A run is scored when a runner legally advances around the bases in order and touches home plate (the place where the player started as a batter). The team that scores the most runs by the end of the game is the winner.The first objective of the batting team is to have a player reach first base safely. A player on the batting team who reaches first base without being called "out" can attempt to advance to subsequent bases as a runner, either immediately or during teammates' turns batting. The fielding team tries to prevent runs by getting batters or runners "out", which forces them out of the field of play. Both the pitcher and fielders have methods of getting the batting team's players out. The opposing teams switch back and forth between batting and fielding; the batting team's turn to bat is over once the fielding team records three outs. One turn batting for each team constitutes an inning. A game is usually composed of nine innings, and the team with the greater number of runs at the end of the game wins. If scores are tied at the end of nine innings, extra innings are usually played. Baseball has no game clock, although most games end in the ninth inning.Baseball evolved from older bat-and-ball games already being played in England by the mid-18th century. This game was brought by immigrants to North America, where the modern version developed. By the late 19th century, baseball was widely recognized as the national sport of the United States. Baseball is popular in North America and parts of Central and South America, the Caribbean, and East Asia, particularly in Japan, South Korea, and Taiwan.</item>
        <item>Badminton is a racquet sport played using racquets to hit a shuttlecock across a net. Although it may be played with larger teams, the most common forms of the game are "singles" (with one player per side) and "doubles" (with two players per side). Badminton is often played as a casual outdoor activity in a yard or on a beach; formal games are played on a rectangular indoor court. Points are scored by striking the shuttlecock with the racquet and landing it within the opposing side's half of the court.Each side may only strike the shuttlecock once before it passes over the net. Play ends once the shuttlecock has struck the floor or if a fault has been called by the umpire, service judge, or (in their absence) the opposing side.[1]The shuttlecock is a feathered or (in informal matches) plastic projectile which flies differently from the balls used in many other sports. In particular, the feathers create much higher drag, causing the shuttlecock to decelerate more rapidly. Shuttlecocks also have a high top speed compared to the balls in other racquet sports. The flight of the shuttlecock gives the sport its distinctive nature.The game developed in British India from the earlier game of battledore and shuttlecock. European play came to be dominated by Denmark but the game has become very popular in Asia, with recent competitions dominated by China. Since 1992, badminton has been a Summer Olympic sport with four events: men's singles, women's singles, men's doubles, and women's doubles,[2] with mixed doubles added four years later. At high levels of play, the sport demands excellent fitness: players require aerobic stamina, agility, strength, speed, and precision. It is also a technical sport, requiring good motor coordination and the development of sophisticated racquet movements.[3</item>
        <item>Basketball is a team sport in which two teams, most commonly of five players each, opposing one another on a rectangular court, compete with the primary objective of shooting a basketball (approximately 9.4 inches (24 cm) in diameter) through the defender's hoop (a basket 18 inches (46 cm) in diameter mounted 10 feet (3.048 m) high to a backboard at each end of the court), while preventing the opposing team from shooting through their own hoop. A field goal is worth two points, unless made from behind the three-point line, when it is worth three. After a foul, timed play stops and the player fouled or designated to shoot a technical foul is given one, two or three one-point free throws. The team with the most points at the end of the game wins, but if regulation play expires with the score tied, an additional period of play (overtime) is mandated.Players advance the ball by bouncing it while walking or running (dribbling) or by passing it to a teammate, both of which require considerable skill. On offense, players may use a variety of shots – the layup, the jump shot, or a dunk; on defense, they may steal the ball from a dribbler, intercept passes, or block shots; either offense or defense may collect a rebound, that is, a missed shot that bounces from rim or backboard. It is a violation to lift or drag one's pivot foot without dribbling the ball, to carry it, or to hold the ball with both hands then resume dribbling.The five players on each side fall into five playing positions. The tallest player is usually the center, the second-tallest and strongest is the power forward, a slightly shorter but more agile player is the small forward, and the shortest players or the best ball handlers are the shooting guard and the point guard, who implements the coach's game plan by managing the execution of offensive and defensive plays (player positioning). Informally, players may play three-on-three, two-on-two, and one-on-one</item>
        <item>Bowling is a target sport and recreational activity in which a player rolls a ball toward pins (in pin bowling) or another target (in target bowling). The term bowling usually refers to pin bowling (most commonly ten-pin bowling), though in the United Kingdom and Commonwealth countries, bowling could also refer to target bowling, such as lawn bowls.In pin bowling, the goal is to knock over pins on a long playing surface known as a lane. Lanes have a wood or synthetic surface onto which protective lubricating oil is applied in different specified oil patterns that affect ball motion. A strike is achieved when all the pins are knocked down on the first roll, and a spare is achieved if all the pins are knocked over on a second roll. Common types of pin bowling include ten-pin, candlepin, duckpin, nine-pin, five-pin and kegel. The historical game skittles is the forerunner of modern pin bowling.In target bowling, the aim is usually to get the ball as close to a mark as possible. The surface in target bowling may be grass, gravel, or synthetic.[1] Lawn bowls, bocce, carpet bowls, pétanque, and boules may have both indoor and outdoor varieties. Curling is also related to bowls.Bowling is played by 120 million people in more than 90 countries (including 70 million in the United States alone),[2] and is the subject of video games.</item>
        <item>Cycling, also called bicycling or biking, is the use of bicycles for transport, recreation, exercise or sport.[1] People engaged in cycling are referred to as "cyclists",[2] "bicyclists",[3] or "bikers".[4] Apart from two-wheeled bicycles, "cycling" also includes the riding of unicycles, tricycles, quadricycles, recumbent and similar human-powered vehicles (HPVs).Bicycles were introduced in the 19th century and now number approximately one billion worldwide.[5] They are the principal means of transportation in many parts of the world, especially in densely populated European cities.[6]Cycling is widely regarded as an effective and efficient mode of transportation[7][8] optimal for short to moderate distances.Bicycles provide numerous possible benefits in comparison with motor vehicles, including the sustained physical exercise involved in cycling, easier parking, increased maneuverability, and access to roads, bike paths and rural trails. Cycling also offers a reduced consumption of fossil fuels, less air or noise pollution, reduced greenhouse gas emissions,[9] and greatly reduced traffic congestion.[10] These have a lower financial cost for users as well as for society at large (negligible damage to roads, less road area required). By fitting bicycle racks on the front of buses, transit agencies can significantly increase the areas they can serve.[11]In addition, cycling provides a variety of health benefits.[12] The World Health Organization (WHO) states that cycling can reduce the risk of cancers, heart disease, and diabetes that are prevalent in sedentary lifestyles.[13][10] Cycling on stationary bikes have also been used as part of rehabilitation for lower limb injuries, particularly after hip surgery.[14] Individuals who cycle regularly have also reported mental health improvements, including less perceived stress and better vitality.[15]</item>
        <item>Golf is a club-and-ball sport in which players use various clubs to hit balls into a series of holes on a course in as few strokes as possible.Golf, unlike most ball games, cannot and does not utilize a standardized playing area, and coping with the varied terrains encountered on different courses is a key part of the game. The game at the usual level is played on a course with an arranged progression of 18 holes, though recreational courses can be smaller, often having nine holes. Each hole on the course must contain a teeing ground to start from, and a putting green containing the actual hole or cup 4+1⁄4 inches (11 cm) in diameter. There are other standard forms of terrain in between, such as the fairway, rough (long grass), bunkers (or "sand traps"), and various hazards (water, rocks) but each hole on a course is unique in its specific layout and arrangement.Golf is played for the lowest number of strokes by an individual, known as stroke play, or the lowest score on the most individual holes in a complete round by an individual or team, known as match play. Stroke play is the most commonly seen format at all levels, but most especially at the elite level.The modern game of golf originated in 15th century Scotland. The 18-hole round was created at the Old Course at St Andrews in 1764. Golf's first major, and the world's oldest tournament in existence, is The Open Championship, also known as the British Open, which was first played in 1860 at the Prestwick Golf Club in Ayrshire, Scotland. This is one of the four major championships in men's professional golf, the other three being played in the United States: The Masters, the U.S. Open, and the PGA Championship</item>
        <item>Running is a method of terrestrial locomotion allowing humans and other animals to move rapidly on foot. Running is a type of gait characterized by an aerial phase in which all feet are above the ground (though there are exceptions).[1] This is in contrast to walking, where one foot is always in contact with the ground, the legs are kept mostly straight and the center of gravity vaults over the stance leg or legs in an inverted pendulum fashion.[2] A feature of a running body from the viewpoint of spring-mass mechanics is that changes in kinetic and potential energy within a stride occur simultaneously, with energy storage accomplished by springy tendons and passive muscle elasticity.[3] The term running can refer to any of a variety of speeds ranging from jogging to sprinting.Running in humans is associated with improved health and life expectancy.[4]It is assumed that the ancestors of humankind developed the ability to run for long distances about 2.6 million years ago, probably in order to hunt animals.[5] Competitive running grew out of religious festivals in various areas. Records of competitive racing date back to the Tailteann Games in Ireland between 632 BCE and 1171 BCE,[6][7][8] while the first recorded Olympic Games took place in 776 BCE. Running has been described as the world's most accessible sport.[9]</item>
        <item>"Soccer team" and "Soccer" redirect here. For the band, see Soccer Team (band). For other uses, see Soccer (disambiguation).This article is about the sport of association football. For other codes of football, see Football.Association football, more commonly known as simply football or soccer,[a] is a team sport played with a spherical ball between two teams of 11 players. It is played by approximately 250 million players in over 200 countries and dependencies, making it the world's most popular sport. The game is played on a rectangular field called a pitch with a goal at each end. The object of the game is to score more goals than the opposition by moving the ball beyond the goal line into the opposing goal, usually within a time frame of 90 or more minutes.Football is played in accordance with a set of rules known as the Laws of the Game. The ball is 68–70 cm (27–28 in) in circumference and known as the football. The two teams compete to get the ball into the other team's goal (between the posts and under the bar), thereby scoring a goal. Players are not allowed to touch the ball with hands or arms while it is in play, except for the goalkeepers within the penalty area. Players may use any other part of their body to strike or pass the ball and mainly use their feet. The team that scores more goals at the end of the game is the winner; if both teams have scored an equal number of goals, either a draw is declared or the game goes into extra time or a penalty shootout, depending on the format of the competition. Each team is led by a captain who has only one official responsibility as mandated by the Laws of the Game: to represent their team in the coin toss before kick-off or penalty kicks.[4]</item>
        <item>Swimming is the self-propulsion of a person through water, or other liquid, usually for recreation, sport, exercise, or survival. Locomotion is achieved through coordinated movement of the limbs and the body to achieve hydrodynamic thrust which results in directional motion. Humans can hold their breath underwater and undertake rudimentary locomotive swimming within weeks of birth, as a survival response.[1]Swimming is consistently among the top public recreational activities,[2][3][4][5] and in some countries, swimming lessons are a compulsory part of the educational curriculum.[6] As a formalized sport, swimming features in a range of local, national, and international competitions, including every modern Summer Olympics.Swimming relies on the nearly neutral buoyancy of the human body. On average, the body has a relative density of 0.98 compared to water, which causes the body to float. However, buoyancy varies on the basis of body composition, lung inflation, muscle and fat content, centre of gravity and the salinity of the water. Higher levels of body fat and saltier water both lower the relative density of the body and increase its buoyancy. Human males tend to have a lower centre of gravity and higher muscle content, therefore find it more difficult to float or be buoyant. See also: Hydrostatic weighing.Since the human body is less dense than water, water is able to support the weight of the body during swimming. As a result, swimming is “low-impact” compared to land activities such as running. The density and viscosity of water also create resistance for objects moving through the water. Swimming strokes use this resistance to create propulsion, but this same resistance also generates drag on the body.</item>
        <item>Table tennis, also known as ping-pong and whiff-whaff, is a sport in which two or four players hit a lightweight ball, also known as the ping-pong ball, back and forth across a table using small solid rackets. The game takes place on a hard table divided by a net. Except for the initial serve, the rules are generally as follows: players must allow a ball played toward them to bounce once on their side of the table and must return it so that it bounces on the opposite side at least once. A point is scored when a player fails to return the ball within the rules. Play is fast and demands quick reactions. Spinning the ball alters its trajectory and limits an opponent's options, giving the hitter a great advantage.Table tennis is governed by the worldwide organization International Table Tennis Federation (ITTF), founded in 1926. ITTF currently includes 226 member associations.[3] The table tennis official rules are specified in the ITTF handbook.[4] Table tennis has been an Olympic sport since 1988,[5] with several event categories. From 1988 until 2004, these were men's singles, women's singles, men's doubles and women's doubles. Since 2008, a team event has been played instead of the doubles.The sport originated in Victorian England, where it was played among the upper-class as an after-dinner parlour game.[1][2] It has been suggested that makeshift versions of the game were developed by British military officers in India around the 1860s or 1870s, who brought it back with them.[6] A row of books stood up along the center of the table as a net, two more books served as rackets and were used to continuously hit a golf-ball.[7][8]The name "ping-pong" was in wide use before British manufacturer J and Son Ltd trademarked it in 1901. The name "ping-pong" then came to describe the game played using the rather expensive  equipment, with other manufacturers calling it table tennis. A similar situation arose in the United States, where  sold the rights to the "ping-pong" name to Parker Brothers. Parker Brothers then enforced its trademark for the term in the 1920s, making the various associations change their names to "table tennis" instead of the more common, but trademarked, term.[9]</item>
        <item>Tennis is a racket sport that can be played individually against a single opponent (singles) or between two teams of two players each (doubles). Each player uses a tennis racket that is strung with cord to strike a hollow rubber ball covered with felt over or around a net and into the opponent's court. The object of the game is to manoeuvre the ball in such a way that the opponent is not able to play a valid return. The player who is unable to return the ball validly will not gain a point, while the opposite player will.[1][2]Tennis is an Olympic sport and is played at all levels of society and at all ages. The sport can be played by anyone who can hold a racket, including wheelchair users. The modern game of tennis originated in Birmingham, England, in the late 19th century as lawn tennis.[3] It had close connections both to various field (lawn) games such as croquet and bowls as well as to the older racket sport today called real tennis.[4]The rules of modern tennis have changed little since the 1890s. Two exceptions are that until 1961 the server had to keep one foot on the ground at all times,[5][6] and the adoption of the tiebreak in the 1970s.[7] A recent addition to professional tennis has been the adoption of electronic review technology coupled with a point-challenge system, which allows a player to contest the line call of a point, a system known as Hawk-Eye.[8][9]Tennis is played by millions of recreational players and is also a popular worldwide spectator sport.[10] The four Grand Slam tournaments (also referred to as the Majors) are especially popular: the Australian Open played on hard courts, the French Open played on red clay courts, Wimbledon played on grass courts, and the US Open also played on hard courts.[11]</item>
    </string-array>

Everythink is working fine before adding this string array in strings.xml. You will be thinking that how did I know that error comes after adding this. So I completed , what I was doing and then ran the app and that showed this error.

Execution failed for task ':app:mergeDebugResources'. A failure occurred while executing com.android.build.gradle.internal.res.ResourceCompilerRunnable

So I switched to previous version of the app that was running fine and add this string-array to the strings.xml and got this error again.

if Run with stack trace then this would shown up

sorry I am pasting the whole

Caused by: org.gradle.workers.internal.DefaultWorkerExecutor$WorkExecutionException: A failure occurred while executing com.android.build.gradle.internal.res.ResourceCompilerRunnable

Caused by: com.android.aaptcompiler.ResourceCompilationException: Resource compilation failed. Check logs for details.
    at com.android.aaptcompiler.ResourceCompiler.compileResource(ResourceCompiler.kt:121)
    at com.android.build.gradle.internal.res.ResourceCompilerRunnable$Companion.compileSingleResource(ResourceCompilerRunnable.kt:32)
    at com.android.build.gradle.internal.res.ResourceCompilerRunnable.run(ResourceCompilerRunnable.kt:15)
    at com.android.build.gradle.internal.profile.ProfileAwareWorkAction.execute
Caused by: java.lang.NullPointerException
    at com.android.aaptcompiler.TableExtractor.flattenXmlSubTree(TableExtractor.kt:850)
    at com.android.aaptcompiler.TableExtractor.parseXml(TableExtractor.kt:515)
    at com.android.aaptcompiler.TableExtractor.parseArrayImpl(TableExtractor.kt:1275)
    at com.android.aaptcompiler.TableExtractor.parseStringArray(TableExtractor.kt:1218)
    at com.android.aaptcompiler.TableExtractor.access$parseStringArray(TableExtractor.kt:146)
    at com.android.aaptcompiler.TableExtractor$extractResource$parseBagMethod$12.invoke(TableExtractor.kt:436)
    at com.android.aaptcompiler.TableExtractor$extractResource$parseBagMethod$12.invoke(TableExtractor.kt:436)
    at com.android.aaptcompiler.TableExtractor.extractResource(TableExtractor.kt:457)
    at com.android.aaptcompiler.TableExtractor.extractResourceValues(TableExtractor.kt:267)
    at com.android.aaptcompiler.TableExtractor.extract(TableExtractor.kt:182)
    at com.android.aaptcompiler.ResourceCompiler.compileTable(ResourceCompiler.kt:181)
    at com.android.aaptcompiler.ResourceCompiler.access$compileTable(ResourceCompiler.kt:1)
    at com.android.aaptcompiler.ResourceCompiler$getCompileMethod$1.invoke(ResourceCompiler.kt:132)
    at com.android.aaptcompiler.ResourceCompiler$getCompileMethod$1.invoke(ResourceCompiler.kt:132)
    at com.android.aaptcompiler.ResourceCompiler.compileResource(ResourceCompiler.kt:118)

ANSWER

Answered 2022-Feb-28 at 05:46

Cheers everyone I just found it . The solution is just remove the single quotation mark this one '

And if you want to use this mark then use like this

Source https://stackoverflow.com/questions/71290646

QUESTION

How to return an object property from an array of objects in react

Asked 2022-Feb-18 at 14:46

I have an array of object and I need to get a single property, but it's returning undefined and have no idea why. Does anyone know how to solve?

  const [questions, setQuestions] = useState({});
  const [index, setIndex] = useState(0);
  
  async function handleQuestions() {
    const fetchQuestions = await fetchTriviaApi();
    setQuestions(fetchQuestions.results);
  }

  useEffect(() => {
    handleQuestions();
  }, []);

Return of questions:

    (5) [{…}, {…}, {…}, {…}, {…}]
0: {category: 'Entertainment: Music', type: 'boolean', difficulty: 'medium', question: 'Rapper Snoop Dogg&#039;s real name is &#039;Cordozar Calvin Broadus, Jr.&#039;.', correct_answer: 'True', …}
1: {category: 'Entertainment: Video Games', type: 'multiple', difficulty: 'medium', question: 'Which of these &quot;Worms&quot; games featured 3D gameplay?', correct_answer: 'Worms 4: Mayhem', …}
2: {category: 'Vehicles', type: 'multiple', difficulty: 'easy', question: 'What UK Train does NOT go over 125MPH?', correct_answer: 'Sprinter', …}
3: {category: 'Entertainment: Video Games', type: 'boolean', difficulty: 'medium', question: 'Tony Hawk&#039;s Pro Skater was released in 1999.', correct_answer: 'True', …}
4: {category: 'Entertainment: Video Games', type: 'boolean', difficulty: 'hard', question: 'In The Witcher 3, the Zoltan Chivay Gwent card can be found under the Hanged Man&#039;s Tree.', correct_answer: 'True', …}
length: 5

Return of questions[index]:

{category: 'Entertainment: Music', type: 'boolean', difficulty: 'medium', question: 'Rapper Snoop Dogg&#039;s real name is &#039;Cordozar Calvin Broadus, Jr.&#039;.', correct_answer: 'True', …}

Return of questions[index].category: When I try to access any property it returns undefined.

ANSWER

Answered 2022-Feb-18 at 14:41

You need to initialize the questions state to an empty array.

const [questions, setQuestions] = useState([]);

And when accessing the items you have to check whether the questions is populated by the API call.

{questions.length > 0 && questions[index].category}

Source https://stackoverflow.com/questions/71175231

QUESTION

Pandas - Merging Two Data frames with different index names but same amount of Columns

Asked 2022-Feb-16 at 09:26

I'm trying to merge together two Pandas data frames but not able to get the desired result. One data frame has been transposed, and I think for this reason has a header row containing the column indexes. The other has a header row with column names, however each have the same number of columns.

Data Frame 1 (DF1):

+-----------------------------------------------------+
|      NAME      RD1     RD2     RD3     RD4     RD5  |
+-----------------------------------------------------+
| 0     Tom      4       2       1       -       3    |
| 1     Mark     2       2       3       2       1    |
| 2     James    -       1       5       5       4    |
| ...   ...      ...     ...     ...     ...     ...  |
| 30    Ted      2       3       3       3       2    |
+-----------------------------------------------------+

Data Frame 2 (DF2)

+--------------------------------------------------------------------+
|       0      1          2          3          4          5         |
+--------------------------------------------------------------------+
| 0     Desc   Round 1    Round 2    Round 3    Round 4    Round 5   |
| 1     Opp    Hawks      Dolphins   Raptors    Sharks     Bears     |
| 2     Date   02/04      08/04      16/04      24/04      30/04     |
| 3     Venue  MELB       PERTH      MELB       SYD        MELB      |
+--------------------------------------------------------------------+

I've tried using multiple pandas methods (join / append / merge / concat) to join the two data frames without adding any columns. However each method seems to add up both the columns and rows together. I want to be able to do an inner join but I can seem to find a way without having column index numbers. Is there a way I can add a top row to DF1 with the column index numbers the perform an inner join of the two DFs? I have also looked for ways to convert DF into headers instead with no luck as of yet.

The desired final DF would look like:

+--------------------------------------------------------------------+
|       Desc   Round 1    Round 2    Round 3    Round 4    Round 5   |
|       Opp    Hawks      Dolphins   Raptors    Sharks     Bears     |
|       Date   02/04      08/04      16/04      24/04      30/04     |
|       Venue  MELB       PERTH      MELB       SYD        MELB      |
+--------------------------------------------------------------------+
| 0     Tom    4          2          1          -          3         |
| 1     Mark   2          2          3          2          1         |
| 2     James  -          1          5          5          4         |
| ...   ...    ...        ...        ...        ...        ...       |
| 30    Ted    2          3          3          3          2         |
+--------------------------------------------------------------------+

ANSWER

Answered 2022-Feb-16 at 09:13

I think you need MultiIndex by another DataFrame by MultiIndex.from_frame with transposing:

DF1.columns = pd.MultiIndex.from_frame(DF2.T, names= [None] * len(DF2))
print (DF1)
     Desc Round 1  Round 2 Round 3 Round 4 Round 5
      Opp   Hawks Dolphins Raptors  Sharks   Bears
     Date   02/04    08/04   16/04   24/04   30/04
    Venue    MELB    PERTH    MELB     SYD    MELB
0     Tom       4        2       1       -       3
1    Mark       2        2       3       2       1
2   James       -        1       5       5       4
30    Ted       2        3       3       3       2

Source https://stackoverflow.com/questions/71138896

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