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fjord | Fjord , F # programming language for the JVM | Runtime Evironment library

 by   penberg Java Version: Current License: No License

 by   penberg Java Version: Current License: No License

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kandi X-RAY | fjord Summary

fjord is a Java library typically used in Server, Runtime Evironment applications. fjord has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
Fjord is an implementation of the F# programming language for the JVM.
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Support
Quality
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Security
Security
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kandi-support Support

  • fjord has a low active ecosystem.
  • It has 198 star(s) with 24 fork(s). There are 34 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 1 open issues and 2 have been closed. On average issues are closed in 10 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of fjord is current.
fjord Support
Best in #Runtime Evironment
Average in #Runtime Evironment
fjord Support
Best in #Runtime Evironment
Average in #Runtime Evironment

quality kandi Quality

  • fjord has 0 bugs and 0 code smells.
fjord Quality
Best in #Runtime Evironment
Average in #Runtime Evironment
fjord Quality
Best in #Runtime Evironment
Average in #Runtime Evironment

securitySecurity

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

license License

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

buildReuse

  • fjord releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • fjord saves you 1488 person hours of effort in developing the same functionality from scratch.
  • It has 3319 lines of code, 712 functions and 147 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
fjord Reuse
Best in #Runtime Evironment
Average in #Runtime Evironment
fjord Reuse
Best in #Runtime Evironment
Average in #Runtime Evironment
Top functions reviewed by kandi - BETA

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

  • Evaluates an input .
    • Analyze an AST node .
      • Visits this node then the expression before the pattern .
        • Parses the given string into a script fragment
          • Defines the code that loads a value definition .
            • Normalizes the given operator .
              • Gets the prim type .
                • Define class .
                  • Returns a string representation of this constraint .
                    • Bootstrap a static call site .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      fjord Key Features

                      Aims at F# 3.0 language compatibility

                      Building from sources

                      copy iconCopydownload iconDownload
                      export MAVEN_OPTS="-Xmx1g"
                      mvn package
                      

                      Trying it out

                      copy iconCopydownload iconDownload
                      ./bin/fji
                      

                      calculate value basis the column value post dataframe grouping in pandas

                      copy iconCopydownload iconDownload
                      df.sort_values(["IMO", "Datetime"])
                      df.loc[df["State"]=="Sailing", "Time_int"] = df["Datetime"].diff()
                      
                      >>> df
                             IMO          Name    State            Datetime        Time_int
                      0  8300327  SILVER FJORD  Arrival 2021-08-13 04:51:00             NaT
                      1  8300327  SILVER FJORD  Sailing 2021-08-13 22:59:00 0 days 18:08:00
                      2  8300327  SILVER FJORD  Arrival 2021-08-20 10:52:00             NaT
                      3  8300327  SILVER FJORD  Sailing 2021-08-20 20:24:00 0 days 09:32:00
                      4  9340738     FRAMFJORD  Arrival 2021-08-19 11:05:00             NaT
                      5  9340738     FRAMFJORD  Sailing 2021-08-20 17:32:00 1 days 06:27:00
                      
                      df['Time_int'] = df.loc[1::2, 'Datetime'] - df.loc[::2, 'Datetime'].values
                      
                      >>> df
                             IMO          Name    State            Datetime        Time_int
                      0  8300327  SILVER FJORD  Arrival 2021-08-13 04:51:00             NaT
                      1  8300327  SILVER FJORD  Sailing 2021-08-13 22:59:00 0 days 18:08:00
                      2  8300327  SILVER FJORD  Arrival 2021-08-20 10:52:00             NaT
                      3  8300327  SILVER FJORD  Sailing 2021-08-21 02:24:00 0 days 15:32:00
                      4  9340738     FRAMFJORD  Arrival 2021-08-19 11:05:00             NaT
                      5  9340738     FRAMFJORD  Sailing 2021-08-20 17:32:00 1 days 06:27:00
                      
                      df['Time_int'] = df.loc[1::2, 'Datetime'] - df.loc[::2, 'Datetime'].values
                      
                      >>> df
                             IMO          Name    State            Datetime        Time_int
                      0  8300327  SILVER FJORD  Arrival 2021-08-13 04:51:00             NaT
                      1  8300327  SILVER FJORD  Sailing 2021-08-13 22:59:00 0 days 18:08:00
                      2  8300327  SILVER FJORD  Arrival 2021-08-20 10:52:00             NaT
                      3  8300327  SILVER FJORD  Sailing 2021-08-21 02:24:00 0 days 15:32:00
                      4  9340738     FRAMFJORD  Arrival 2021-08-19 11:05:00             NaT
                      5  9340738     FRAMFJORD  Sailing 2021-08-20 17:32:00 1 days 06:27:00
                      
                      df['time_diff'] = df.sort_values(["IMO","Datetime"]).groupby(["IMO"],as_index="False")['Datetime'].diff().dt.seconds.div(3600)
                      cond1=df['State']=="Arrival"
                      df.loc[cond1,"time_diff"]=0
                      
                      IMO      State  Datetime            time_diff
                      8300327 Arrival 2021-08-13 04:51:00 0
                      8300327 Sailing 2021-08-13 22:59:00 18.1333333
                      8300327 Arrival 2021-08-20 10:52:00 0
                      8300327 Sailing 2021-08-21 02:24:00 15.5333333
                      8516263 Arrival 2021-08-22 20:10:00 0
                      8516263 Sailing 2021-08-23 17:25:00 21.25
                      8802882 Arrival 2021-08-18 07:25:00 0
                      8802882 Sailing 2021-08-18 22:01:00 14.6
                      
                      df['time_diff'] = df.sort_values(["IMO","Datetime"]).groupby(["IMO"],as_index="False")['Datetime'].diff().dt.seconds.div(3600)
                      cond1=df['State']=="Arrival"
                      df.loc[cond1,"time_diff"]=0
                      
                      IMO      State  Datetime            time_diff
                      8300327 Arrival 2021-08-13 04:51:00 0
                      8300327 Sailing 2021-08-13 22:59:00 18.1333333
                      8300327 Arrival 2021-08-20 10:52:00 0
                      8300327 Sailing 2021-08-21 02:24:00 15.5333333
                      8516263 Arrival 2021-08-22 20:10:00 0
                      8516263 Sailing 2021-08-23 17:25:00 21.25
                      8802882 Arrival 2021-08-18 07:25:00 0
                      8802882 Sailing 2021-08-18 22:01:00 14.6
                      

                      Horizontal Scrolling in Modal Image gallery thumbnail section is now working

                      copy iconCopydownload iconDownload
                      .modal-content {
                          margin: auto;
                          display: block;
                          background-color: #f2f2f2;
                          border: none;
                          width: 100%;
                          height: 100%;
                          max-width: 1000px;
                          max-height: max-content;  //THIS
                      }
                      

                      I need to do multiple bind statements on the same tkinter combobox, but only the first one will work

                      copy iconCopydownload iconDownload
                      def state_combo_changed(event):
                          state_abb(event)
                          parks_list(abb)
                      
                      state_combo.bind("<<ComboboxSelected>>", state_combo_changed) 
                      
                      state_combo.bind("<<ComboboxSelected>>", state_abb)
                      state_combo.bind("<<ComboboxSelected>>", parks_list, add=True)
                      
                      def state_combo_changed(event):
                          state_abb(event)
                          parks_list(abb)
                      
                      state_combo.bind("<<ComboboxSelected>>", state_combo_changed) 
                      
                      state_combo.bind("<<ComboboxSelected>>", state_abb)
                      state_combo.bind("<<ComboboxSelected>>", parks_list, add=True)
                      

                      function that returns object names

                      copy iconCopydownload iconDownload
                      let wish = userId.wishlist;
                      
                      users[userId].visited.map((visited) => wish.includes(visited));
                      
                      const users= {
                        "karah.branch3": { visited: [1], wishlist: [4,6] },
                        "dwayne.m55": { visited: [2,5,1], wishlist: [] },
                        "thiagostrong1": { visited: [5], wishlist: [6,3,2] },
                        "don.kim1990": { visited: [2,6], wishlist: [1] }
                      };
                      
                      function getUsersForUserWishlist(users, userId) {
                        const wishlist = users[userId].wishlist;
                        // Get all user names
                        return Object.keys(users)
                          // Filter them
                          .filter(
                            // If the wishlist has some elements which that user has visited
                            name => wishlist.some(park => users[name].visited.includes(park))
                          );
                      }
                      
                      console.log(getUsersForUserWishlist(users, "karah.branch3")); //> ["don.kim1990"]
                      console.log(getUsersForUserWishlist(users, "dwayne.m55")); //> []
                      let wish = userId.wishlist;
                      
                      users[userId].visited.map((visited) => wish.includes(visited));
                      
                      const users= {
                        "karah.branch3": { visited: [1], wishlist: [4,6] },
                        "dwayne.m55": { visited: [2,5,1], wishlist: [] },
                        "thiagostrong1": { visited: [5], wishlist: [6,3,2] },
                        "don.kim1990": { visited: [2,6], wishlist: [1] }
                      };
                      
                      function getUsersForUserWishlist(users, userId) {
                        const wishlist = users[userId].wishlist;
                        // Get all user names
                        return Object.keys(users)
                          // Filter them
                          .filter(
                            // If the wishlist has some elements which that user has visited
                            name => wishlist.some(park => users[name].visited.includes(park))
                          );
                      }
                      
                      console.log(getUsersForUserWishlist(users, "karah.branch3")); //> ["don.kim1990"]
                      console.log(getUsersForUserWishlist(users, "dwayne.m55")); //> []
                      let wish = userId.wishlist;
                      
                      users[userId].visited.map((visited) => wish.includes(visited));
                      
                      const users= {
                        "karah.branch3": { visited: [1], wishlist: [4,6] },
                        "dwayne.m55": { visited: [2,5,1], wishlist: [] },
                        "thiagostrong1": { visited: [5], wishlist: [6,3,2] },
                        "don.kim1990": { visited: [2,6], wishlist: [1] }
                      };
                      
                      function getUsersForUserWishlist(users, userId) {
                        const wishlist = users[userId].wishlist;
                        // Get all user names
                        return Object.keys(users)
                          // Filter them
                          .filter(
                            // If the wishlist has some elements which that user has visited
                            name => wishlist.some(park => users[name].visited.includes(park))
                          );
                      }
                      
                      console.log(getUsersForUserWishlist(users, "karah.branch3")); //> ["don.kim1990"]
                      console.log(getUsersForUserWishlist(users, "dwayne.m55")); //> []

                      Function userHasVisitedAllParksInState

                      copy iconCopydownload iconDownload
                      function userHasVisitedAllParksInState(parks, users, state, userId) {
                        var parksForState = parks.filter((park) => park.location.state === state);
                        return users[userId].visited.length === parksForState.length;
                      }
                      
                      function userHasVisitedAllParksInState1(parks, users, state, userId) {
                        var user = users[userId];
                        var parksForState = parks.filter((park) => park.location.state === state);
                        // Assume the person did visit all to start
                        var visitedAll = true;
                        for (var i = 0; i < parksForState.length; i++) {
                          // If the park we're checking atm is in the visited array, we've still visited all parks. If parksForState[i].id is not in user.visited
                          visitedAll = visitedAll && user.visited.indexOf(parksForState[i].id) > -1;
                        }
                        return visitedAll;
                      }
                      
                      function userHasVisitedAllParksInState(parks, users, state, userId) {
                        var parksForState = parks.filter((park) => park.location.state === state);
                        return users[userId].visited.length === parksForState.length;
                      }
                      
                      function userHasVisitedAllParksInState1(parks, users, state, userId) {
                        var user = users[userId];
                        var parksForState = parks.filter((park) => park.location.state === state);
                        // Assume the person did visit all to start
                        var visitedAll = true;
                        for (var i = 0; i < parksForState.length; i++) {
                          // If the park we're checking atm is in the visited array, we've still visited all parks. If parksForState[i].id is not in user.visited
                          visitedAll = visitedAll && user.visited.indexOf(parksForState[i].id) > -1;
                        }
                        return visitedAll;
                      }
                      

                      Adding a favicon to an existing hugo theme

                      copy iconCopydownload iconDownload
                      ...
                      {% if site.params.favicon %}
                      <link rel="icon" href="{{ site.params.favicon | relative_url }}">
                      {% endif %}
                      <link rel="apple-touch-icon" sizes="180x180" href="/apple-touch-icon.png">
                      <link rel="icon" type="image/png" sizes="32x32" href="/favicon-32x32.png">
                      <link rel="icon" type="image/png" sizes="16x16" href="/favicon-16x16.png">
                      <link rel="manifest" href="/site.webmanifest">
                      

                      I need user input to point from one dataframe to another and display a column from the second dataframe-python

                      copy iconCopydownload iconDownload
                      sdf = pd.DataFrame({'abbr':statedf['Abbr'].values.flatten(),'state':statedf['State'].values.flatten()})
                      
                      val = sdf[sdf['abbr']==entry]
                      state_name = val.values.flatten()[1]
                      
                      ndf = {state_name:pdf[state_name].values}
                      print(tabulate(ndf, headers='keys', tablefmt='simple', showindex=False))
                      
                      sdf = pd.DataFrame({'abbr':statedf['Abbr'].values.flatten(),'state':statedf['State'].values.flatten()})
                      
                      val = sdf[sdf['abbr']==entry]
                      state_name = val.values.flatten()[1]
                      
                      ndf = {state_name:pdf[state_name].values}
                      print(tabulate(ndf, headers='keys', tablefmt='simple', showindex=False))
                      
                      sdf = pd.DataFrame({'abbr':statedf['Abbr'].values.flatten(),'state':statedf['State'].values.flatten()})
                      
                      val = sdf[sdf['abbr']==entry]
                      state_name = val.values.flatten()[1]
                      
                      ndf = {state_name:pdf[state_name].values}
                      print(tabulate(ndf, headers='keys', tablefmt='simple', showindex=False))
                      
                      STATE_DICT = {
                          'state_name': {
                              'AK': 'Alaska',
                              'AS': 'American Samoa',
                              'AZ': 'Arizona',
                              'AR': 'Arkansas',
                              'CA': 'California',
                              'CO': 'Colorado',
                              'FL': 'Florida',
                              'HI': 'Hawaii',
                              'KY': 'Kentucky',
                              'ME': 'Maine',
                              'MI': 'Michigan',
                              'MN': 'Minnesota',
                              'MT': 'Montana',
                              'NV': 'Nevada',
                              'NM': 'New Mexico',
                              'NC': 'North Carolina',
                              'ND': 'North Dakota',
                              'OH': 'Ohio',
                              'OR': 'Oregon',
                              'SC': 'South Carolina',
                              'SD': 'South Dakota',
                              'TN': 'Tennessee',
                              'TX': 'Texas',
                              'USVI': 'US Vergin Islands',
                              'UT': 'Utah',
                              'VA': 'Virginia',
                              'WA': 'Washington',
                              'WY': 'Wyoming'}
                      }
                      
                      PARKS_DICT = {
                          'state_name': {
                              'DENA': 'Alaska',
                              'GAAR': 'Alaska',
                              'GLBA': 'Alaska',
                              'KATM': 'Alaska',
                              'KEFJ': 'Alaska',
                              'KOVA': 'Alaska',
                              'LACLk': 'Alaska',
                              'WRST': 'Alaska',
                              'NSPA': 'American_Samoa',
                              'GRCA': 'Arizona',
                              'PEFO': 'Arizona',
                              'SAGU': 'Arizona',
                              'HOSP': 'Arkansas',
                              'CHIS': 'California',
                              'DVNP': 'California',
                              'JOTR': 'California',
                              'KICA': 'California',
                              'LAVO': 'California',
                              'REDW': 'California',
                              'SEKI': 'California',
                              'YOSE': 'California',
                              'BLCA': 'Caolorado',
                              'GRSA': 'Caolorado',
                              'MEVE': 'Caolorado',
                              'ROMO': 'Caolorado',
                              'BISC': 'Florida',
                              'DRTO': 'Florida',
                              'EVER': 'Florida',
                              'HALE': 'Hawaii',
                              'HAVO': 'Hawaii',
                              'MACA': 'Kentucky',
                              'ACAD': 'Maine',
                              'ISRO': 'Michigan',
                              'VOYA': 'Minnesota',
                              'GLAC': 'Montana',
                              'GRBA': 'Nevada',
                              'CAVE': 'New_Mexico',
                              'GRSM': 'Tennessee',
                              'THRO': 'North_Dakota',
                              'CUVA': 'Ohio',
                              'CRLA': 'Oregon',
                              'COSW': 'South_Carolina',
                              'BADL': 'South_Dakota',
                              'BIBE': 'Texas',
                              'GUMO': 'Texas',
                              'VIIS': 'US_Virgin_Islands',
                              'ARCH': 'Utah',
                              'BRCA': 'Utah',
                              'CANY': 'Utah',
                              'CARE': 'Utah',
                              'ZION': 'Utah',
                              'SHEN': 'Virginia',
                              'MORA': 'Washington',
                              'NOCA': 'Washington',
                              'OLYM': 'Washington',
                              'GRTE': 'Wyoming',
                              'YELL': 'Wyoming'},
                          'park_name': {
                              'DENA': 'Denali National Park and Preserve',
                              'GAAR': 'Gates of the Arctic National Park',
                              'GLBA': 'Glacier Bay National Park',
                              'KATM': 'Katmai National Park and Preserve',
                              'KEFJ': 'Kenai Fjords National Park',
                              'KOVA': 'Kobuk Valley National Park',
                              'LACLk': 'Lake Clark National Park',
                              'WRST': 'Wrangell – St Elias National Park and Preserve',
                              'NSPA': 'National Park of American Samoa',
                              'GRCA': 'Grand Canyon National Park',
                              'PEFO': 'Petrified Forest National Park',
                              'SAGU': 'Saguaro National Park',
                              'HOSP': 'Hot Springs National Park',
                              'CHIS': 'Channel Islands National Park',
                              'DVNP': 'Death Valley National Park',
                              'JOTR': 'Joshua Tree National Park',
                              'KICA': 'Kings Canyon National Park',
                              'LAVO': 'Lassen Volcanic National Park',
                              'REDW': 'Redwood National Park',
                              'SEKI': 'Sequoia National Park',
                              'YOSE': 'Yosemite National Park',
                              'BLCA': 'Black Canyon of the Gunnison National Park',
                              'GRSA': 'Great Sand Dunes National Park and Preserve',
                              'MEVE': 'Mesa Verde National Park',
                              'ROMO': 'Rocky Mountain National Park',
                              'BISC': 'Biscayne National Park',
                              'DRTO': 'Dry Tortugas National Park',
                              'EVER': 'Everglades National Park',
                              'HALE': 'Haleakala National Park',
                              'HAVO': 'Hawaii Volcanoes National Park',
                              'MACA': 'Mammoth Cave National Park',
                              'ACAD': 'Acadia National Park',
                              'ISRO': 'Isle Royale National Park',
                              'VOYA': 'Voyageurs National Park',
                              'GLAC': 'Glacier National Park',
                              'GRBA': 'Great Basin National Park',
                              'CAVE': 'Carlsbad Caverns National Park',
                              'GRSM': 'Great Smoky Mountains National Park',
                              'THRO': 'Theodore Roosevelt National Park',
                              'CUVA': 'Cuyahoga Valley National Park',
                              'CRLA': 'Crater Lake National Park',
                              'COSW': 'Congaree National Park',
                              'BADL': 'Badlands National Park',
                              'BIBE': 'Big Bend National Park',
                              'GUMO': 'Guadalupe Mountains National Park',
                              'VIIS': 'Virgin Islands National Park',
                              'ARCH': 'Arches National Park',
                              'BRCA': 'Bryce Canyon National Park',
                              'CANY': 'Canyonlands National Park',
                              'CARE': 'Capitol Reef National Park',
                              'ZION': 'Zion National Park',
                              'SHEN': 'Shenandoah National Park',
                              'MORA': 'Mount Rainier National Park',
                              'NOCA': 'North Cascades National Park',
                              'OLYM': 'Olympic National Park',
                              'GRTE': 'Grand Teton National Park',
                              'YELL': 'Yellowstone National Park'}
                      }
                      
                      # make your dataframes:
                      states = pd.DataFrame(STATE_DICT).rename_axis('state_abbrev')
                      parks = pd.DataFrame(PARKS_DICT).rename_axis('park_abbrev')
                      
                      # query
                      state_abbrev = input('enter state abbreviation: ')
                      state_name = states.loc[state_abbrev, 'state_name']
                      parks_list = parks.loc[parks.state_name == state_name]
                      
                      df_joined = pd.merge(states.reset_index(), parks.reset_index(), on='state_name', how='inner')
                      parks_list = df_joined[df_joined.state_abbrev == state_abbrev]
                      
                      STATE_DICT = {
                          'state_name': {
                              'AK': 'Alaska',
                              'AS': 'American Samoa',
                              'AZ': 'Arizona',
                              'AR': 'Arkansas',
                              'CA': 'California',
                              'CO': 'Colorado',
                              'FL': 'Florida',
                              'HI': 'Hawaii',
                              'KY': 'Kentucky',
                              'ME': 'Maine',
                              'MI': 'Michigan',
                              'MN': 'Minnesota',
                              'MT': 'Montana',
                              'NV': 'Nevada',
                              'NM': 'New Mexico',
                              'NC': 'North Carolina',
                              'ND': 'North Dakota',
                              'OH': 'Ohio',
                              'OR': 'Oregon',
                              'SC': 'South Carolina',
                              'SD': 'South Dakota',
                              'TN': 'Tennessee',
                              'TX': 'Texas',
                              'USVI': 'US Vergin Islands',
                              'UT': 'Utah',
                              'VA': 'Virginia',
                              'WA': 'Washington',
                              'WY': 'Wyoming'}
                      }
                      
                      PARKS_DICT = {
                          'state_name': {
                              'DENA': 'Alaska',
                              'GAAR': 'Alaska',
                              'GLBA': 'Alaska',
                              'KATM': 'Alaska',
                              'KEFJ': 'Alaska',
                              'KOVA': 'Alaska',
                              'LACLk': 'Alaska',
                              'WRST': 'Alaska',
                              'NSPA': 'American_Samoa',
                              'GRCA': 'Arizona',
                              'PEFO': 'Arizona',
                              'SAGU': 'Arizona',
                              'HOSP': 'Arkansas',
                              'CHIS': 'California',
                              'DVNP': 'California',
                              'JOTR': 'California',
                              'KICA': 'California',
                              'LAVO': 'California',
                              'REDW': 'California',
                              'SEKI': 'California',
                              'YOSE': 'California',
                              'BLCA': 'Caolorado',
                              'GRSA': 'Caolorado',
                              'MEVE': 'Caolorado',
                              'ROMO': 'Caolorado',
                              'BISC': 'Florida',
                              'DRTO': 'Florida',
                              'EVER': 'Florida',
                              'HALE': 'Hawaii',
                              'HAVO': 'Hawaii',
                              'MACA': 'Kentucky',
                              'ACAD': 'Maine',
                              'ISRO': 'Michigan',
                              'VOYA': 'Minnesota',
                              'GLAC': 'Montana',
                              'GRBA': 'Nevada',
                              'CAVE': 'New_Mexico',
                              'GRSM': 'Tennessee',
                              'THRO': 'North_Dakota',
                              'CUVA': 'Ohio',
                              'CRLA': 'Oregon',
                              'COSW': 'South_Carolina',
                              'BADL': 'South_Dakota',
                              'BIBE': 'Texas',
                              'GUMO': 'Texas',
                              'VIIS': 'US_Virgin_Islands',
                              'ARCH': 'Utah',
                              'BRCA': 'Utah',
                              'CANY': 'Utah',
                              'CARE': 'Utah',
                              'ZION': 'Utah',
                              'SHEN': 'Virginia',
                              'MORA': 'Washington',
                              'NOCA': 'Washington',
                              'OLYM': 'Washington',
                              'GRTE': 'Wyoming',
                              'YELL': 'Wyoming'},
                          'park_name': {
                              'DENA': 'Denali National Park and Preserve',
                              'GAAR': 'Gates of the Arctic National Park',
                              'GLBA': 'Glacier Bay National Park',
                              'KATM': 'Katmai National Park and Preserve',
                              'KEFJ': 'Kenai Fjords National Park',
                              'KOVA': 'Kobuk Valley National Park',
                              'LACLk': 'Lake Clark National Park',
                              'WRST': 'Wrangell – St Elias National Park and Preserve',
                              'NSPA': 'National Park of American Samoa',
                              'GRCA': 'Grand Canyon National Park',
                              'PEFO': 'Petrified Forest National Park',
                              'SAGU': 'Saguaro National Park',
                              'HOSP': 'Hot Springs National Park',
                              'CHIS': 'Channel Islands National Park',
                              'DVNP': 'Death Valley National Park',
                              'JOTR': 'Joshua Tree National Park',
                              'KICA': 'Kings Canyon National Park',
                              'LAVO': 'Lassen Volcanic National Park',
                              'REDW': 'Redwood National Park',
                              'SEKI': 'Sequoia National Park',
                              'YOSE': 'Yosemite National Park',
                              'BLCA': 'Black Canyon of the Gunnison National Park',
                              'GRSA': 'Great Sand Dunes National Park and Preserve',
                              'MEVE': 'Mesa Verde National Park',
                              'ROMO': 'Rocky Mountain National Park',
                              'BISC': 'Biscayne National Park',
                              'DRTO': 'Dry Tortugas National Park',
                              'EVER': 'Everglades National Park',
                              'HALE': 'Haleakala National Park',
                              'HAVO': 'Hawaii Volcanoes National Park',
                              'MACA': 'Mammoth Cave National Park',
                              'ACAD': 'Acadia National Park',
                              'ISRO': 'Isle Royale National Park',
                              'VOYA': 'Voyageurs National Park',
                              'GLAC': 'Glacier National Park',
                              'GRBA': 'Great Basin National Park',
                              'CAVE': 'Carlsbad Caverns National Park',
                              'GRSM': 'Great Smoky Mountains National Park',
                              'THRO': 'Theodore Roosevelt National Park',
                              'CUVA': 'Cuyahoga Valley National Park',
                              'CRLA': 'Crater Lake National Park',
                              'COSW': 'Congaree National Park',
                              'BADL': 'Badlands National Park',
                              'BIBE': 'Big Bend National Park',
                              'GUMO': 'Guadalupe Mountains National Park',
                              'VIIS': 'Virgin Islands National Park',
                              'ARCH': 'Arches National Park',
                              'BRCA': 'Bryce Canyon National Park',
                              'CANY': 'Canyonlands National Park',
                              'CARE': 'Capitol Reef National Park',
                              'ZION': 'Zion National Park',
                              'SHEN': 'Shenandoah National Park',
                              'MORA': 'Mount Rainier National Park',
                              'NOCA': 'North Cascades National Park',
                              'OLYM': 'Olympic National Park',
                              'GRTE': 'Grand Teton National Park',
                              'YELL': 'Yellowstone National Park'}
                      }
                      
                      # make your dataframes:
                      states = pd.DataFrame(STATE_DICT).rename_axis('state_abbrev')
                      parks = pd.DataFrame(PARKS_DICT).rename_axis('park_abbrev')
                      
                      # query
                      state_abbrev = input('enter state abbreviation: ')
                      state_name = states.loc[state_abbrev, 'state_name']
                      parks_list = parks.loc[parks.state_name == state_name]
                      
                      df_joined = pd.merge(states.reset_index(), parks.reset_index(), on='state_name', how='inner')
                      parks_list = df_joined[df_joined.state_abbrev == state_abbrev]
                      
                      STATE_DICT = {
                          'state_name': {
                              'AK': 'Alaska',
                              'AS': 'American Samoa',
                              'AZ': 'Arizona',
                              'AR': 'Arkansas',
                              'CA': 'California',
                              'CO': 'Colorado',
                              'FL': 'Florida',
                              'HI': 'Hawaii',
                              'KY': 'Kentucky',
                              'ME': 'Maine',
                              'MI': 'Michigan',
                              'MN': 'Minnesota',
                              'MT': 'Montana',
                              'NV': 'Nevada',
                              'NM': 'New Mexico',
                              'NC': 'North Carolina',
                              'ND': 'North Dakota',
                              'OH': 'Ohio',
                              'OR': 'Oregon',
                              'SC': 'South Carolina',
                              'SD': 'South Dakota',
                              'TN': 'Tennessee',
                              'TX': 'Texas',
                              'USVI': 'US Vergin Islands',
                              'UT': 'Utah',
                              'VA': 'Virginia',
                              'WA': 'Washington',
                              'WY': 'Wyoming'}
                      }
                      
                      PARKS_DICT = {
                          'state_name': {
                              'DENA': 'Alaska',
                              'GAAR': 'Alaska',
                              'GLBA': 'Alaska',
                              'KATM': 'Alaska',
                              'KEFJ': 'Alaska',
                              'KOVA': 'Alaska',
                              'LACLk': 'Alaska',
                              'WRST': 'Alaska',
                              'NSPA': 'American_Samoa',
                              'GRCA': 'Arizona',
                              'PEFO': 'Arizona',
                              'SAGU': 'Arizona',
                              'HOSP': 'Arkansas',
                              'CHIS': 'California',
                              'DVNP': 'California',
                              'JOTR': 'California',
                              'KICA': 'California',
                              'LAVO': 'California',
                              'REDW': 'California',
                              'SEKI': 'California',
                              'YOSE': 'California',
                              'BLCA': 'Caolorado',
                              'GRSA': 'Caolorado',
                              'MEVE': 'Caolorado',
                              'ROMO': 'Caolorado',
                              'BISC': 'Florida',
                              'DRTO': 'Florida',
                              'EVER': 'Florida',
                              'HALE': 'Hawaii',
                              'HAVO': 'Hawaii',
                              'MACA': 'Kentucky',
                              'ACAD': 'Maine',
                              'ISRO': 'Michigan',
                              'VOYA': 'Minnesota',
                              'GLAC': 'Montana',
                              'GRBA': 'Nevada',
                              'CAVE': 'New_Mexico',
                              'GRSM': 'Tennessee',
                              'THRO': 'North_Dakota',
                              'CUVA': 'Ohio',
                              'CRLA': 'Oregon',
                              'COSW': 'South_Carolina',
                              'BADL': 'South_Dakota',
                              'BIBE': 'Texas',
                              'GUMO': 'Texas',
                              'VIIS': 'US_Virgin_Islands',
                              'ARCH': 'Utah',
                              'BRCA': 'Utah',
                              'CANY': 'Utah',
                              'CARE': 'Utah',
                              'ZION': 'Utah',
                              'SHEN': 'Virginia',
                              'MORA': 'Washington',
                              'NOCA': 'Washington',
                              'OLYM': 'Washington',
                              'GRTE': 'Wyoming',
                              'YELL': 'Wyoming'},
                          'park_name': {
                              'DENA': 'Denali National Park and Preserve',
                              'GAAR': 'Gates of the Arctic National Park',
                              'GLBA': 'Glacier Bay National Park',
                              'KATM': 'Katmai National Park and Preserve',
                              'KEFJ': 'Kenai Fjords National Park',
                              'KOVA': 'Kobuk Valley National Park',
                              'LACLk': 'Lake Clark National Park',
                              'WRST': 'Wrangell – St Elias National Park and Preserve',
                              'NSPA': 'National Park of American Samoa',
                              'GRCA': 'Grand Canyon National Park',
                              'PEFO': 'Petrified Forest National Park',
                              'SAGU': 'Saguaro National Park',
                              'HOSP': 'Hot Springs National Park',
                              'CHIS': 'Channel Islands National Park',
                              'DVNP': 'Death Valley National Park',
                              'JOTR': 'Joshua Tree National Park',
                              'KICA': 'Kings Canyon National Park',
                              'LAVO': 'Lassen Volcanic National Park',
                              'REDW': 'Redwood National Park',
                              'SEKI': 'Sequoia National Park',
                              'YOSE': 'Yosemite National Park',
                              'BLCA': 'Black Canyon of the Gunnison National Park',
                              'GRSA': 'Great Sand Dunes National Park and Preserve',
                              'MEVE': 'Mesa Verde National Park',
                              'ROMO': 'Rocky Mountain National Park',
                              'BISC': 'Biscayne National Park',
                              'DRTO': 'Dry Tortugas National Park',
                              'EVER': 'Everglades National Park',
                              'HALE': 'Haleakala National Park',
                              'HAVO': 'Hawaii Volcanoes National Park',
                              'MACA': 'Mammoth Cave National Park',
                              'ACAD': 'Acadia National Park',
                              'ISRO': 'Isle Royale National Park',
                              'VOYA': 'Voyageurs National Park',
                              'GLAC': 'Glacier National Park',
                              'GRBA': 'Great Basin National Park',
                              'CAVE': 'Carlsbad Caverns National Park',
                              'GRSM': 'Great Smoky Mountains National Park',
                              'THRO': 'Theodore Roosevelt National Park',
                              'CUVA': 'Cuyahoga Valley National Park',
                              'CRLA': 'Crater Lake National Park',
                              'COSW': 'Congaree National Park',
                              'BADL': 'Badlands National Park',
                              'BIBE': 'Big Bend National Park',
                              'GUMO': 'Guadalupe Mountains National Park',
                              'VIIS': 'Virgin Islands National Park',
                              'ARCH': 'Arches National Park',
                              'BRCA': 'Bryce Canyon National Park',
                              'CANY': 'Canyonlands National Park',
                              'CARE': 'Capitol Reef National Park',
                              'ZION': 'Zion National Park',
                              'SHEN': 'Shenandoah National Park',
                              'MORA': 'Mount Rainier National Park',
                              'NOCA': 'North Cascades National Park',
                              'OLYM': 'Olympic National Park',
                              'GRTE': 'Grand Teton National Park',
                              'YELL': 'Yellowstone National Park'}
                      }
                      
                      # make your dataframes:
                      states = pd.DataFrame(STATE_DICT).rename_axis('state_abbrev')
                      parks = pd.DataFrame(PARKS_DICT).rename_axis('park_abbrev')
                      
                      # query
                      state_abbrev = input('enter state abbreviation: ')
                      state_name = states.loc[state_abbrev, 'state_name']
                      parks_list = parks.loc[parks.state_name == state_name]
                      
                      df_joined = pd.merge(states.reset_index(), parks.reset_index(), on='state_name', how='inner')
                      parks_list = df_joined[df_joined.state_abbrev == state_abbrev]
                      

                      deploying jhipster on heroku with remote Mysql db not working

                      copy iconCopydownload iconDownload
                      jdbc:mysql://username:pass@word@localhost:3306/dbname
                      
                      jdbc:mysql://username:pass%40word@localhost:3306/dbname
                      
                      jdbc:mysql://username:pass@word@localhost:3306/dbname
                      
                      jdbc:mysql://username:pass%40word@localhost:3306/dbname
                      

                      Carousel to be the same size as image

                      copy iconCopydownload iconDownload
                              .modal-content .mySlides img{
                                  display:block;
                                  margin:auto;
                              }

                      Typescript, React: TypeError: Cannot read property 'push' of undefined

                      copy iconCopydownload iconDownload
                      class Library {
                          static library: Array<object> // <-- Declared but not initialized
                          
                          // ---- this is not static property, this will create a property for class
                          library = [ 
                              {
                              author: 'Johann Fjord Kallenberg',
                              title: 'Norvegian dragonslayers',
                              pages: '443',
                              rating: 4
                              }, {
                              author: 'Dungo McCallahey',
                              title: 'Irish Golden Era',
                              pages: '318',
                              rating: 3
                              }, {
                              author: 'John Doe Mouse',
                              title: 'Preparing to fight a Wolfgod',
                              pages: '714',
                              rating: 4
                              }
                          ]
                      }
                      
                      console.log( 'Static Property' , Library.library);
                      
                      const libObj = new Library();
                      console.log( 'Instance Property' , libObj.library);
                      class Library {
                          static library: Array<object> = [
                              {
                              author: 'Johann Fjord Kallenberg',
                              title: 'Norvegian dragonslayers',
                              pages: '443',
                              rating: 4
                              }, {
                              author: 'Dungo McCallahey',
                              title: 'Irish Golden Era',
                              pages: '318',
                              rating: 3
                              }, {
                              author: 'John Doe Mouse',
                              title: 'Preparing to fight a Wolfgod',
                              pages: '714',
                              rating: 4
                              }
                          ]
                      }
                      
                      console.log( 'Static Property' , Library.library);
                      class Library {
                          static library: Array<object> // <-- Declared but not initialized
                          
                          // ---- this is not static property, this will create a property for class
                          library = [ 
                              {
                              author: 'Johann Fjord Kallenberg',
                              title: 'Norvegian dragonslayers',
                              pages: '443',
                              rating: 4
                              }, {
                              author: 'Dungo McCallahey',
                              title: 'Irish Golden Era',
                              pages: '318',
                              rating: 3
                              }, {
                              author: 'John Doe Mouse',
                              title: 'Preparing to fight a Wolfgod',
                              pages: '714',
                              rating: 4
                              }
                          ]
                      }
                      
                      console.log( 'Static Property' , Library.library);
                      
                      const libObj = new Library();
                      console.log( 'Instance Property' , libObj.library);
                      class Library {
                          static library: Array<object> = [
                              {
                              author: 'Johann Fjord Kallenberg',
                              title: 'Norvegian dragonslayers',
                              pages: '443',
                              rating: 4
                              }, {
                              author: 'Dungo McCallahey',
                              title: 'Irish Golden Era',
                              pages: '318',
                              rating: 3
                              }, {
                              author: 'John Doe Mouse',
                              title: 'Preparing to fight a Wolfgod',
                              pages: '714',
                              rating: 4
                              }
                          ]
                      }
                      
                      console.log( 'Static Property' , Library.library);
                      class Library {
                        static library = [
                              {
                              author: 'Johann Fjord Kallenberg',
                              title: 'Norvegian dragonslayers',
                              pages: '443',
                              rating: 4
                              }, {
                              author: 'Dungo McCallahey',
                              title: 'Irish Golden Era',
                              pages: '318',
                              rating: 3
                              }, {
                              author: 'John Doe Mouse',
                              title: 'Preparing to fight a Wolfgod',
                              pages: '714',
                              rating: 4
                              }
                          ];
                      }
                      

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                      QUESTION

                      calculate value basis the column value post dataframe grouping in pandas

                      Asked 2021-Sep-03 at 15:35

                      I am trying to find difference in time between sailing and arrival for each IMO number.

                      IMO       Name          State      Datetime
                      8300327 SILVER FJORD    Arrival 13/08/2021 04:51
                      8300327 SILVER FJORD    Sailing 13/08/2021 22:59
                      8300327 SILVER FJORD    Arrival 20/08/2021 10:52
                      8300327 SILVER FJORD    Sailing 20/08/2021 20:24
                      9340738 FRAMFJORD       Arrival 19/08/2021 11:05
                      9340738 FRAMFJORD       Sailing 20/08/2021 17:32
                      

                      for above dataframe the output should be

                      IMO     Name            State     Datetime           Time_int
                      8300327 SILVER FJORD    Arrival 13/08/2021 04:51    
                      8300327 SILVER FJORD    Sailing 13/08/2021 22:59    18:08:00
                      8300327 SILVER FJORD    Arrival 20/08/2021 10:52    
                      8300327 SILVER FJORD    Sailing 20/08/2021 20:24    09:32:00
                      9340738 FRAMFJORD       Arrival 19/08/2021 11:05    
                      9340738 FRAMFJORD       Sailing 20/08/2021 17:32    06:27:00
                      

                      I have written below code for the calculation

                      def dwell_calc(df):
                          if (df['State'] == "Sailing"):
                              val = df['Datetime'].diff().dt.seconds.div(3600).fillna(0).reset_index()
                      
                              return val
                      
                      
                      # data.sort_values(['IMO', 'Datetime'], inplace=True)
                      
                      
                      cond2=(data['State']=='Sailing')
                      data.loc[cond2, 'time_int'] = dwell_calc(data)
                      
                      print(data['time_int'])
                      

                      I am getting error:

                       if (df['State'] == "Sailing"):
                      ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
                      

                      Please help with solution to find time interval using python

                      ANSWER

                      Answered 2021-Aug-31 at 19:55

                      Try diff after sort_values():

                      df.sort_values(["IMO", "Datetime"])
                      df.loc[df["State"]=="Sailing", "Time_int"] = df["Datetime"].diff()
                      
                      >>> df
                             IMO          Name    State            Datetime        Time_int
                      0  8300327  SILVER FJORD  Arrival 2021-08-13 04:51:00             NaT
                      1  8300327  SILVER FJORD  Sailing 2021-08-13 22:59:00 0 days 18:08:00
                      2  8300327  SILVER FJORD  Arrival 2021-08-20 10:52:00             NaT
                      3  8300327  SILVER FJORD  Sailing 2021-08-20 20:24:00 0 days 09:32:00
                      4  9340738     FRAMFJORD  Arrival 2021-08-19 11:05:00             NaT
                      5  9340738     FRAMFJORD  Sailing 2021-08-20 17:32:00 1 days 06:27:00
                      

                      Source https://stackoverflow.com/questions/69004415

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