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whassup | true. - nothin

 by   jberkel Java Version: Current License: Apache-2.0

 by   jberkel Java Version: Current License: Apache-2.0

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kandi X-RAY | whassup Summary

whassup is a Java library. whassup has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
nothin. watching the game, decryptin' some messages. true. Provides access to WhatsApp messages stored on your Android phone, provided that automatic backups are enabled in the settings. For information about WhatsApp's "security" see the WhatsApp Database Encryption Report.
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kandi-support Support

  • whassup has a low active ecosystem.
  • It has 15 star(s) with 9 fork(s). There are 4 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 1 open issues and 1 have been closed. On average issues are closed in 18 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of whassup is current.
whassup Support
Best in #Java
Average in #Java
whassup Support
Best in #Java
Average in #Java

quality kandi Quality

  • whassup has 0 bugs and 0 code smells.
whassup Quality
Best in #Java
Average in #Java
whassup Quality
Best in #Java
Average in #Java

securitySecurity

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

license License

  • whassup 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.
whassup License
Best in #Java
Average in #Java
whassup License
Best in #Java
Average in #Java

buildReuse

  • whassup 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.
whassup Reuse
Best in #Java
Average in #Java
whassup Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

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

  • Retrieves the most recent timestamp .
    • Utility method to decrypt a database .
      • Get a query from the database .
        • Fetch messages from the given WHassup .
          • Decrypt the given InputStream using the given cipher .
            • Creates a cipher for the given email .
              • Returns the local database file .
                • This method removes the private area from a string
                  • Gets the remote jid number .
                    • Parse byte array .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      whassup Key Features

                      true.

                      usage

                      copy iconCopydownload iconDownload
                      $ git clone https://github.com/jberkel/whassup.git
                      $ cd whassup && mvn install
                      

                      Convert dataframe for category percentages in python

                      copy iconCopydownload iconDownload
                      df = {
                          'Submission_Date': ['2021-01-01', '2021-03-01', '2021-03-01', '2021-03-01', '2021-04-01', '2021-04-01'],
                          'Flair': ['Hedge Fund Tears', 'Hedge Fund Tears', 'Hedge Fund Tears', 'Due Diligence', 'Discussion', 'News']
                      }
                      df = pd.DataFrame(df)
                      df['Submission_Date'] = pd.to_datetime(df['Submission_Date'])
                      display(df)
                      
                      unique_flairs = list(df['Flair'].unique())
                      flair_df = pd.DataFrame()
                      
                      # iterate over unique dates
                      for date in df['Submission_Date'].unique():
                          date_subset = df.loc[df['Submission_Date'] == date]
                          # for flairs in each date, get counts of values
                          counts = date_subset['Flair'].value_counts()
                          # get shares
                          shares = counts / len(date_subset)
                          # transpose series for appending
                          shares_df = pd.DataFrame(shares).transpose()
                          shares_df['Submission_Date'] = date
                          for flair in [x for x in unique_flairs if x not in shares_df.columns]:
                              shares_df[flair] = np.nan
                          # append shares per date
                          flair_df = pd.concat([flair_df, shares_df])
                          flair_df = flair_df.reset_index(drop=True)
                      
                      # rearrange columns
                      flair_df = flair_df[['Submission_Date'] + unique_flairs]
                      display(flair_df)
                      
                      df = {
                          'Submission_Date': ['2021-01-01', '2021-03-01', '2021-03-01', '2021-03-01', '2021-04-01', '2021-04-01'],
                          'Flair': ['Hedge Fund Tears', 'Hedge Fund Tears', 'Hedge Fund Tears', 'Due Diligence', 'Discussion', 'News']
                      }
                      df = pd.DataFrame(df)
                      df['Submission_Date'] = pd.to_datetime(df['Submission_Date'])
                      display(df)
                      
                      unique_flairs = list(df['Flair'].unique())
                      flair_df = pd.DataFrame()
                      
                      # iterate over unique dates
                      for date in df['Submission_Date'].unique():
                          date_subset = df.loc[df['Submission_Date'] == date]
                          # for flairs in each date, get counts of values
                          counts = date_subset['Flair'].value_counts()
                          # get shares
                          shares = counts / len(date_subset)
                          # transpose series for appending
                          shares_df = pd.DataFrame(shares).transpose()
                          shares_df['Submission_Date'] = date
                          for flair in [x for x in unique_flairs if x not in shares_df.columns]:
                              shares_df[flair] = np.nan
                          # append shares per date
                          flair_df = pd.concat([flair_df, shares_df])
                          flair_df = flair_df.reset_index(drop=True)
                      
                      # rearrange columns
                      flair_df = flair_df[['Submission_Date'] + unique_flairs]
                      display(flair_df)
                      
                      data = pandas.DataFrame.from_dict({
                          "Submission_Date": [
                              datetime.date(2021, 1, 1),
                              datetime.date(2021, 1, 1),
                              datetime.date(2021, 1, 2),
                              datetime.date(2021, 1, 2),
                              datetime.date(2021, 1, 3),
                              datetime.date(2021, 1, 3),
                              datetime.date(2021, 1, 3),
                              datetime.date(2021, 1, 4),
                          ],
                          "Flair": ["Discussion", "Due Diligence", "Due Diligence", "Discussion", "Discussion",  "Hedge Fund Tears", "News", "News"],
                      })
                      data["Flair1"] = data.Flair.values # copy to another column to assist pivot
                      res = pandas.pivot_table(
                          data,
                          index=["Submission_Date"],
                          values=['Flair'],
                          columns=['Flair1'],
                          aggfunc='count',
                          fill_value=0
                      
                      )
                      res = pandas.DataFrame(res.to_records())
                      res.columns = [col.replace("('Flair', ", '').replace(")", '') for col in res.columns]
                      res['Total'] = res.astype({col:float for col in res.columns if col != "Submission_Date"}).sum(numeric_only=True, axis=1) # find Total
                      res[[col for col in res.columns if col != "Submission_Date"]] = res[[col for col in res.columns if col != "Submission_Date"]].div(res.Total, axis=0) # divide by Total
                      res = res.drop(columns=['Total']) # drop Total
                      print(res)
                      

                      Community Discussions

                      Trending Discussions on whassup
                      • Convert dataframe for category percentages in python
                      Trending Discussions on whassup

                      QUESTION

                      Convert dataframe for category percentages in python

                      Asked 2021-Oct-03 at 18:22

                      i would like to convert a dataframe with calculating percentage points for a graph later on in python.

                      The current frame looks like this

                      Post ID Title Url Author Score Submission_Date Total_Num_of_Comments Permalink Flair Selftext TitleAndText Word Count
                      k4nllk Update: Whassup bro? https://www.reddit.com/r/GME/comments/k4nllk/update_whassup_bro/ matt_xndever 1 2021-01-01 16:58:48 13 /r/GME/comments/k4nllk/update_whassup_bro/ Hedge Fund Tears asdasdasd asdasdasdasd 59.0

                      Where flairs are the categories i want to look for (over 40). On one submission day (i want to look onto days only), there can be multiple posts with different flairs. These flairs should add up to 100%.

                      So i want to create a dataframe like that:

                      Submission_Date Discussion Due Diligence Hedge Fund Tears News
                      01.01.2021 NaN NaN 1.0 NaN
                      03.01.2021 NaN 0.333333 0.666667 NaN

                      My graph should look like this: Plot stacked (100%) bar chart for multiple categories on multiple dates in Python

                      Can someone help me with the preparation for that?

                      Thanks and best regards

                      ANSWER

                      Answered 2021-Oct-03 at 18:11

                      You can approach the problem as follows:

                      • iterate over unique dates and slice the dataframe for each date
                      • compute counts for each flair category with pandas value_counts()
                      • get shares by dividing over the size of each slice
                      • transpose the pandas series containing the shares for appending
                      • append the shares for each date

                      Here is a sample input:

                      df = {
                          'Submission_Date': ['2021-01-01', '2021-03-01', '2021-03-01', '2021-03-01', '2021-04-01', '2021-04-01'],
                          'Flair': ['Hedge Fund Tears', 'Hedge Fund Tears', 'Hedge Fund Tears', 'Due Diligence', 'Discussion', 'News']
                      }
                      df = pd.DataFrame(df)
                      df['Submission_Date'] = pd.to_datetime(df['Submission_Date'])
                      display(df)
                      

                      enter image description here

                      And here is a sample implementation:

                      unique_flairs = list(df['Flair'].unique())
                      flair_df = pd.DataFrame()
                      
                      # iterate over unique dates
                      for date in df['Submission_Date'].unique():
                          date_subset = df.loc[df['Submission_Date'] == date]
                          # for flairs in each date, get counts of values
                          counts = date_subset['Flair'].value_counts()
                          # get shares
                          shares = counts / len(date_subset)
                          # transpose series for appending
                          shares_df = pd.DataFrame(shares).transpose()
                          shares_df['Submission_Date'] = date
                          for flair in [x for x in unique_flairs if x not in shares_df.columns]:
                              shares_df[flair] = np.nan
                          # append shares per date
                          flair_df = pd.concat([flair_df, shares_df])
                          flair_df = flair_df.reset_index(drop=True)
                      
                      # rearrange columns
                      flair_df = flair_df[['Submission_Date'] + unique_flairs]
                      display(flair_df)
                      

                      Output:

                      enter image description here

                      Source https://stackoverflow.com/questions/69426942

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

                      Vulnerabilities

                      No vulnerabilities reported

                      Install whassup

                      You can download it from GitHub.
                      You can use whassup like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the whassup component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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