Grouper | Python command line tool to manage Azure Network Security | Security library

 by   marlinspike Python Version: Current License: No License

kandi X-RAY | Grouper Summary

kandi X-RAY | Grouper Summary

Grouper is a Python library typically used in Security applications. Grouper has no bugs, it has build file available and it has low support. However Grouper has 1 vulnerabilities. You can download it from GitHub.

A Python 3 command line tool to manage Azure Network Security Group Rules.
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              Grouper has a low active ecosystem.
              It has 10 star(s) with 3 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              Grouper has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Grouper is current.

            kandi-Quality Quality

              Grouper has no bugs reported.

            kandi-Security Security

              Grouper has 1 vulnerability issues reported (0 critical, 0 high, 1 medium, 0 low).

            kandi-License License

              Grouper does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              Grouper 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.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Grouper and discovered the below as its top functions. This is intended to give you an instant insight into Grouper implemented functionality, and help decide if they suit your requirements.
            • Convert a CSV file to ARM format
            • Parse the contents of the CSV file
            • Write an ARM template file
            • Builds the Nsg_Template from a list of NSG rules
            • Import NSG rules from an Azure account
            • Returns a dictionary of attribute values
            • Reorder a list of preferred order
            • Writes the security rules to a csv file
            • Create a CLI script from a csv file
            • Writes CLI_SCRIPT to file
            • Get attribute value by name
            • Generates a sample data file
            • Load a prefixed preference
            • Print a pretty formatted output table
            • Create a security rule from a dictionary
            Get all kandi verified functions for this library.

            Grouper Key Features

            No Key Features are available at this moment for Grouper.

            Grouper Examples and Code Snippets

            No Code Snippets are available at this moment for Grouper.

            Community Discussions

            QUESTION

            Pandas: Subtract timestamps
            Asked 2021-Jun-14 at 22:22

            I grouped a dataframe test_df2 by frequency 'B' (by business day, so each name of the group is the date of that day at 00:00) and am now looping over the groups to calculate timestamp differences and save them in the dict grouped_bins. The data in the original dataframe and the groups looks like this:

            timestamp status externalId 0 2020-05-11 13:06:05.922 1 1 7 2020-05-11 13:14:29.759 10 1 8 2020-05-11 13:16:09.147 1 2 16 2020-05-11 13:19:08.641 10 2

            What I want is to calculate the difference between each row's timestamp, for example of rows 7 and 0, since they have the same externalId.

            What I did for that purpose is the following.

            ...

            ANSWER

            Answered 2021-Jun-14 at 22:22

            To convert your timestamp strings to a datetime object:

            Source https://stackoverflow.com/questions/67977606

            QUESTION

            Seaborn FacetGrid multiple page pdf plotting
            Asked 2021-Jun-14 at 17:37

            I'm trying to create a multi-page pdf using FacetGrid from this (https://seaborn.pydata.org/examples/many_facets.html). There are 20 grids images and I want to save the first 10 grids in the first page of pdf and the second 10 grids to the second page of pdf file. I got the idea of create mutipage pdf file from this (Export huge seaborn chart into pdf with multiple pages). This example works on sns.catplot() but in my case (sns.FacetGrid) the output pdf file has two pages and each page has all of the 20 grids instead of dividing 10 grids in each page.

            ...

            ANSWER

            Answered 2021-Jun-14 at 17:16

            You are missing the col_order=cols argument to the grid = sns.FacetGrid(...) call.

            Source https://stackoverflow.com/questions/67974473

            QUESTION

            How to asign Datetime values of a dataframe to the next 15min Timestep without using min/max/sum or mean?
            Asked 2021-Jun-13 at 14:55

            I've got a dataframe with power profiles. The dataframe shows start and endtime and consumed power during a transaction. It looks something like this:

            TransactionId StartTime EndTime Power xyza123 2018.01.01 07:07:34 2018.01.01 07:34:08 70 hjker383 2018.01.01 10:21:00 2018.01.01 11:40:08 23

            My Goal is to assign a new Start- and EndTime which are set at 15 min values. Like so:

            TransactionId StartTime New Starttime EndTime New EndTime Power xyza123 2018.01.01 07:07:34 2018.01.01 07:00:00 2018.01.01 07:34:08 2018.01.01 07:30:00 70 hjker383 2018.01.01 10:21:00 2018.01.01 10:30:00 2018.01.01 11:40:08 2018.01.01 11:45:00 23

            The old Timestamps can be deleted afterwards. However I don't want to aggregate them. So I guess

            df.groupby(pd.Grouper(key="StartTime", freq="15min")).sum()

            or

            df.groupby(pd.Grouper(key="StartEndtime", freq="15min")).mean()

            etc. is not an option. Another idea I had was creating a dataframe with values between 2018.01.01 00:00:00 and 2018.01.01 23:45:00. However I am not sure how to iterate true the two dataframes, to achieve my goal and if iteration true dataframes is a good idea in the first place.

            ...

            ANSWER

            Answered 2021-Apr-28 at 08:27

            You can use a function to convert a datetime to nearest 15 minute and then apply it to the column This function was inspired from this link:

            Source https://stackoverflow.com/questions/67296055

            QUESTION

            Year to Date Returns in Pandas DataFrame
            Asked 2021-Jun-12 at 14:49

            I'd like to have a running year to date pct change column in my pandas dataframe:

            Here is the dataframe:

            ...

            ANSWER

            Answered 2021-Jun-12 at 14:49

            If I understand you well, you want the running percent change with respect to the last value of the previous year. It’s maybe not the most elegant, but you can explicitly build this last-value-of-previous-year series.

            To start, you build a series with the date indices and years as values:

            Source https://stackoverflow.com/questions/67948803

            QUESTION

            count frequency of values by week using pandas then plot
            Asked 2021-Jun-12 at 00:36

            Lets say I have the following Time Series with an item:

            ...

            ANSWER

            Answered 2021-Jun-12 at 00:36

            Try groupby size on both pd.Grouper and item (use Anchored Offset to set Saturday to Saturday weekly):

            Source https://stackoverflow.com/questions/67944537

            QUESTION

            How to group the cumulative sum of rain values into a new column for given timestamps
            Asked 2021-Jun-09 at 16:49

            I have a timeseries dataframe of rain values for every given hour.

            This is the current dataframe:

            print(assomption_rain_df.head(25))

            ...

            ANSWER

            Answered 2021-Jun-09 at 16:36

            You are looking for DataFrame.rolling. It creates a rolling window of size n that you can perform operations with.

            You want

            Source https://stackoverflow.com/questions/67908246

            QUESTION

            rerunning agg on pandas groupby object modifies the original dataframe
            Asked 2021-Jun-09 at 09:46

            I am trying to aggregate a bunch of dictionaries, with string keys and lists of binary numbers as values, stored in a pandas dataframe. Like this:

            Example dataframe that this problem occurs with:

            ...

            ANSWER

            Answered 2021-Jun-09 at 09:46

            The issue is that merge_probe_trial_dicts mutates the original list that is in df4 instead of creating a new one.

            Just add .copy() as below and you should be good.

            Source https://stackoverflow.com/questions/67868452

            QUESTION

            Average of n lowest priced hourly intervals in a day pandas dataframe
            Asked 2021-Jun-08 at 17:30

            I have a dataframe that is made up of hourly electricity price data. What I am trying to do is find a way to calculate the average of the n lowest price hourly periods in day. The data spans many years and aiming to get the average of the n lowest price periods for each day. Synthetic data can be created using the following:

            ...

            ANSWER

            Answered 2021-Jun-07 at 12:43

            We can group the dataframe by Grouper object with daily frequency then aggregate Price using nsmallest to obtain the n smallest values, now calculate the mean on level=0 to get the average of n smallest values in a day

            Source https://stackoverflow.com/questions/67871597

            QUESTION

            Resampling Within a Pandas MultiIndex Loses Values
            Asked 2021-Jun-07 at 09:06

            I have some hierarchical data from 2003 to 2011 which bottoms out into time series data which looks something like this:

            ...

            ANSWER

            Answered 2021-Jun-07 at 09:06

            I have created synthetic data to test your approach and it worked fine. I then arbitrarily removed data points to see if the aggregation would fail with missing dates and it skips missing values from the time series, as displayed on the output immediately below. Therefore, I still don't understand why your output stops in 2005.

            Output without resampling and interpolation:

            Source https://stackoverflow.com/questions/67822946

            QUESTION

            Pandas: how to aggregate data weekly?
            Asked 2021-Jun-01 at 10:49

            I have a pandas dataframe the looks like the following:

            ...

            ANSWER

            Answered 2021-Jun-01 at 10:49

            Convert val to numeric first and then remove [] around 'lat', 'lon':

            Source https://stackoverflow.com/questions/67787062

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

            Vulnerabilities

            Cross-site scripting (XSS) vulnerability in UiV2Public.index in Internet2 Grouper 2.2 and 2.3 allows remote attackers to inject arbitrary web script or HTML via the code parameter.

            Install Grouper

            You can download it from GitHub.
            You can use Grouper like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            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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            https://github.com/marlinspike/Grouper.git

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            gh repo clone marlinspike/Grouper

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            git@github.com:marlinspike/Grouper.git

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