pd | Placement driver for TiKV

by tikv Go Version: v6.5.3 License: Apache-2.0

X-Ray Key Features Code Snippets(1)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | pd Summary

pd is a Go library. pd has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

PD is the abbreviation for Placement Driver. It is used to manage and schedule the TiKV cluster. PD supports distribution and fault-tolerance by embedding etcd. If you're interested in contributing to PD, see CONTRIBUTING.md. For more contributing information, please click on the contributor icon above.

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pd has a medium active ecosystem.

It has 969 star(s) with 690 fork(s). There are 93 watchers for this library.

It had no major release in the last 12 months.

There are 386 open issues and 1504 have been closed. On average issues are closed in 49 days. There are 79 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pd is v6.5.3

Quality

pd has 0 bugs and 0 code smells.

Security

pd has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pd code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pd 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.

Reuse

pd releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 91352 lines of code, 5251 functions and 501 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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pd Key Features

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pd Examples and Code Snippets

Returns True if the operator_a and operator_a == True otherwise returns False .

python

Lines of Code : 11

License : Non-SPDX (Apache License 2.0)

Copy

def combined_commuting_positive_definite_hint(operator_a, operator_b):
  """Get combined PD hint for compositions."""
  # pylint:disable=g-bool-id-comparison
  if (operator_a.is_positive_definite is True and
      operator_a.is_self_adjoint is True a

Community Discussions

Trending Discussions on pd

Good alternative to Pandas .append() method, now that it is being deprecated?

How to preform loc with one condition that include two columns

Mapping complex JSON to Pandas Dataframe

AttributeError: Can't get attribute 'new_block' on

Counting all combinations of values in multiple columns

Merge two pandas DataFrame based on partial match

If condition with a dataframe

Create a new column in a Pandas DataFrame from existing column names

How do I melt a pandas dataframe?

Convert subset of columns to rows by combining columns

QUESTION

Good alternative to Pandas .append() method, now that it is being deprecated?

Asked 2022-Mar-28 at 02:38

I use the following method a lot to append a single row to a dataframe. One thing I really like about it is that it allows you to append a simple dict object. For example:

...

ANSWER

Answered 2022-Jan-24 at 16:57

Create a list with your dictionaries, if they are needed, and then create a new dataframe with df = pd.DataFrame.from_records(your_list). List's "append" method are very efficient and won't be ever deprecated. Dataframes on the other hand, frequently have to be recreated and all data copied over on appends, due to their design - that is why they deprecated the method

Source https://stackoverflow.com/questions/70837397

QUESTION

How to preform loc with one condition that include two columns

Asked 2022-Mar-21 at 16:03

Hello guys I need your help.

I have df with two columns A and B both of them are columns with string values

example:

...

ANSWER

Answered 2022-Mar-21 at 15:58

You can try:

Source https://stackoverflow.com/questions/71560036

QUESTION

Mapping complex JSON to Pandas Dataframe

Asked 2022-Feb-25 at 13:57

Background
I have a complex nested JSON object, which I am trying to unpack into a pandas df in a very specific way.

JSON Object
this is an extract, containing randomized data of the JSON object, which shows examples of the hierarchy (inc. children) for 1x family (i.e. 'Falconer Family'), however there is 100s of them in total and this extract just has 1x family, however the full JSON object has multiple -

...

ANSWER

Answered 2022-Feb-16 at 06:41

I think this gets you pretty close; might just need to adjust the various name columns and drop the extra data (I kept the grouping column).

The main idea is to recursively use pd.json_normalize with pd.concat for all availalable children levels.

EDIT: Put everything into a single function and added section to collapse the name columns like the expected output.

Source https://stackoverflow.com/questions/71104848

QUESTION

AttributeError: Can't get attribute 'new_block' on

Asked 2022-Feb-25 at 13:18

I was using pyspark on AWS EMR (4 r5.xlarge as 4 workers, each has one executor and 4 cores), and I got AttributeError: Can't get attribute 'new_block' on . Below is a snippet of the code that threw this error:

...

ANSWER

Answered 2021-Aug-26 at 14:53

I had the same error using pandas 1.3.2 in the server while 1.2 in my client. Downgrading pandas to 1.2 solved the problem.

Source https://stackoverflow.com/questions/68625748

QUESTION

Counting all combinations of values in multiple columns

Asked 2022-Feb-21 at 15:40

The following is an example of items rated by 1,2 or 3 stars. I am trying to count all combinations of item ratings (stars) per month.


In the following example, item 10 was rated in month 1 and has two ratings equal 1, one rating equal 2 and one rating equal 3.
 ...

ANSWER

Answered 2022-Feb-20 at 22:37

Series.value_counts + Series.unstack to convert to dataframe

Source https://stackoverflow.com/questions/71198478

QUESTION

Merge two pandas DataFrame based on partial match

Asked 2022-Jan-06 at 00:54

Two DataFrames have city names that are not formatted the same way. I'd like to do a Left-outer join and pull geo field for all partial string matches between the field City in both DataFrames.

...

ANSWER

Answered 2021-Sep-12 at 20:24

This should do the job. String match with Levenshtein_distance.


pip install thefuzz[speedup]

Source https://stackoverflow.com/questions/69125666

QUESTION

If condition with a dataframe

Asked 2021-Nov-21 at 02:39

I want if the conditions are true if df[df["tg"] > 10 and df[df["tg"] < 32 then multiply by five otherwise divide by two. However, I get the following error



ValueError: The truth value of a DataFrame is ambiguous. Use a.empty,
a.bool(), a.item(), a.any() or a.all().

 ...

ANSWER

Answered 2021-Nov-04 at 16:11

Use np.where:

Source https://stackoverflow.com/questions/69842280

QUESTION

Create a new column in a Pandas DataFrame from existing column names

Asked 2021-Nov-15 at 00:22

I want to deconstruct a pandas DataFrame, using column headers as a new data-column and create a list with all combinations of the row index and columns. Easier to show than explain:

...

ANSWER

Answered 2021-Nov-09 at 23:58

The structure that you want your data in is very messy, so this is probably the best method given the data you want.

Source https://stackoverflow.com/questions/69906411

QUESTION

How do I melt a pandas dataframe?

Asked 2021-Nov-04 at 09:34

On the pandas tag, I often see users asking questions about melting dataframes in pandas. I am gonna attempt a cannonical Q&A (self-answer) with this topic.


I am gonna clarify:

What is melt?

How do I use melt?

When do I use melt?


I see some hotter questions about melt, like:

pandas convert some columns into rows : This one actually could be good, but some more explanation would be better.

Pandas Melt Function : Nice question answer is good, but it's a bit too vague, not much expanation.

Melting a pandas dataframe : Also a nice answer! But it's only for that particular situation, which is pretty simple, only pd.melt(df)

Pandas dataframe use columns as rows (melt) : Very neat! But the problem is that it's only for the specific question the OP asked, which is also required to use pivot_table as well.


So I am gonna attempt a canonical Q&A for this topic.


Dataset:
I will have all my answers on this dataset of random grades for random people with random ages (easier to explain for the answers :D):
 ...

ANSWER

Answered 2021-Nov-04 at 09:34

Note for users with pandas version under <  0.20.0, I will be using df.melt(...) for my examples, but your version would be too low for df.melt, you would need to use pd.melt(df, ...) instead.
Documentation references:
Most of the solutions here would be used with melt, so to know the method melt, see the documentaion explanation

Unpivot a DataFrame from wide to long format, optionally leaving
identifiers set.
This function is useful to massage a DataFrame into a format where one
or more columns are identifier variables (id_vars), while all other
columns, considered measured variables (value_vars), are “unpivoted”
to the row axis, leaving just two non-identifier columns, ‘variable’
and ‘value’.

And the parameters are:

Parameters

id_vars : tuple, list, or ndarray, optional
Column(s) to use as identifier variables.

value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

var_name : scalar
Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.

value_name : scalar, default ‘value’
Name to use for the ‘value’ column.

col_level : int or str, optional
If columns are a MultiIndex then use this level to melt.

ignore_index : bool, default True
If True, original index is ignored. If False, the original index is retained. Index labels will be repeated
as necessary.
New in version 1.1.0.



Logic to melting:
Melting merges multiple columns and converts the dataframe from wide to long, for the solution to Problem 1 (see below), the steps are:

First we got the original dataframe.

Then the melt firstly merges the Math and English columns and makes the dataframe replicated (longer).

Then finally adds the column Subject which is the subject of the Grades columns value respectively.



This is the simple logic to what the melt function does.
Solutions:
I will solve my own questions.
Problem 1:
Problem 1 could be solve using pd.DataFrame.melt with the following code:

Source https://stackoverflow.com/questions/68961796

QUESTION

Convert subset of columns to rows by combining columns

Asked 2021-Oct-26 at 04:54

Pandas 1.1.4


MRE:
 ...

ANSWER

Answered 2021-Oct-26 at 03:13

This is essentially a reshape operation using stack

Source https://stackoverflow.com/questions/69716911

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

 Vulnerabilities
No vulnerabilities reported

 Install pd
Make sure Go (version 1.16) is installed.
Use make to install PD. PD is installed in the bin directory.

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For any new features, suggestions and bugs create an issue on  GitHub. 
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