phylopandas | Pandas DataFrames for phylogenetics | Data Manipulation library

by Zsailer Python Version: 0.8.0 License: BSD-3-Clause

X-Ray Key Features Code Snippets(3)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | phylopandas Summary

phylopandas is a Python library typically used in Utilities, Data Manipulation, Pandas applications. phylopandas has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install phylopandas' or download it from GitHub, PyPI.

PhyloPandas provides a Pandas-like interface for reading sequence and phylogenetic tree data into pandas DataFrames. This enables easy manipulation of phylogenetic data using familiar Python/Pandas functions. Finally, phylogenetics for humans!.

Support

Quality

Security

License

Reuse

Support

phylopandas has a low active ecosystem.

It has 67 star(s) with 20 fork(s). There are 6 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 6 have been closed. On average issues are closed in 61 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of phylopandas is 0.8.0

Quality

phylopandas has 0 bugs and 0 code smells.

Security

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

phylopandas code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

phylopandas is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

phylopandas releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

phylopandas saves you 234 person hours of effort in developing the same functionality from scratch.

It has 572 lines of code, 34 functions and 13 files.

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

Top functions reviewed by kandi - BETA

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

Read a blast XML file
Get a random id
Create a write method to write data
Convert a pandas DataFrame to a dendropy tree
Write data to biopython fasta file
Convert pandas dataframe to biopython seqrecord
Convert Pandas Series to biopython seqrecord
Return a string suitable for writing to a file
Create a write function to write data
Create a method to read data
Convert a Dendropy tree to a pandas dataframe
Reads a Dendropy tree
Create a phylopandas dataframe
Create a read function to write data
Runs the build
Convert pandas dataframe to a phylogenetic tree
Reads the dendropy dataset

Get all kandi verified functions for this library.

phylopandas Key Features

No Key Features are available at this moment for phylopandas.

phylopandas Examples and Code Snippets

Basic Usage

Python

Lines of Code : 14

License : Permissive (BSD-3-Clause)

Copy

import phylopandas as ph

df1 = ph.read_fasta('sequences.fasta')
df2 = ph.read_phylip('sequences.phy')

df1.phylo.to_clustal('sequences.clustal')

# Read a format.
df = ph.read_fasta('sequences.fasta')

# Write to a different format.
df.phylo.to_phyl

Install

Python

Lines of Code : 4

License : Permissive (BSD-3-Clause)

Copy

pip install phylopandas

git clone https://github.com/Zsailer/phylopandas
cd phylopandas
pip install -e .

Testing

Python

Lines of Code : 2

License : Permissive (BSD-3-Clause)

Copy

$ cd phylopandas
$ pytest

Community Discussions

Trending Discussions on Data Manipulation

R: Is there a "Un-Character" Command in R?

Creating new columns based on data in row separated by specific character in R

Multiplying and Adding Values across Rows

How to make a rank column in R

How to return the column title wherein the row contains the greatest value in Pandas Dataframe

Split large csv file into multiple files based on column(s)

Get the first non-null value from selected cells in a row

pivot_longer with column pairs

Simulating Random Draws From a "Hat"

Break Apart a String into Separate Columns R

QUESTION

R: Is there a "Un-Character" Command in R?

Asked 2022-Apr-10 at 17:37

I am working with the R programming language.

I have the following dataset:

...

ANSWER

Answered 2022-Apr-10 at 05:36

Up front, "1,3,4" != 1. It seems you should look to split the strings using strsplit(., ",").

Source https://stackoverflow.com/questions/71813866

QUESTION

Creating new columns based on data in row separated by specific character in R

Asked 2022-Mar-15 at 08:48

I've the following table

Owner Pet Housing_Type A Cats;Dog;Rabbit 3 B Dog;Rabbit 2 C Cats 2 D Cats;Rabbit 3 E Cats;Fish 1

The code is as follows:

...

ANSWER

Answered 2022-Mar-15 at 08:48

One approach is to define a helper function that matches for a specific animal, then bind the columns to the original frame.

Note that some wrangling is done to get rid of whitespace to identify the unique animals to query.

Source https://stackoverflow.com/questions/71478316

QUESTION

Multiplying and Adding Values across Rows

Asked 2022-Mar-10 at 08:24

I have this data frame:

...

ANSWER

Answered 2022-Mar-10 at 04:12

We can use stri_replace_all_regex to replace your color_1 into integers together with the arithmetic operator.

Here I've stored your values into a vector color_1_convert. We can use this as the input in stri_replace_all_regex for better management of the values.

Source https://stackoverflow.com/questions/71418533

QUESTION

How to make a rank column in R

Asked 2022-Mar-07 at 16:19

I have a database with columns M1, M2 and M3. These M values correspond to the values obtained by each method. My idea is now to make a rank column for each of them. For M1 and M2, the rank will be from the highest value to the lowest value and M3 in reverse. I made the output table for you to see.

...

ANSWER

Answered 2022-Mar-07 at 14:15

Using rank and relocate:

Source https://stackoverflow.com/questions/71381995

QUESTION

How to return the column title wherein the row contains the greatest value in Pandas Dataframe

Asked 2022-Feb-24 at 20:56

I working on a Python project that has a DataFrame like this:

...

ANSWER

Answered 2022-Feb-24 at 20:48

You could use the idxmax method on axis:

Source https://stackoverflow.com/questions/71258033

QUESTION

Split large csv file into multiple files based on column(s)

Asked 2022-Feb-07 at 12:49

I would like to know of a fast/efficient way in any program (awk/perl/python) to split a csv file (say 10k columns) into multiple small files each containing 2 columns. I would be doing this on a unix machine.

...

ANSWER

Answered 2021-Dec-12 at 05:22

With your show samples, attempts; please try following awk code. Since you are opening files all together it may fail with infamous "too many files opened error" So to avoid that have all values into an array and in END block of this awk code print them one by one and I am closing them ASAP all contents are getting printed to output file.

Source https://stackoverflow.com/questions/70320648

QUESTION

Get the first non-null value from selected cells in a row

Asked 2022-Feb-04 at 09:55

Good afternoon, friends!

I'm currently performing some calculations in R (df is displayed below). My goal is to display in a new column the first non-null value from selected cells for each row.

My df is:

...

ANSWER

Answered 2022-Feb-03 at 11:16

One option with dplyr could be:

Source https://stackoverflow.com/questions/70970158

QUESTION

pivot_longer with column pairs

Asked 2022-Feb-03 at 14:02

I am again struggling with transforming a wide df into a long one using pivot_longer The data frame is a result of power analysis for different effect sizes and sample sizes, this is how the original df looks like:

...

ANSWER

Answered 2022-Feb-03 at 10:59

library(tidyverse)

example %>% 
  pivot_longer(cols = starts_with("es"), names_to = "type", names_prefix = "es_", values_to = "es") %>%
  pivot_longer(cols = starts_with("pwr"), names_to = "pwr", names_prefix = "pwr_") %>% 
  filter(substr(type, 1, 3) == substr(pwr, 1, 3)) %>% 
  mutate(pwr = parse_number(pwr)) %>% 
  arrange(pwr, es, type)

Source https://stackoverflow.com/questions/70969176

QUESTION

Simulating Random Draws From a "Hat"

Asked 2021-Dec-28 at 21:50

Suppose I have the following 10 variables (num_var_1, num_var_2, num_var_3, num_var_4, num_var_5, factor_var_1, factor_var_2, factor_var_3, factor_var_4, factor_var_5):

...

ANSWER

Answered 2021-Dec-26 at 10:11

You may define a function FUN(n) that creates a data set as shown in OP.

Source https://stackoverflow.com/questions/70483731

QUESTION

Break Apart a String into Separate Columns R

Asked 2021-Dec-17 at 20:39

I am trying to tidy up some data that is all contained in 1 column called "game_info" as a string. This data contains college basketball upcoming game data, with the Date, Time, Team IDs, Team Names, etc. Ideally each one of those would be their own column. I have tried separating with a space delimiter, but that has not worked well since there are teams such as "Duke" with 1 part to their name, and teams with 2 to 3 parts to their name (Michigan State, South Dakota State, etc). There also teams with "-" dashes in their name.

Here is my data:

...

ANSWER

Answered 2021-Dec-16 at 15:25

Here's one with regex. See regex101 link for the regex explanations

Source https://stackoverflow.com/questions/70381064

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

Vulnerabilities

No vulnerabilities reported

Install phylopandas

You can install using 'pip install phylopandas' or download it from GitHub, PyPI.
You can use phylopandas 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.