Pyply Documentation

About Pyply

Pyply is a package designed to incorporate several R functions into Python (3.x). A few other functions are also included to help with data manipulation.

If you’re coming from R and miss having functions like sapply or lapply, this package is for you.

Installation

Pyply can be installed via pip (using all lowercase):

pip install pyply

Requirements

Pyply requires the following packages to be installed:

collections
functools
pandas

The above packages are included in a standard installation of Anaconda.

Methods

Pyply comes with one module, also named pyply.  This module contains the following methods:

flatten
ifelse
is_DataFrame
is_dict
is_float
is_int
is_list
is_str
is_tuple
lapply
mode
rapply
sapply
split
switch
unsplit

Method: flatten(LIST)

Flattens a list of lists.  Outputs the new list.

Possible Parameters

LIST

Input list of sub-lists, or other elements.  The output will flatten any sub-list within the input list.

Examples

from pyply.pyply import flatten

sample_list = [[3,4,5],[6,7,8],[9,10,'c','d']]

View Results.

Method: ifelse(EXPR, flow1, flow2)

Single-line, functional version of if / else.  Based off R’s ifelse function.

Possible Parameters

EXPR

Expression to be evaluated as True / False

flow1


If EXPR is True, then flow1 will be returned

flow2

If EXPR is False, then flow2 will be returned

Examples

from pyply.pyply import flatten

result = ifelse(2 > 1, "yes", "no")

result

View Results.

Methods: Boolean is_* functions

All of the below methods take a single parameter, x, and return a Boolean result. Each one tests whether the input is of a particular data type e.g. is_DataFrame returns True if the input, x, is a pandas data frame; is_str tests if an input is a string etc.

is_DataFrame
is_dict
is_float
is_int
is_list
is_str
is_tuple

Method: lapply(OBJECT, func, keys = None)

Based off R’s lapply function.   Applies a function to every element of a list, tuple, or dictionary.  Returns a dictionary.  Lambda functions are supported in the func parameter.

Possible Parameters

OBJECT

An object of type list, tuple, or dictionary

func


The function to be applied to each element in OBJECT

keys

The keys that the returned dictionary should have.  The default is None, meaning that the returned result will have enumerated keys (e.g. inputting a list of five elements through lapply will result in a dictionary with keys 0, 1, 2, 3, and 4).

Examples

from pyply.pyply import lapply

sample_list = [2 , 4 , 6]

lapply(sample_list , lambda x: x*2)

lapply(sample_list , lambda x: x*2, ['a','b','c'])

View Results.

Method: mode(OBJECT)

Finds the statistical mode of a list or tuple.  If there is a tie, a list is returned with each element comprising the tie.

Possible Parameters

OBJECT

A list or tuple

Examples

from pyply.pyply import mode

sample_list = [2 , 2, 4, 5, 6, 7, 2]

mode(sample_list)

second_sample = [2 , 2, 4, 5, 6, 7, 2, 7, 7]

mode(second_sample)

View Results.

Method: rapply(OBJECT, func, keys = None, _type = None)

Based off R’s rapply function.   Applies a function to every element to a list, tuple, or dictionary, provided the element has type equal to the _type parameter.

If the input parameter, OBJECT, is a list or tuple, then a list is returned.  If OBJECT is a dictionary, then a dictionary is returned.

Like lapply and sapply, lambda functions are supported in the func parameter.

Possible Parameters

OBJECT

An object of type list, tuple, or dictionary

func


The function to be applied to each element in OBJECT

keys

The keys that the returned dictionary should have.  The default is None, meaning that the returned result will have enumerated keys (e.g. inputting a list of five elements through rapply will result in a dictionary with keys 0, 1, 2, 3, and 4).

_type


The data type of the elements in OBJECT that func should be applied to (e.g. if _type = int, then func will only be applied to the integers in OBJECT).

Examples

from pyply.pyply import rapply

sample_list = [2, 4, 6, 'a', 'b', 'c']

rapply(sample_list, lambda x: x*2, _type = int)

rapply(sample_list, lambda x: x.upper(), _type = str)

View Results.

Method: sapply(OBJECT, func, return_type = list)

Based off R’s sapply function.   Applies a function to every element in a list or tuple.  Returns a list.

Like lapply and rapply, lambda functions are supported in the func parameter.

Possible Parameters

OBJECT

A list or tuple

func


The function to be applied to each element in OBJECT


return_type

Data type of the object that should be returned.  Default is list, but can also be tuple.

Examples

from pyply.pyply import sapply

sample_list = [2, 4, 6]

sapply(sample_list , lambda x: x*2)

View Results.

Method: split(df, field)

Based off R’s split function.  Splits a data frame by the different values of a field. Returns an OrderedDict (from the collections package).  The keys of the OrderedDict are the distinct values in the field paramter.  Each corresponding OrderedDict value is

Possible Parameters

df

A data frame

field


A string -- the name of the field that df should be split on

Examples

import pandas as pd
from pyply.pyply import split

df = pd.DataFrame()
df['day_of_week'] =['sunday','monday','tuesday','wednesday','thursday','friday','saturday']
df['num'] = [1,2,3,4,5,6,7]

df_after_split = split(df , "day_of_week")

df_after_split['friday']

View Results.

Method: switch(EXPR , *args,**kwargs)

Based off the switch function from R and other languages.  Returns a selection from a list of choices.

Possible Parameters

EXPR

This can be an int or a string

*args


A list or tuple

**kwargs

Additional keyword parameters as needed.  See examples below

Examples

from pyply.pyply import switch, mode
import numpy as np

'''simple example '''
switch(2 , "a", "b", "c", "d")

'''Example based of R's documentation for swtich '''
med = switch("median" , mean = np.mean, median = np.median, mode = mode)
med([1,2,3,4,5])

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Method: unsplit(df_dict)

Based off R’s unsplit function; tries to reverse the split function by taking a dict (or OrderedDict) of data frames, and returning the result of stacking them together.

Possible Parameters

df_dict

A dictionary or OrderedDict with data frames as values

Examples

'''Continuing the "split" example from above'''
from pyply.pyply import unsplit
unsplit(df_after_split)

View Results.

To learn more about Python, see some book suggestions here.