Metadata-Version: 2.4
Name: pynto
Version: 2.4.1
Summary: Data analysis using a concatenative paradigm
Author-email: Peter Graf <peter@pynto.tech>
License-Expression: MIT
Project-URL: Homepage, https://github.com/punkbrwstr/pynto
Project-URL: Repository, https://github.com/punkbrwstr/pynto
Keywords: data analysis,quantitative,tabular,concatenative,functional
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3.12
Classifier: Operating System :: OS Independent
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: python-dateutil
Requires-Dist: bottleneck
Requires-Dist: redis
Requires-Dist: hiredis
Dynamic: license-file

![pynto logo](resources/pynto.png)

## pynto: Data analysis in Python using stack-based programming

pynto is a Python package that lets you manipulate a data frame as a stack of columns, using the the expressiveness of the [concatenative](https://en.wikipedia.org/wiki/Concatenative_programming_language)/[stack-oriented](https://en.wikipedia.org/wiki/Stack-oriented_programming) paradigm.  

## How does it work?

With pynto you chain together functions called _words_ to formally specify how to calculate each column of your data frame.  The composed _words_ can be lazily evaluated over any range of rows to create your data frame.

_Words_ add, remove or modify columns.  They can operate on the entire stack or be limited to a certain columns using a _column indexer_.  Composed _words_ will operate in left-to-right order, with operators following their operands in _postfix_ (Reverse Polish Notation) style.  More complex operations can be specified using _quotations_, anonymous blocks of _words_ that do not operate immediately, and _combinators_, higher-order words that control the execution of _quotations_.


## What does it look like?
Here's a program to calculate deviations from moving average for each column in a table using the _combinator_/_quotation_ pattern.
```
>>> import pynto as pt 
>>> ma_dev = (                        # create a pynto expression by concatenating words to
>>>     pt.load('stock_prices')      # append columns to stack from the build-in database
>>>     .q                            # start a quotation 
>>>         .dup                      # push a copy of the top (leftmost) column of the stack
>>>         .ravg(20)                 # calculate 20-period moving average
>>>         .sub                      # subtract top column from second column 
>>>     .p                            # close the quotation
>>>     .map                          # use the map combinator to apply the quotation
>>> )                                 # to each column in the stack
>>>
>>> df = ma_dev.rows['2021-06-01':]         # evaluate over a range of rows to get a DataFrame
>>> pt.db['stocks_ma_dev'] = df             # save the results back to the database   
```

## Why pynto?
 - Expressive: Pythonic syntax; Combinatory logic for modular, reusable code 
 - Performant: Memoization to eliminate duplicate operations
 - Batteries included:  Built-in time series database
 - Interoperable: Seemlessly integration with Pandas/numpy

## Get pynto
```
pip install pynto
```

## Reference

### The Basics

## Constant literals
Add constant-value columns to the stack using literals that start with `c`, followed by a number with `-` and `.` characters replaced by `_`.  `r`_n_ adds whole number-value constant columns up to _n - 1_.
```
>>> # Compose _words_ that add a column of 10s to the stack, duplicate the column, 
>>> # and then multiply the columns together
>>> ten_squared = pt.c10_0.dup.mul         
```

## Row indexers
To evaluate your expression, you use a row indexer.  Specify rows by date range using the `.rows[`_start_`:`_stop (exclusive)_`:`_periodicity_`]` syntax. None slicing arguments default to the widest range available.  _int_ indices also work with the `.rows` indexer. `.first`, and `.last` are included for convenience.
```
>>> ten_squared.rows['2021-06-01':'2021-06-03','B']                   # evaluate over a two business day date range                                                   
                 c
2021-06-01     100.0
2021-06-02     100.0
```

## Quotations and Combinators
Combinators are higher-order functions that allow pynto to do more complicated things like branching and looping.  Combinators operate on quotations, expressions that are pushed to the stack instead of operating on the stack.  To push a quotation to the stack, put words in between `q` and `p` (or put an expression in the local namespace within the parentheses of `pt.q(_expression_)`).  THe `map` combinator evaluated a quotation at the top of the stack over each column below in the stack.
```
>>> pt.c9.c10.q.dup.mul.p.map.last
                 c         c
2021-06-02      81.0     100.0
```

## Headers
Each column has a string header.  `hset` sets the header to a new value.  Headers are useful for filtering or arranging columns.
```
>>> pt.c9.c10.q.dup.mul.p.map.hset('a','b').last
                 a         b
2021-06-02      81.0     100.0
```

## Column indexers
Column indexers specify the columns on which a _word_ operates, overiding the _word's_ default.  Postive _int_ indices start from the bottom (left) of the stack and negative indices start from the top.

By default `add` has a column indexer of [-2:]
```
>>> pt.r5.add.last
              c    c    c    c
2021-06-02  0.0  1.0  2.0  7.0
```
Change the column indexer of `add` to [:] to sum all columns
```
>>> pt.r5.add[:].last
               c
2025-06-02  10.0
```
You can also index columns by header, using regular expressions
```
>>> pt.r3.hset('a,b,c').add['(a|c)'].last
              b    a
2025-06-02  1.0  2.0
```

## Defining words
_Words_ in the local namespace can be composed using the  `+` operator.  
```
>>> squared = pt.dup.mul
>>> ten_squared2 = pt.c10_0 + squared    # same thing
```

_Words_ can also be defined globally in the pynto vocabulary.
```
>>> pt.define['squared'] = pt.dup.mul
>>> ten_squared3 = pt.c10_0.squared    # same thing
```


### The Database

pynto has built-in database functionality that lets you save DataFrames and Series to a Redis database.  The database saves the underlying numpy data in native byte format for zero-copy retrieval.   Each DataFrame column is saved as an independent key and can be retrieved or updated on its own.  The database also supports three-dimensional frames that have a two-level MultiIndex.

```
>>> pt.db['my_df'] = expr.rows['2021-06-01':'2021-06-03']
>>> pt.load('my_df').rows[:]
              constant  constant
2021-06-01      81.0     100.0
2021-06-02      81.0     100.0
```

## pynto built-in vocabulary



### Column Creation

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
c|[-1:]|_values_|Pushes constant columns for each of _values_
day_count|[-1:]||Pushes a column with the number of days in the period
from_pandas|[:]|_pandas_, _round__|Pushes columns from Pandas DataFrame or Series _pandas_
load|[-1:]||Pushes columns of a DataFrame saved to internal DB as _key_
nan|[-1:]|_values_|Pushes a constant nan-valued column
period_ordinal|[-1:]||Pushes a column with the period ordinal
r|[-1:]|_n_|Pushes constant columns for each whole number from 0 to _n_ - 1
randn|[-1:]||Pushes a column with values from a random normal distribution
timestamp|[-1:]||Pushes a column with the timestamp of the end of the period

### Stack Manipulation

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
drop|[-1:]||Removes selected columns
dup|[-1:]||Duplicates columns
hsort|[:]||Sorts columns by header
id|[:]||Identity/no-op
interleave|[:]|_parts_|Divides columns in _parts_ groups and interleaves the groups
keep|[:]||Removes non-selected columns
nip|[-1:]||Removes non-selected columns, defaulting selection to top
pull|[:]||Brings selected columns to the top
rev|[:]||Reverses the order of selected columns
roll|[:]||Permutes selected columns
swap|[-2:]||Swaps top and bottom selected columns

### Quotation

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
q|[-1:]|_quoted_, _this_|Wraps the following words until *p* as a quotation, or wraps _quoted_ expression as a quotation

### Header manipulation

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
halpha|[:]||Set headers to alphabetical values
happly|[:]|_header_func_|Apply _header_func_ to headers_
hformat|[:]|_format_spec_|Apply _format_spec_ to headers
hreplace|[:]|_old_, _new_|Replace _old_ with _new_ in headers
hset|[:]|_headers_|Set headers to _*headers_ 
hsetall|[:]|_headers_|Set headers to _*headers_ repeating, if necessary

### Combinators

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
call|[:]||Applies quotation
cleave|[:]|_num_quotations_|Applies all preceding quotations
compose|[:]|_num_quotations_|Combines quotations
hmap|[:]||Applies quotation to stacks created grouping columns by header
ifexists|[:]|_count_|Applies quotation if stack has at least _count_ columns
ifexistselse|[:]|_count_|Applies top quotation if stack has at least _count_ columns, otherwise applies second quotation
ifheaders|[:]|_predicate_|Applies top quotation if list of column headers fulfills _predicate_
ifheaderselse|[:]|_predicate_|Applies quotation if list of column headers fulfills _predicate_, otherwise applies second quotation
map|[:]|_every_|Applies quotation in groups of _every_
partial|[-1:]|_quoted_, _this_|Pushes stack columns to the front of quotation
repeat|[:]|_times_|Applies quotation _times_ times

### Data cleanup

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
ffill|[:]|_lookback_, _leave_end_|Fills nans with previous values, looking back _lookback_ before range and leaving trailing nans unless not _leave_end_
fill|[:]||Fills nans with _value_ 
fillfirst|[-1:]|_lookback_|Fills first row with previous non-nan value, looking back _lookback_  before range
join|[-2:]|_date_|Joins two columns at _date_
sync|[:]||Align available data by setting all values to NaN when any values is NaN
zero_first|[-1:]||Changes first value to zero
zero_to_na|[-1:]||Changes zeros to nans

### Resample methods

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
resample_avg|[:]||Sets periodicity resampling method to avg
resample_first|[:]||Sets periodicity resampling method to first
resample_firstnofill|[:]||Sets periodicity resampling method to first
resample_last|[:]||Sets periodicity resampling method to last
resample_lastnofill|[:]||Sets periodicity resampling method to last with no fill
resample_max|[:]||Sets periodicity resampling method to max
resample_min|[:]||Sets periodicity resampling method to min
resample_sum|[:]||Sets periodicity resampling method to sum
set_periodicity|[-1:]|_periodicity_|Changes column periodicity to _periodicity_, then resamples
set_start|[-1:]|_start_|Changes period start to _start_, then resamples

### Row-wise Reduction

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
add|[-2:]|_ignore_nans_|Addition
avg|[-2:]|_ignore_nans_|Arithmetic average
div|[-2:]|_ignore_nans_|Division
max|[-2:]|_ignore_nans_|Maximum
med|[-2:]|_ignore_nans_|Median
min|[-2:]|_ignore_nans_|Minimum
mod|[-2:]|_ignore_nans_|Modulo
mul|[-2:]|_ignore_nans_|Multiplication
pow|[-2:]|_ignore_nans_|Power
std|[-2:]|_ignore_nans_|Standard deviation
sub|[-2:]|_ignore_nans_|Subtraction
var|[-2:]|_ignore_nans_|Variance

### Row-wise Reduction Ignoring NaNs

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
nadd|[-2:]|_ignore_nans_|Addition
navg|[-2:]|_ignore_nans_|Arithmetic average
ndiv|[-2:]|_ignore_nans_|Division
nmax|[-2:]|_ignore_nans_|Maximum
nmed|[-2:]|_ignore_nans_|Median
nmin|[-2:]|_ignore_nans_|Minimum
nmod|[-2:]|_ignore_nans_|Modulo
nmul|[-2:]|_ignore_nans_|Multiplication
npow|[-2:]|_ignore_nans_|Power
nstd|[-2:]|_ignore_nans_|Standard deviation
nsub|[-2:]|_ignore_nans_|Subtraction
nvar|[-2:]|_ignore_nans_|Variance

### Rolling Window

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
ewm_mean|[-1:]|_window_|Exponentially-weighted moving average
ewm_std|[-1:]|_window_|Exponentially-weighted standard deviation
ewm_var|[-1:]|_window_|Exponentially-weighted variance
radd|[-1:]|_window_|Addition
ravg|[-1:]|_window_|Arithmetic average
rcor|[-2:]|_window_|Correlation
rcov|[-2:]|_window_|Covariance
rdif|[-1:]|_window_|Lagged difference
rlag|[-1:]|_window_|Lag
rmax|[-1:]|_window_|Maximum
rmed|[-1:]|_window_|Median
rmin|[-1:]|_window_|Minimum
rret|[-1:]|_window_|Lagged return
rstd|[-1:]|_window_|Standard deviation
rvar|[-1:]|_window_|Variance
rzsc|[-1:]|_window_|Z-score

### Cumulative

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
cadd|[-1:]||Addition
cavg|[-1:]||Arithmetic average
cdif|[-1:]||Lagged difference
clag|[-1:]||Lag
cmax|[-1:]||Maximum
cmin|[-1:]||Minimum
cmul|[-1:]||Multiplication
cret|[-1:]||Lagged return
cstd|[-1:]||Standard deviation
csub|[-1:]||Subtraction
cvar|[-1:]||Variance

### Reverse Cumulative

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
rcadd|[-1:]||Addition
rcavg|[-1:]||Arithmetic average
rcdif|[-1:]||Lagged difference
rclag|[-1:]||Lag
rcmax|[-1:]||Maximum
rcmin|[-1:]||Minimum
rcmul|[-1:]||Multiplication
rcret|[-1:]||Lagged return
rcstd|[-1:]||Standard deviation
rcsub|[-1:]||Subtraction
rcvar|[-1:]||Variance

### One-for-one functions

Word | Default Selector | Parameters | Description
:---|:---|:---|:---
abs|[-1:]||Absolute value
dec|[-1:]||Decrement
exp|[-1:]||Exponential
expm1|[-1:]||Exponential minus one
inc|[-1:]||Increment
inv|[-1:]||Multiplicative inverse
lnot|[-1:]||Logical not
log|[-1:]||Natural log
log1p|[-1:]||Natural log of increment
neg|[-1:]||Additive inverse
rank|[:]||Row-wise rank
sign|[-1:]||Sign
sqrt|[-1:]||Square root

