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"description": "## pynto: Time series analysis in Python using the concatenative paradigm\n\npynto is a Python package that lets you manipulate tabular data with the expressiveness and code reusability of [concatenative](https://en.wikipedia.org/wiki/Concatenative_programming_language) programming. With pynto you define an _expression_ that formally specifies how to calculate the data in your table. Expressions are made by chaining together functions called _words_. It works like a pipeline: the output from one word becomes the input for the following word. The table of data is treated like a stack of independent columns. The rightmost column in the table is the top of the stack. Words can add, remove or modify columns, but they are row-agnostic--expressions can be evaluated over any range of rows. \n## What does it look like?\n```\n>>> import pynto as pt \n>>> ma_dev = pt.saved('stock_prices') \\ # define an expression that appends columns from the build-in database\n>>> .quote(pt.dup.rolling(20).mean.sub) \\ # append a quoted expression to the stack\n>>> .each # use the each combinator to apply the quotation to the previous columns\n>>> df = ma_dev['2021-06-01':] # evaluate your expression over a date range to get a DataFrame\n>>> pt.db['stocks_ma_dev'] = df # save the results back to the database \n```\n\n## Why pynto?\n - Expressive: Pythonic syntax; Combinatory logic for modular, reusable code \n - Performant: Column-wise multiprocessing; caching of duplicate operations\n - Batteries included: Built-in Redis-based time series database\n - Interoperable: Seemlessly integration with Pandas\n\n## Get pynto\n```\npip install pynto\n```\n## Reference\n\n### The Basics\n\nCreate expressions by chaining built-in words together using a fluent interface, or using the `+` operator for words in the local namespace. When evaluated, words will operate in left-to-right order, with operators following their operands in _postfix_ style. \n```\n>>> square = pt.dup.mul # duplicates the top column, then multiplies top two columns together\n```\nThe word `c` that adds a constant-value column to the stack. Like many pynto words, `c` takes a _parameter_ in parentheses to specify the constant value `c(10.0)`. pynto can handle any NumPy data type, but all rows in a column have to have the same type.\n\n```\n>>> expr = pt.c(10.0) + square # compose square expression with a columns of 10s\n```\nTo evaluate your expression, specify a date range using the `[`_start_`:`_stop (exclusive)_`:`_periodicity_`]` syntax. (Instances of `pt.Range` in the local namespace also work.)\n```\n>>> expr['2021-06-01':'2021-06-03','B'] # evaluate over a two business day date range \n constant\n2021-06-01 100.0\n2021-06-02 100.0\n```\nEach column has a string header that can be modified. `hset` sets the header to a new value. Headers can be usefully for filtering or arranging columns.\n```\n>>> expr += pt.hset('ten squared')\n>>> expr['2021-06-01':'2021-06-03','B'] \n ten squared\n2021-06-01 100.0\n2021-06-02 100.0\n```\n_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 create a quotation put an expression within the parentheses of `pt.quote()`.\n```\n>>> expr = pt.c(9.).c(10.).quote(square).each\n>>> expr['2021-06-01':'2021-06-02']\n constant constant\n2021-06-01 81.0 100.0\n```\n\n### The Database\n\npynto has built-in time series database functionality that lets you save pandas DataFrames and Series to a Redis database. The database uses only String key/values, saving 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 on its own. The database also supports three-dimensional frames that have a two-level MultiIndex. Saved data can be accessed through pynto expressions or standard methods\n\n```\n>>> pt.db['my_df'] = expr['2021-06-01':'2021-06-03']\n>>> pt.saved('my_df')['2021-06-01':'2021-06-02']\n constant constant\n2021-06-01 81.0 100.0\n>>> pt.db.read('my_df')\n constant constant\n2021-06-01 81.0 100.0\n2021-06-02 81.0 100.0\n```\n\n## pynto built-in vocabulary\n\n### Words for adding columns\nName | Parameters |Stack effect
_before_ -- _after_|Description\n:---|:---|:---|:---\nc|value| -- c|Adds a constant-_value_ column.\ncsv|csv_file, index_col=0, header='infer'| -- c (c)|Adds columns from _csv_file_.\npandas|frame_or_series| -- c (c)|Adds columns from a pandas data structure.\nc_range|value| -- c (c)|Add constant int columns from 0 to _value_.\n\n### Combinators\nName | Parameters |Stack effect
_before_ -- _after_|Description\n:---|:---|:---|:---\ncall|depth=None, copy=False| a q -- c| Apply quotation to stack, up to _depth_ if specified. Optionally leaves stack in place with _copy_.\neach|start=0, stop=None, every=1, copy=False| a b q -- c d| Apply quotation stack elements from _start_ to _end_ in groups of _every_. Optionally leaves stack in place with _copy_.\nrepeat|times| a b q -- c d| Apply quotation to the stack _times_ times.\ncleave|num_quotations, depth=None, copy=False| a q q -- c d| Apply _num_quotations_ quotations to copies of stack elements up to _depth_. Optionally leaves stack in place with _copy_.\n\n### Words to manipulate columns\nName | Parameters |Stack effect
_before_ -- _after_|Description\n:---|:---|:---|:---\ndup|| a -- a a| Duplicate top column.\nroll|| a b c -- c a b| Permute columns.\nswap|| a b -- b a| Swap top two columns.\ndrop|| a b c -- a b| Drop top column.\nclear|| a b c -- | Clear columns.\ninterleave|count=None, split_into=2|a b c d -- a c b d|Divide columns into _split into_ groups and interleave group elements.\npull|start,end=None,clear=False|a b c -- b c a|Bring columns _start_ (to _end_) to the top.\nhpull|\\*headers, clear=False|a b c -- b c a|Bring columns with headers matching regex _headers_ to the top. Optionally clear remainder of stack\nhfilter|\\*headers, clear=False|a b c -- a|Shortcut for hpull with _clear_=True\n\n### Words to manipulate headers\nName | Parameters |Stack effect
_before_ -- _after_|Description\n:---|:---|:---|:---\nhset|\\*headers| a b -- a b|Set top columns' headers to _headers_.\nhformat|format_string| a -- a|Apply _format_string_ to existing headers.\nhapply|header_function| a -- a|Apply _header_function_ to existing header.\n\n### Words for arithmetic or logical operators\nName | Parameters |Stack effect
_before_ -- _after_|Description\n:---|:---|:---|:---\nadd||a b -- c|a + b\nsub||a b -- c|a - b\nmul||a b -- c|a * b\ndiv||a b -- c|a / b\nmod||a b -- c|a % b\npow||a b -- c|a ** b\neq||a b -- c|a == b\nne||a b -- c|a != b\nge||a b -- c|a >= b\ngt||a b -- c|a > b\nle||a b -- c|a <= b\nlt||a b -- c|a < b\nneg||a -- c|a * -1\nabs||a -- c|abs(a)\nsqrt||a -- c|a ** 0.5\nzeroToNa|| a -- c|Replaces zeros with np.nan\n\n### Words for creating window columns\nName | Parameters |Stack effect
_before_ -- _after_|Description\n:---|:---|:---|:---\nrolling|window=2, exclude_nans=True, lookback_multiplier=2|a -- w|Create window column with values from most recent _window_ rows. Exclude nan-valued rows from count unless _exclude_nans_. Extend history up to _lookback_multiplier_ to look for non-nan rows. \ncrossing||a b c -- w|Create window column with cross-sectional values from the same rows of all columns. \n\n### Words for calculating statistics on window columns\nName | Parameters |Stack effect
_before_ -- _after_|Description\n:---|:---|:---|:---\nsum||w -- c|Sums of windows.\nmean||w -- c|Means of windows.\nvar||w -- c|Variances of windows.\nstd||w -- c|Standard deviations of windows.\nchange||w -- c|Changes between first and last rows of windows.\npct_change||w -- c|Percent changes between first and last rows of windows.\nlog_change||w -- c|Differences of logs of first and last rows of windows.\nfirst||w -- c|First rows of windows.\nlast||w -- c|Last rows of windows.\nzscore||w -- c|Z-score of most recent rows within windows.\n\n### Words for cleaning up data\nName | Parameters |Stack effect
_before_ -- _after_|Description\n:---|:---|:---|:---\nfill|value|a -- a|Fill nans with _value_.\nffill||a -- a|Last observation carry-forward.\njoin|date|a b -- c|Join top two columns, switching from second to first on _date_ index.\n\n### Other words\nName | Parameters |Stack effect
_before_ -- _after_|Description\n:---|:---|:---|:---\newma|window, fill_nans=True|a -- c|Calculates exponentially-weighted moving average with half-life _window_. \nwlag|number|w -- c|Lag _number_ rows.",
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