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merge.py
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merge.py
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "merge.py",
"provenance": [],
"authorship_tag": "ABX9TyMdJQnm+IcCAJueWN4I1whR",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/qsdcfd/TIL/blob/TIL/Study__245/merge.py\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "t1xXdmEQTR-p"
},
"outputs": [],
"source": [
"#직접 오픈 소스 코드가 있는 깃허브를 찾아서 필사했습니다.\n",
"\n",
"\"\"\"\n",
"SQL-style merge routines\n",
"\"\"\"\n",
"\n",
"from __future__ import annotations # from __future__ import 가 표시 될 때마다 최신 또는 예정된 Python 릴리스의 기능이 이전 버전으로 백 포트되었음을 의미\n",
"\n",
"import copy #레퍼런스 대입 시의 문제를 해결하기 위해서 copy모듈 사용합니다\n",
"import datetime #시간 모듈\n",
"from functools import partial #하나 이상의 인수가 이미 채워진 함수의 새 버전 사용, 함수의 새 버전은 그 자체를 기술\n",
"\"\"\"\n",
"지수 함수를 만든다고 가정\n",
"\n",
"아래 코드가 좋아보이긴 하지만 숫자가 많아지면 가독성이 떨어지게 될 것입니다.\n",
"\n",
"def power(base, exponent):\n",
"\treturn base ** exponent\n",
"\n",
"def square(base):\n",
"\treturn power(base,2)\n",
"\n",
"def cub(base):\n",
"\treturn power(base,3)\n",
"\n",
"from functools import partial\n",
"square = partial(power, exponent = 2)\n",
"cube = parial(power, exponent =3)\n",
"\n",
"def test_partials():\n",
"\tassert square(2) == 4\n",
" assert cube(2) == 8\n",
"\n",
"# 부분함수 톺아보기\n",
"\n",
"def test_partial_docs():\n",
"\tassert square.keywords == {'exponent':2}\n",
" assert square.func == power\n",
" \n",
" assert cube.keywords == {'exponent':3}\n",
" assert cube.func == power\n",
"\"\"\"\n",
"\n",
"import hashlib # 값이 거짓이면 제한된 환경에서 안전하지 않고 차단된 해싱 알고리즘 사용을 허락\n",
"import string #문자열에 값을 치환\n",
"from typing import(\n",
"\tTYPE_CHECKING,\n",
" Hashable,\n",
" cast\n",
") #형 힌트 지원\n",
"\n"
]
},
{
"cell_type": "code",
"source": [
"import warnings #경고 메세지 삭제\n",
"\n",
"import numpy as np\n",
"\n",
"from pandas._libs import (\n",
" Timedelta,\n",
" hashtable as libhashtable,\n",
" join as libjoin,\n",
" lib,\n",
")\n",
"from pandas._typing import (\n",
" ArrayLike,\n",
" DtypeObj,\n",
" IndexLabel,\n",
" Suffixes,\n",
" npt,\n",
")\n",
"\n",
"from pandas.errors import MergeError\n",
"from pandas.util._decorators import (\n",
" Appender,\n",
" Substitution,\n",
")\n"
],
"metadata": {
"id": "IEMw7CJXYD__"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from pandas.util._exceptions import find_stack_level\n",
"\n",
"from pandas.core.dtypes.cast import find_common_type\n",
"from pandas.core.dtypes.common import (\n",
" ensure_float64,\n",
" ensure_int64,\n",
" ensure_object,\n",
" is_array_like,\n",
" is_bool,\n",
" is_bool_dtype,\n",
" is_categorical_dtype,\n",
" is_datetime64tz_dtype,\n",
" is_dtype_equal,\n",
" is_extension_array_dtype,\n",
" is_float_dtype,\n",
" is_integer,\n",
" is_integer_dtype,\n",
" is_list_like,\n",
" is_number,\n",
" is_numeric_dtype,\n",
" is_object_dtype,\n",
" needs_i8_conversion,\n",
")"
],
"metadata": {
"id": "ZgD6mH2KVCqz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from pandas.core.dtypes.missing import (\n",
" isna,\n",
" na_value_for_dtype,\n",
")\n",
"\n",
"from pandas import (\n",
" Categorical,\n",
" Index,\n",
" MultiIndex,\n",
" Series,\n",
")\n",
"from pandas.core import groupby\n",
"import pandas.core.algorithms as algos\n",
"from pandas.core.arrays import ExtensionArray\n",
"from pandas.core.arrays._mixins import NDArrayBackedExtensionArray\n",
"import pandas.core.common as com\n",
"from pandas.core.construction import extract_array\n",
"from pandas.core.frame import _merge_doc\n",
"from pandas.core.internals import concatenate_managers\n",
"from pandas.core.sorting import is_int64_overflow_possible\n",
"\n",
"if TYPE_CHECKING:\n",
" from pandas import DataFrame\n",
" from pandas.core.arrays import DatetimeArray\n"
],
"metadata": {
"id": "4EbF1ZWnYWug"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"@Substitution(\"\\nleft : DataFrame or named Series\")\n",
"@Appender(_merge_doc, indents=0)\n",
"def merge(\n",
" left: DataFrame | Series,\n",
" right: DataFrame | Series,\n",
" how: str = \"inner\",\n",
" on: IndexLabel | None = None,\n",
" left_on: IndexLabel | None = None,\n",
" right_on: IndexLabel | None = None,\n",
" left_index: bool = False,\n",
" right_index: bool = False,\n",
" sort: bool = False,\n",
" suffixes: Suffixes = (\"_x\", \"_y\"),\n",
" copy: bool = True,\n",
" indicator: bool = False,\n",
" validate: str | None = None,\n",
") -> DataFrame:\n",
" op = _MergeOperation(\n",
" left,\n",
" right,\n",
" how=how,\n",
" on=on,\n",
" left_on=left_on,\n",
" right_on=right_on,\n",
" left_index=left_index,\n",
" right_index=right_index,\n",
" sort=sort,\n",
" suffixes=suffixes,\n",
" copy=copy,\n",
" indicator=indicator,\n",
" validate=validate,\n",
" )\n",
" return op.get_result()\n"
],
"metadata": {
"id": "Fpx6b8CfYWwy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"if __debug__:\n",
" merge.__doc__ = _merge_doc % \"\\nleft : DataFrame\"\n",
"\n",
"\n",
"def _groupby_and_merge(by, left: DataFrame, right: DataFrame, merge_pieces):\n",
" \"\"\"\n",
" groupby & merge; we are always performing a left-by type operation\n",
" Parameters\n",
" ----------\n",
" by: field to group\n",
" left: DataFrame\n",
" right: DataFrame\n",
" merge_pieces: function for merging\n",
" \"\"\"\n",
" pieces = []\n",
" if not isinstance(by, (list, tuple)):\n",
" by = [by]\n",
"\n",
" lby = left.groupby(by, sort=False)\n",
" rby: groupby.DataFrameGroupBy | None = None\n",
"\n",
" # if we can groupby the rhs\n",
" # then we can get vastly better perf\n",
" if all(item in right.columns for item in by):\n",
" rby = right.groupby(by, sort=False)\n",
"\n",
" for key, lhs in lby:\n",
"\n",
" if rby is None:\n",
" rhs = right\n",
" else:\n",
" try:\n",
" rhs = right.take(rby.indices[key])\n",
" except KeyError:\n",
" # key doesn't exist in left\n",
" lcols = lhs.columns.tolist()\n",
" cols = lcols + [r for r in right.columns if r not in set(lcols)]\n",
" merged = lhs.reindex(columns=cols)\n",
" merged.index = range(len(merged))\n",
" pieces.append(merged)\n",
" continue\n",
"\n",
" merged = merge_pieces(lhs, rhs)\n",
"\n",
" # make sure join keys are in the merged\n",
" # TODO, should merge_pieces do this?\n",
" merged[by] = key\n",
"\n",
" pieces.append(merged)\n",
"\n",
" # preserve the original order\n",
" # if we have a missing piece this can be reset\n",
" from pandas.core.reshape.concat import concat\n",
"\n",
" result = concat(pieces, ignore_index=True)\n",
" result = result.reindex(columns=pieces[0].columns, copy=False)\n",
" return result, lby"
],
"metadata": {
"id": "Vpvzhp89YWzb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def merge_ordered(\n",
" left: DataFrame,\n",
" right: DataFrame,\n",
" on: IndexLabel | None = None,\n",
" left_on: IndexLabel | None = None,\n",
" right_on: IndexLabel | None = None,\n",
" left_by=None,\n",
" right_by=None,\n",
" fill_method: str | None = None,\n",
" suffixes: Suffixes = (\"_x\", \"_y\"),\n",
" how: str = \"outer\",\n",
") -> DataFrame:\n",
" \"\"\"\n",
" Perform a merge for ordered data with optional filling/interpolation.\n",
" Designed for ordered data like time series data. Optionally\n",
" perform group-wise merge (see examples).\n",
" Parameters\n",
" ----------\n",
" left : DataFrame\n",
" right : DataFrame\n",
" on : label or list\n",
" Field names to join on. Must be found in both DataFrames.\n",
" left_on : label or list, or array-like\n",
" Field names to join on in left DataFrame. Can be a vector or list of\n",
" vectors of the length of the DataFrame to use a particular vector as\n",
" the join key instead of columns.\n",
" right_on : label or list, or array-like\n",
" Field names to join on in right DataFrame or vector/list of vectors per\n",
" left_on docs.\n",
" left_by : column name or list of column names\n",
" Group left DataFrame by group columns and merge piece by piece with\n",
" right DataFrame.\n",
" right_by : column name or list of column names\n",
" Group right DataFrame by group columns and merge piece by piece with\n",
" left DataFrame.\n",
" fill_method : {'ffill', None}, default None\n",
" Interpolation method for data.\n",
" suffixes : list-like, default is (\"_x\", \"_y\")\n",
" A length-2 sequence where each element is optionally a string\n",
" indicating the suffix to add to overlapping column names in\n",
" `left` and `right` respectively. Pass a value of `None` instead\n",
" of a string to indicate that the column name from `left` or\n",
" `right` should be left as-is, with no suffix. At least one of the\n",
" values must not be None.\n",
" .. versionchanged:: 0.25.0\n",
" how : {'left', 'right', 'outer', 'inner'}, default 'outer'\n",
" * left: use only keys from left frame (SQL: left outer join)\n",
" * right: use only keys from right frame (SQL: right outer join)\n",
" * outer: use union of keys from both frames (SQL: full outer join)\n",
" * inner: use intersection of keys from both frames (SQL: inner join).\n",
" Returns\n",
" -------\n",
" DataFrame\n",
" The merged DataFrame output type will the be same as\n",
" 'left', if it is a subclass of DataFrame.\n",
" See Also\n",
" --------\n",
" merge : Merge with a database-style join.\n",
" merge_asof : Merge on nearest keys.\n",
" Examples\n",
" --------\n",
" >>> df1 = pd.DataFrame(\n",
" ... {\n",
" ... \"key\": [\"a\", \"c\", \"e\", \"a\", \"c\", \"e\"],\n",
" ... \"lvalue\": [1, 2, 3, 1, 2, 3],\n",
" ... \"group\": [\"a\", \"a\", \"a\", \"b\", \"b\", \"b\"]\n",
" ... }\n",
" ... )\n",
" >>> df1\n",
" key lvalue group\n",
" 0 a 1 a\n",
" 1 c 2 a\n",
" 2 e 3 a\n",
" 3 a 1 b\n",
" 4 c 2 b\n",
" 5 e 3 b\n",
" >>> df2 = pd.DataFrame({\"key\": [\"b\", \"c\", \"d\"], \"rvalue\": [1, 2, 3]})\n",
" >>> df2\n",
" key rvalue\n",
" 0 b 1\n",
" 1 c 2\n",
" 2 d 3\n",
" >>> merge_ordered(df1, df2, fill_method=\"ffill\", left_by=\"group\")\n",
" key lvalue group rvalue\n",
" 0 a 1 a NaN\n",
" 1 b 1 a 1.0\n",
" 2 c 2 a 2.0\n",
" 3 d 2 a 3.0\n",
" 4 e 3 a 3.0\n",
" 5 a 1 b NaN\n",
" 6 b 1 b 1.0\n",
" 7 c 2 b 2.0\n",
" 8 d 2 b 3.0\n",
" 9 e 3 b 3.0\n",
" \"\"\"\n",
"\n",
" def _merger(x, y) -> DataFrame:\n",
" # perform the ordered merge operation\n",
" op = _OrderedMerge(\n",
" x,\n",
" y,\n",
" on=on,\n",
" left_on=left_on,\n",
" right_on=right_on,\n",
" suffixes=suffixes,\n",
" fill_method=fill_method,\n",
" how=how,\n",
" )\n",
" return op.get_result()\n",
"\n",
" if left_by is not None and right_by is not None:\n",
" raise ValueError(\"Can only group either left or right frames\")\n",
" elif left_by is not None:\n",
" if isinstance(left_by, str):\n",
" left_by = [left_by]\n",
" check = set(left_by).difference(left.columns)\n",
" if len(check) != 0:\n",
" raise KeyError(f\"{check} not found in left columns\")\n",
" result, _ = _groupby_and_merge(left_by, left, right, lambda x, y: _merger(x, y))\n",
" elif right_by is not None:\n",
" if isinstance(right_by, str):\n",
" right_by = [right_by]\n",
" check = set(right_by).difference(right.columns)\n",
" if len(check) != 0:\n",
" raise KeyError(f\"{check} not found in right columns\")\n",
" result, _ = _groupby_and_merge(\n",
" right_by, right, left, lambda x, y: _merger(y, x)\n",
" )\n",
" else:\n",
" result = _merger(left, right)\n",
" return result\n"
],
"metadata": {
"id": "jvqB8gWqYW1t"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def merge_asof(\n",
" left: DataFrame | Series,\n",
" right: DataFrame | Series,\n",
" on: IndexLabel | None = None,\n",
" left_on: IndexLabel | None = None,\n",
" right_on: IndexLabel | None = None,\n",
" left_index: bool = False,\n",
" right_index: bool = False,\n",
" by=None,\n",
" left_by=None,\n",
" right_by=None,\n",
" suffixes: Suffixes = (\"_x\", \"_y\"),\n",
" tolerance=None,\n",
" allow_exact_matches: bool = True,\n",
" direction: str = \"backward\",\n",
") -> DataFrame:\n",
" \"\"\"\n",
" Perform a merge by key distance.\n",
" This is similar to a left-join except that we match on nearest\n",
" key rather than equal keys. Both DataFrames must be sorted by the key.\n",
" For each row in the left DataFrame:\n",
" - A \"backward\" search selects the last row in the right DataFrame whose\n",
" 'on' key is less than or equal to the left's key.\n",
" - A \"forward\" search selects the first row in the right DataFrame whose\n",
" 'on' key is greater than or equal to the left's key.\n",
" - A \"nearest\" search selects the row in the right DataFrame whose 'on'\n",
" key is closest in absolute distance to the left's key.\n",
" The default is \"backward\" and is compatible in versions below 0.20.0.\n",
" The direction parameter was added in version 0.20.0 and introduces\n",
" \"forward\" and \"nearest\".\n",
" Optionally match on equivalent keys with 'by' before searching with 'on'.\n",
" Parameters\n",
" ----------\n",
" left : DataFrame or named Series\n",
" right : DataFrame or named Series\n",
" on : label\n",
" Field name to join on. Must be found in both DataFrames.\n",
" The data MUST be ordered. Furthermore this must be a numeric column,\n",
" such as datetimelike, integer, or float. On or left_on/right_on\n",
" must be given.\n",
" left_on : label\n",
" Field name to join on in left DataFrame.\n",
" right_on : label\n",
" Field name to join on in right DataFrame.\n",
" left_index : bool\n",
" Use the index of the left DataFrame as the join key.\n",
" right_index : bool\n",
" Use the index of the right DataFrame as the join key.\n",
" by : column name or list of column names\n",
" Match on these columns before performing merge operation.\n",
" left_by : column name\n",
" Field names to match on in the left DataFrame.\n",
" right_by : column name\n",
" Field names to match on in the right DataFrame.\n",
" suffixes : 2-length sequence (tuple, list, ...)\n",
" Suffix to apply to overlapping column names in the left and right\n",
" side, respectively.\n",
" tolerance : int or Timedelta, optional, default None\n",
" Select asof tolerance within this range; must be compatible\n",
" with the merge index.\n",
" allow_exact_matches : bool, default True\n",
" - If True, allow matching with the same 'on' value\n",
" (i.e. less-than-or-equal-to / greater-than-or-equal-to)\n",
" - If False, don't match the same 'on' value\n",
" (i.e., strictly less-than / strictly greater-than).\n",
" direction : 'backward' (default), 'forward', or 'nearest'\n",
" Whether to search for prior, subsequent, or closest matches.\n",
" Returns\n",
" -------\n",
" merged : DataFrame\n",
" See Also\n",
" --------\n",
" merge : Merge with a database-style join.\n",
" merge_ordered : Merge with optional filling/interpolation.\n",
" Examples\n",
" --------\n",
" >>> left = pd.DataFrame({\"a\": [1, 5, 10], \"left_val\": [\"a\", \"b\", \"c\"]})\n",
" >>> left\n",
" a left_val\n",
" 0 1 a\n",
" 1 5 b\n",
" 2 10 c\n",
" >>> right = pd.DataFrame({\"a\": [1, 2, 3, 6, 7], \"right_val\": [1, 2, 3, 6, 7]})\n",
" >>> right\n",
" a right_val\n",
" 0 1 1\n",
" 1 2 2\n",
" 2 3 3\n",
" 3 6 6\n",
" 4 7 7\n",
" >>> pd.merge_asof(left, right, on=\"a\")\n",
" a left_val right_val\n",
" 0 1 a 1\n",
" 1 5 b 3\n",
" 2 10 c 7\n",
" >>> pd.merge_asof(left, right, on=\"a\", allow_exact_matches=False)\n",
" a left_val right_val\n",
" 0 1 a NaN\n",
" 1 5 b 3.0\n",
" 2 10 c 7.0\n",
" >>> pd.merge_asof(left, right, on=\"a\", direction=\"forward\")\n",
" a left_val right_val\n",
" 0 1 a 1.0\n",
" 1 5 b 6.0\n",
" 2 10 c NaN\n",
" >>> pd.merge_asof(left, right, on=\"a\", direction=\"nearest\")\n",
" a left_val right_val\n",
" 0 1 a 1\n",
" 1 5 b 6\n",
" 2 10 c 7\n",
" We can use indexed DataFrames as well.\n",
" >>> left = pd.DataFrame({\"left_val\": [\"a\", \"b\", \"c\"]}, index=[1, 5, 10])\n",
" >>> left\n",
" left_val\n",
" 1 a\n",
" 5 b\n",
" 10 c\n",
" >>> right = pd.DataFrame({\"right_val\": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])\n",
" >>> right\n",
" right_val\n",
" 1 1\n",
" 2 2\n",
" 3 3\n",
" 6 6\n",
" 7 7\n",
" >>> pd.merge_asof(left, right, left_index=True, right_index=True)\n",
" left_val right_val\n",
" 1 a 1\n",
" 5 b 3\n",
" 10 c 7\n",
" Here is a real-world times-series example\n",
" >>> quotes = pd.DataFrame(\n",
" ... {\n",
" ... \"time\": [\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.023\"),\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.023\"),\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.030\"),\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.041\"),\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.048\"),\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.049\"),\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.072\"),\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.075\")\n",
" ... ],\n",
" ... \"ticker\": [\n",
" ... \"GOOG\",\n",
" ... \"MSFT\",\n",
" ... \"MSFT\",\n",
" ... \"MSFT\",\n",
" ... \"GOOG\",\n",
" ... \"AAPL\",\n",
" ... \"GOOG\",\n",
" ... \"MSFT\"\n",
" ... ],\n",
" ... \"bid\": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],\n",
" ... \"ask\": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03]\n",
" ... }\n",
" ... )\n",
" >>> quotes\n",
" time ticker bid ask\n",
" 0 2016-05-25 13:30:00.023 GOOG 720.50 720.93\n",
" 1 2016-05-25 13:30:00.023 MSFT 51.95 51.96\n",
" 2 2016-05-25 13:30:00.030 MSFT 51.97 51.98\n",
" 3 2016-05-25 13:30:00.041 MSFT 51.99 52.00\n",
" 4 2016-05-25 13:30:00.048 GOOG 720.50 720.93\n",
" 5 2016-05-25 13:30:00.049 AAPL 97.99 98.01\n",
" 6 2016-05-25 13:30:00.072 GOOG 720.50 720.88\n",
" 7 2016-05-25 13:30:00.075 MSFT 52.01 52.03\n",
" >>> trades = pd.DataFrame(\n",
" ... {\n",
" ... \"time\": [\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.023\"),\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.038\"),\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.048\"),\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.048\"),\n",
" ... pd.Timestamp(\"2016-05-25 13:30:00.048\")\n",
" ... ],\n",
" ... \"ticker\": [\"MSFT\", \"MSFT\", \"GOOG\", \"GOOG\", \"AAPL\"],\n",
" ... \"price\": [51.95, 51.95, 720.77, 720.92, 98.0],\n",
" ... \"quantity\": [75, 155, 100, 100, 100]\n",
" ... }\n",
" ... )\n",
" >>> trades\n",
" time ticker price quantity\n",
" 0 2016-05-25 13:30:00.023 MSFT 51.95 75\n",
" 1 2016-05-25 13:30:00.038 MSFT 51.95 155\n",
" 2 2016-05-25 13:30:00.048 GOOG 720.77 100\n",
" 3 2016-05-25 13:30:00.048 GOOG 720.92 100\n",
" 4 2016-05-25 13:30:00.048 AAPL 98.00 100\n",
" By default we are taking the asof of the quotes\n",
" >>> pd.merge_asof(trades, quotes, on=\"time\", by=\"ticker\")\n",
" time ticker price quantity bid ask\n",
" 0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96\n",
" 1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98\n",
" 2 2016-05-25 13:30:00.048 GOOG 720.77 100 720.50 720.93\n",
" 3 2016-05-25 13:30:00.048 GOOG 720.92 100 720.50 720.93\n",
" 4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN\n",
" We only asof within 2ms between the quote time and the trade time\n",
" >>> pd.merge_asof(\n",
" ... trades, quotes, on=\"time\", by=\"ticker\", tolerance=pd.Timedelta(\"2ms\")\n",
" ... )\n",
" time ticker price quantity bid ask\n",
" 0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96\n",
" 1 2016-05-25 13:30:00.038 MSFT 51.95 155 NaN NaN\n",
" 2 2016-05-25 13:30:00.048 GOOG 720.77 100 720.50 720.93\n",
" 3 2016-05-25 13:30:00.048 GOOG 720.92 100 720.50 720.93\n",
" 4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN\n",
" We only asof within 10ms between the quote time and the trade time\n",
" and we exclude exact matches on time. However *prior* data will\n",
" propagate forward\n",
" >>> pd.merge_asof(\n",
" ... trades,\n",
" ... quotes,\n",
" ... on=\"time\",\n",
" ... by=\"ticker\",\n",
" ... tolerance=pd.Timedelta(\"10ms\"),\n",
" ... allow_exact_matches=False\n",
" ... )\n",
" time ticker price quantity bid ask\n",
" 0 2016-05-25 13:30:00.023 MSFT 51.95 75 NaN NaN\n",
" 1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98\n",
" 2 2016-05-25 13:30:00.048 GOOG 720.77 100 NaN NaN\n",
" 3 2016-05-25 13:30:00.048 GOOG 720.92 100 NaN NaN\n",
" 4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN\n",
" \"\"\"\n",
" op = _AsOfMerge(\n",
" left,\n",
" right,\n",
" on=on,\n",
" left_on=left_on,\n",
" right_on=right_on,\n",
" left_index=left_index,\n",
" right_index=right_index,\n",
" by=by,\n",
" left_by=left_by,\n",
" right_by=right_by,\n",
" suffixes=suffixes,\n",
" how=\"asof\",\n",
" tolerance=tolerance,\n",
" allow_exact_matches=allow_exact_matches,\n",
" direction=direction,\n",
" )\n",
" return op.get_result()"
],
"metadata": {
"id": "CHY5OnzaYW36"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# TODO: transformations??\n",
"# TODO: only copy DataFrames when modification necessary\n",
"class _MergeOperation:\n",
" \"\"\"\n",
" Perform a database (SQL) merge operation between two DataFrame or Series\n",
" objects using either columns as keys or their row indexes\n",
" \"\"\"\n",
"\n",
" _merge_type = \"merge\"\n",
"\n",
" def __init__(\n",
" self,\n",
" left: DataFrame | Series,\n",
" right: DataFrame | Series,\n",
" how: str = \"inner\",\n",
" on: IndexLabel | None = None,\n",
" left_on: IndexLabel | None = None,\n",
" right_on: IndexLabel | None = None,\n",
" axis: int = 1,\n",
" left_index: bool = False,\n",
" right_index: bool = False,\n",
" sort: bool = True,\n",
" suffixes: Suffixes = (\"_x\", \"_y\"),\n",
" copy: bool = True,\n",
" indicator: bool = False,\n",
" validate: str | None = None,\n",
" ) -> None:\n",
" _left = _validate_operand(left)\n",
" _right = _validate_operand(right)\n",
" self.left = self.orig_left = _left\n",
" self.right = self.orig_right = _right\n",
" self.how = how\n",
"\n",
" # bm_axis -> the axis on the BlockManager\n",
" self.bm_axis = axis\n",
" # axis --> the axis on the Series/DataFrame\n",
" self.axis = 1 - axis if self.left.ndim == 2 else 0\n",
"\n",
" self.on = com.maybe_make_list(on)\n",
" self.left_on = com.maybe_make_list(left_on)\n",
" self.right_on = com.maybe_make_list(right_on)\n",
"\n",
" self.copy = copy\n",
" self.suffixes = suffixes\n",
" self.sort = sort\n",
"\n",
" self.left_index = left_index\n",
" self.right_index = right_index\n",
"\n",
" self.indicator = indicator\n",
"\n",
" self.indicator_name: str | None\n",
" if isinstance(self.indicator, str):\n",
" self.indicator_name = self.indicator\n",
" elif isinstance(self.indicator, bool):\n",
" self.indicator_name = \"_merge\" if self.indicator else None\n",
" else:\n",
" raise ValueError(\n",
" \"indicator option can only accept boolean or string arguments\"\n",
" )\n",
"\n",
" if not is_bool(left_index):\n",
" raise ValueError(\n",
" f\"left_index parameter must be of type bool, not {type(left_index)}\"\n",
" )\n",
" if not is_bool(right_index):\n",
" raise ValueError(\n",
" f\"right_index parameter must be of type bool, not {type(right_index)}\"\n",
" )\n",
"\n",
" # warn user when merging between different levels\n",
" if _left.columns.nlevels != _right.columns.nlevels:\n",
" msg = (\n",
" \"merging between different levels is deprecated and will be removed \"\n",
" f\"in a future version. ({left.columns.nlevels} levels on the left, \"\n",
" f\"{right.columns.nlevels} on the right)\"\n",
" )\n",
" # stacklevel chosen to be correct when this is reached via pd.merge\n",
" # (and not DataFrame.join)\n",
" warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())\n",
"\n",
" self._validate_specification()\n",
"\n",
" cross_col = None\n",
" if self.how == \"cross\":\n",
" (\n",
" self.left,\n",
" self.right,\n",
" self.how,\n",
" cross_col,\n",
" ) = self._create_cross_configuration(self.left, self.right)\n",
" self.left_on = self.right_on = [cross_col]\n",
" self._cross = cross_col\n",
"\n",
" # note this function has side effects\n",
" (\n",
" self.left_join_keys,\n",
" self.right_join_keys,\n",
" self.join_names,\n",
" ) = self._get_merge_keys()\n",
"\n",
" # validate the merge keys dtypes. We may need to coerce\n",
" # to avoid incompatible dtypes\n",
" self._maybe_coerce_merge_keys()\n",
"\n",
" # If argument passed to validate,\n",
" # check if columns specified as unique\n",
" # are in fact unique.\n",
" if validate is not None:\n",
" self._validate(validate)\n",
" def get_result(self) -> DataFrame:\n",
" if self.indicator:\n",
" self.left, self.right = self._indicator_pre_merge(self.left, self.right)\n",
"\n",
" join_index, left_indexer, right_indexer = self._get_join_info()\n",
"\n",
" llabels, rlabels = _items_overlap_with_suffix(\n",
" self.left._info_axis, self.right._info_axis, self.suffixes\n",
" )\n",
"\n",
" lindexers = {1: left_indexer} if left_indexer is not None else {}\n",
" rindexers = {1: right_indexer} if right_indexer is not None else {}\n",
"\n",
" result_data = concatenate_managers(\n",
" [(self.left._mgr, lindexers), (self.right._mgr, rindexers)],\n",
" axes=[llabels.append(rlabels), join_index],\n",
" concat_axis=0,\n",
" copy=self.copy,\n",
" )\n",
"\n",
" typ = self.left._constructor\n",
" result = typ(result_data).__finalize__(self, method=self._merge_type)\n",
"\n",
" if self.indicator:\n",
" result = self._indicator_post_merge(result)\n",
"\n",
" self._maybe_add_join_keys(result, left_indexer, right_indexer)\n",
"\n",
" self._maybe_restore_index_levels(result)\n",
"\n",
" self._maybe_drop_cross_column(result, self._cross)\n",
"\n",
" return result.__finalize__(self, method=\"merge\")\n",
"\n",
" def _maybe_drop_cross_column(\n",
" self, result: DataFrame, cross_col: str | None\n",
" ) -> None:\n",
" if cross_col is not None:\n",
" del result[cross_col]\n",
"\n",
" def _indicator_pre_merge(\n",
" self, left: DataFrame, right: DataFrame\n",
" ) -> tuple[DataFrame, DataFrame]:\n",
"\n",
" columns = left.columns.union(right.columns)\n",
"\n",
" for i in [\"_left_indicator\", \"_right_indicator\"]:\n",
" if i in columns:\n",
" raise ValueError(\n",
" \"Cannot use `indicator=True` option when \"\n",
" f\"data contains a column named {i}\"\n",
" )\n",
" if self.indicator_name in columns:\n",
" raise ValueError(\n",
" \"Cannot use name of an existing column for indicator column\"\n",
" )\n",
"\n",
" left = left.copy()\n",
" right = right.copy()\n",
"\n",
" left[\"_left_indicator\"] = 1\n",
" left[\"_left_indicator\"] = left[\"_left_indicator\"].astype(\"int8\")\n",
"\n",
" right[\"_right_indicator\"] = 2\n",
" right[\"_right_indicator\"] = right[\"_right_indicator\"].astype(\"int8\")\n",
"\n",
" return left, right\n",
"\n",
" def _indicator_post_merge(self, result: DataFrame) -> DataFrame:\n",
"\n",
" result[\"_left_indicator\"] = result[\"_left_indicator\"].fillna(0)\n",
" result[\"_right_indicator\"] = result[\"_right_indicator\"].fillna(0)\n",
"\n",
" result[self.indicator_name] = Categorical(\n",
" (result[\"_left_indicator\"] + result[\"_right_indicator\"]),\n",
" categories=[1, 2, 3],\n",
" )\n",
" result[self.indicator_name] = result[self.indicator_name].cat.rename_categories(\n",
" [\"left_only\", \"right_only\", \"both\"]\n",
" )\n",
"\n",
" result = result.drop(labels=[\"_left_indicator\", \"_right_indicator\"], axis=1)\n",
" return result\n",
"\n",
" def _maybe_restore_index_levels(self, result: DataFrame) -> None:\n",
" \"\"\"\n",
" Restore index levels specified as `on` parameters\n",
" Here we check for cases where `self.left_on` and `self.right_on` pairs\n",
" each reference an index level in their respective DataFrames. The\n",
" joined columns corresponding to these pairs are then restored to the\n",
" index of `result`.\n",
" **Note:** This method has side effects. It modifies `result` in-place\n",
" Parameters\n",
" ----------\n",
" result: DataFrame\n",
" merge result\n",
" Returns\n",
" -------\n",
" None\n",
" \"\"\"\n",
" names_to_restore = []\n",
" for name, left_key, right_key in zip(\n",
" self.join_names, self.left_on, self.right_on\n",
" ):\n",
" if (\n",
" self.orig_left._is_level_reference(left_key)\n",
" and self.orig_right._is_level_reference(right_key)\n",
" and left_key == right_key\n",
" and name not in result.index.names\n",
" ):\n",
"\n",
" names_to_restore.append(name)\n",
"\n",
" if names_to_restore:\n",
" result.set_index(names_to_restore, inplace=True)\n",
"\n",
" def _maybe_add_join_keys(\n",
" self,\n",
" result: DataFrame,\n",
" left_indexer: np.ndarray | None,\n",
" right_indexer: np.ndarray | None,\n",
" ) -> None:\n",
"\n",
" left_has_missing = None\n",
" right_has_missing = None\n",
"\n",
" keys = zip(self.join_names, self.left_on, self.right_on)\n",
" for i, (name, lname, rname) in enumerate(keys):\n",
" if not _should_fill(lname, rname):\n",
" continue\n",
"\n",
" take_left, take_right = None, None\n",
"\n",
" if name in result:\n",
"\n",
" if left_indexer is not None and right_indexer is not None:\n",
" if name in self.left:\n",
"\n",
" if left_has_missing is None:\n",
" left_has_missing = (left_indexer == -1).any()\n",
"\n",
" if left_has_missing:\n",
" take_right = self.right_join_keys[i]\n",
"\n",
" if not is_dtype_equal(\n",
" result[name].dtype, self.left[name].dtype\n",
" ):\n",
" take_left = self.left[name]._values\n",
"\n",
" elif name in self.right:\n",
"\n",
" if right_has_missing is None:\n",
" right_has_missing = (right_indexer == -1).any()\n",
"\n",
" if right_has_missing:\n",
" take_left = self.left_join_keys[i]\n",
"\n",
" if not is_dtype_equal(\n",
" result[name].dtype, self.right[name].dtype\n",
" ):\n",
" take_right = self.right[name]._values\n",
"\n",
" elif left_indexer is not None and is_array_like(self.left_join_keys[i]):\n",
" take_left = self.left_join_keys[i]\n",
" take_right = self.right_join_keys[i]\n",
"\n",
" if take_left is not None or take_right is not None:\n",
"\n",
" if take_left is None:\n",
" lvals = result[name]._values\n",
" else:\n",
" # TODO: can we pin down take_left's type earlier?\n",
" take_left = extract_array(take_left, extract_numpy=True)\n",
" lfill = na_value_for_dtype(take_left.dtype)\n",
" lvals = algos.take_nd(take_left, left_indexer, fill_value=lfill)\n",
"\n",
" if take_right is None:\n",
" rvals = result[name]._values\n",
" else:\n",
" # TODO: can we pin down take_right's type earlier?\n",
" take_right = extract_array(take_right, extract_numpy=True)\n",
" rfill = na_value_for_dtype(take_right.dtype)\n",
" rvals = algos.take_nd(take_right, right_indexer, fill_value=rfill)\n",
"\n",
" # if we have an all missing left_indexer\n",
" # make sure to just use the right values or vice-versa\n",
" mask_left = left_indexer == -1\n",
" mask_right = right_indexer == -1\n",
" # error: Item \"bool\" of \"Union[Any, bool]\" has no attribute \"all\"\n",
" if mask_left.all(): # type: ignore[union-attr]\n",
" key_col = Index(rvals)\n",
" result_dtype = rvals.dtype\n",
" # error: Item \"bool\" of \"Union[Any, bool]\" has no attribute \"all\"\n",
" elif (\n",