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KyoungHa-Park/분유가격_리포트_v6(제품 이미지 추가).ipynb

Created August 14, 2018 11:08
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분유가격_리포트_v6(제품 이미지 추가).ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "miAoF-RqxmkR"
},
"source": [
"### 이력 관리\n",
"\n",
"+ v1 : matplotlib 사용\n",
"+ v2 : DB 구조(code) 간소화 \n",
"+ v3 : plotly 사용\n",
"+ v4 : Category 추가(민감성, 산양) 및 삭제(소캔)\n",
"+ v5 : bokeh 사용\n",
" + 동적인 graph를 위해, bokeh 또는 plotly가 가능함\n",
" + 그러나 plotly는 무료의 경우 25개의 plot만 운영이 가능하여, bokeh으로 변경됨\n",
"+ v6 : 제품 이미지 추가"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### display 내용\n",
"+ [x] Category 설정/확정\n",
" + [x] Category별 이미지 표기(cross-check 관점)\n",
"+ [x] tab 구분\n",
"+ [x] 동적 관리\n",
" + [x] 동적 그래프 표현(axis moving)\n",
" + [ ] 결과값 표기(floating)\n",
"+ [ ] 제품별 예측값 표기\n",
"+ [x] code 숨기기"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "q1CqjXSSwsBF"
},
"source": [
"# 관련 코드"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "nyA67VGTwsBC"
},
"outputs": [],
"source": [
"# bokeh\n",
"# 원본 : http://bokeh.pydata.org/en/latest/docs/gallery.html\n",
"# 출처 : https://datascienceschool.net/view-notebook/b03af554a1494f159fc94d65d70fe7b2/"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "9xpM1WwmwsBF",
"outputId": "6cfd19c5-ec81-471c-9983-df941533267d"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div class=\"bk-root\">\n",
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" <span id=\"a52c58a3-6a7a-45a9-b9ee-b2e4a8655979\">Loading BokehJS ...</span>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"\n",
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"\n",
" var JS_MIME_TYPE = 'application/javascript';\n",
" var HTML_MIME_TYPE = 'text/html';\n",
" var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
" var CLASS_NAME = 'output_bokeh rendered_html';\n",
"\n",
" /**\n",
" * Render data to the DOM node\n",
" */\n",
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" var script = document.createElement(\"script\");\n",
" node.appendChild(script);\n",
" }\n",
"\n",
" /**\n",
" * Handle when an output is cleared or removed\n",
" */\n",
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"\n",
" var id = cell.output_area._bokeh_element_id;\n",
" var server_id = cell.output_area._bokeh_server_id;\n",
" // Clean up Bokeh references\n",
" if (id != null && id in Bokeh.index) {\n",
" Bokeh.index[id].model.document.clear();\n",
" delete Bokeh.index[id];\n",
" }\n",
"\n",
" if (server_id !== undefined) {\n",
" // Clean up Bokeh references\n",
" var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
" cell.notebook.kernel.execute(cmd, {\n",
" iopub: {\n",
" output: function(msg) {\n",
" var id = msg.content.text.trim();\n",
" if (id in Bokeh.index) {\n",
" Bokeh.index[id].model.document.clear();\n",
" delete Bokeh.index[id];\n",
" }\n",
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" });\n",
" // Destroy server and session\n",
" var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
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"\n",
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" return\n",
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" // store reference to embed id on output_area\n",
" output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
" }\n",
" if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
" var bk_div = document.createElement(\"div\");\n",
" bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
" var script_attrs = bk_div.children[0].attributes;\n",
" for (var i = 0; i < script_attrs.length; i++) {\n",
" toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
" }\n",
" // store reference to server id on output_area\n",
" output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
" }\n",
" }\n",
"\n",
" function register_renderer(events, OutputArea) {\n",
"\n",
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" // create a DOM node to render to\n",
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" metadata,\n",
" CLASS_NAME,\n",
" EXEC_MIME_TYPE\n",
" );\n",
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" // Render to node\n",
" var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
" render(props, toinsert[toinsert.length - 1]);\n",
" element.append(toinsert);\n",
" return toinsert\n",
" }\n",
"\n",
" /* Handle when an output is cleared or removed */\n",
" events.on('clear_output.CodeCell', handleClearOutput);\n",
" events.on('delete.Cell', handleClearOutput);\n",
"\n",
" /* Handle when a new output is added */\n",
" events.on('output_added.OutputArea', handleAddOutput);\n",
"\n",
" /**\n",
" * Register the mime type and append_mime function with output_area\n",
" */\n",
" OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
" /* Is output safe? */\n",
" safe: true,\n",
" /* Index of renderer in `output_area.display_order` */\n",
" index: 0\n",
" });\n",
" }\n",
"\n",
" // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
" if (root.Jupyter !== undefined) {\n",
" var events = require('base/js/events');\n",
" var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
"\n",
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" register_renderer(events, OutputArea);\n",
" }\n",
" }\n",
"\n",
" \n",
" if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
" root._bokeh_timeout = Date.now() + 5000;\n",
" root._bokeh_failed_load = false;\n",
" }\n",
"\n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
"\n",
" function display_loaded() {\n",
" var el = document.getElementById(\"a52c58a3-6a7a-45a9-b9ee-b2e4a8655979\");\n",
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" }\n",
" } else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
"\n",
"\n",
" function run_callbacks() {\n",
" try {\n",
" root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" }\n",
" finally {\n",
" delete root._bokeh_onload_callbacks\n",
" }\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
" function load_libs(js_urls, callback) {\n",
" root._bokeh_onload_callbacks.push(callback);\n",
" if (root._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" root._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" root._bokeh_is_loading--;\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"a52c58a3-6a7a-45a9-b9ee-b2e4a8655979\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a52c58a3-6a7a-45a9-b9ee-b2e4a8655979' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
"\n",
" var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n",
"\n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" Bokeh.set_log_level(\"info\");\n",
" },\n",
" \n",
" function(Bokeh) {\n",
" \n",
" },\n",
" function(Bokeh) {\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
" }\n",
" ];\n",
"\n",
" function run_inline_js() {\n",
" \n",
" if ((root.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i].call(root, root.Bokeh);\n",
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" display_loaded();\n",
" }} else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!root._bokeh_failed_load) {\n",
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" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
"}(window));"
],
"application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"a52c58a3-6a7a-45a9-b9ee-b2e4a8655979\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"a52c58a3-6a7a-45a9-b9ee-b2e4a8655979\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a52c58a3-6a7a-45a9-b9ee-b2e4a8655979' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"a52c58a3-6a7a-45a9-b9ee-b2e4a8655979\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"##모듈 호출\n",
"import sqlite3\n",
"\n",
"from pandas import Series, DataFrame\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from IPython.display import Image\n",
"import IPython.core.display as di\n",
"\n",
"import bokeh.plotting as bp\n",
"from bokeh.models.widgets import Panel, Tabs\n",
"from bokeh.io import output_file, show\n",
"from bokeh.plotting import figure\n",
"\n",
"bp.output_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "MaxJzAlKwsBI"
},
"source": [
"# 기초작업 : 기본 불러오기/전처리"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"base_uri": "https://localhost:8080/",
"height": 244
},
"colab_type": "code",
"executionInfo": {
"elapsed": 1222,
"status": "error",
"timestamp": 1532494195554,
"user": {
"displayName": "박경하",
"photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
"userId": "110612470497084106049"
},
"user_tz": -540
},
"id": "NhNM4LYSwsBK",
"outputId": "da571de5-e48e-4cb9-ad2b-9eaf0b66d59c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DB read success!\n"
]
}
],
"source": [
"##.db 파일(스키마) 접속\n",
"con = sqlite3.connect(\"C:/Users/khpark/AnacondaProjects/milkpower price/MONITERING_PRICE_test.db\")\n",
"cursor = con.cursor()\n",
"\n",
"# df0 = pd.read_sql(\"SELECT max(DATE) FROM MILKPOWER_MIN \", con)\n",
"\n",
"print(\"DB read success!\")"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "IAyAEnDKwsBM"
},
"outputs": [],
"source": [
"#### Excel 저장\n",
"\n",
"# df = pd.read_sql(\"SELECT * FROM MILKPOWER_MIN\", con)\n",
"\n",
"# df.to_excel('data.xlsx', sheet_name= 'sheet1' ,index=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "S7wzUExqwsBT"
},
"outputs": [],
"source": [
"# table 삭제\n",
"# cursor.execute(\"drop TABLE MP_PRODUCT\")\n",
"# con.commit()"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "cXjeDW7swsBR"
},
"outputs": [],
"source": [
"## table 생성 및 자료 넣기\n",
"\n",
"# cursor.execute(\"CREATE TABLE MP_PRODUCT(PRODUCT text, COMPANY text, BRAND text, STAGE text, TYPE1 text, TYPE2 text, VOLUNM1 text, VOLUNM2 text, LINE text)\")\n",
"# con.commit()\n",
"\n",
"# df0=pd.read_excel('C:/Users/khpark/AnacondaProjects/milkpower price/MP_PRODUCT.xlsx','Sheet1')\n",
"\n",
"# df0.to_sql('MP_PRODUCT', con, if_exists='replace')\n",
"\n",
"# con.commit()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "qlBPwiVkwsBY"
},
"source": [
"## 1.캔(고가)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab_type": "text",
"id": "77m8JXHfw312"
},
"outputs": [],
"source": [
"### 1) 자료 불러오기"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "utYS4NHPwsBZ"
},
"outputs": [],
"source": [
"##########\n",
"#1단계\n",
"#####\n",
"\n",
"# 변수지정\n",
"STAGE,PACKAGE,VOLUNM,LINE = \"1단계\",\"캔\",\"대\",\"고가\"\n",
"\n",
"# 쿼리설정\n",
"query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
" 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
" 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
" 'and B.VOLUNM2 = ' + \"'\" + VOLUNM + \"' \" +\n",
" 'and B.LINE = ' + \"'\" + LINE + \"' \" +\n",
" \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# 쿼리실행\n",
"df11 = pd.read_sql(query, con)\n",
"\n",
"#날짜형식 지정\n",
"df11['DATE'] = pd.to_datetime(df11['DATE'])\n",
"\n",
"#숫자형식 지정\n",
"df11['PRICE'] = pd.Series(df11['PRICE']).str.replace(\",\", \"\")\n",
"df11['PRICE'] = pd.to_numeric(df11['PRICE'])\n",
"\n",
"#자료 피벗팅\n",
"# df = df.pivot('DATE','BRAND','PRICE')\n",
"df11 = pd.pivot_table(df11, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n",
"\n",
"\n",
"##########\n",
"#2단계\n",
"#####\n",
"\n",
"# 변수지정\n",
"STAGE,PACKAGE,VOLUNM,LINE = \"2단계\",\"캔\",\"대\",\"고가\"\n",
"\n",
"# 쿼리설정\n",
"query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
" 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
" 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
" 'and B.VOLUNM2 = ' + \"'\" + VOLUNM + \"' \" +\n",
" 'and B.LINE = ' + \"'\" + LINE + \"' \" +\n",
" \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# 쿼리실행\n",
"df12 = pd.read_sql(query, con)\n",
"\n",
"#날짜형식 지정\n",
"df12['DATE'] = pd.to_datetime(df12['DATE'])\n",
"\n",
"#숫자형식 지정\n",
"df12['PRICE'] = pd.Series(df12['PRICE']).str.replace(\",\", \"\")\n",
"df12['PRICE'] = pd.to_numeric(df12['PRICE'])\n",
"\n",
"#자료 피벗팅\n",
"# df = df.pivot('DATE','BRAND','PRICE')\n",
"df12 = pd.pivot_table(df12, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n",
"\n",
"\n",
"##########\n",
"#3단계\n",
"#####\n",
"\n",
"# 변수지정\n",
"STAGE,PACKAGE,VOLUNM,LINE = \"3단계\",\"캔\",\"대\",\"고가\"\n",
"\n",
"# 쿼리설정\n",
"query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
" 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
" 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
" 'and B.VOLUNM2 = ' + \"'\" + VOLUNM + \"' \" +\n",
" 'and B.LINE = ' + \"'\" + LINE + \"' \" +\n",
" \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# 쿼리실행\n",
"df13 = pd.read_sql(query, con)\n",
"\n",
"#날짜형식 지정\n",
"df13['DATE'] = pd.to_datetime(df13['DATE'])\n",
"\n",
"#숫자형식 지정\n",
"df13['PRICE'] = pd.Series(df13['PRICE']).str.replace(\",\", \"\")\n",
"df13['PRICE'] = pd.to_numeric(df13['PRICE'])\n",
"\n",
"#자료 피벗팅\n",
"# df = df.pivot('DATE','BRAND','PRICE')\n",
"df13 = pd.pivot_table(df13, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n",
"\n",
"\n",
"\n",
"##########\n",
"#4단계\n",
"#####\n",
"\n",
"# 변수지정\n",
"STAGE,PACKAGE,VOLUNM,LINE = \"4단계\",\"캔\",\"대\",\"고가\"\n",
"\n",
"# 쿼리설정\n",
"query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
" 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
" 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
" 'and B.VOLUNM2 = ' + \"'\" + VOLUNM + \"' \" +\n",
" 'and B.LINE = ' + \"'\" + LINE + \"' \" +\n",
" \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# 쿼리실행\n",
"df14 = pd.read_sql(query, con)\n",
"\n",
"#날짜형식 지정\n",
"df14['DATE'] = pd.to_datetime(df14['DATE'])\n",
"\n",
"#숫자형식 지정\n",
"df14['PRICE'] = pd.Series(df14['PRICE']).str.replace(\",\", \"\")\n",
"df14['PRICE'] = pd.to_numeric(df14['PRICE'])\n",
"\n",
"#자료 피벗팅\n",
"# df = df.pivot('DATE','BRAND','PRICE')\n",
"df14 = pd.pivot_table(df14, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "Q13OUdpZxmkw"
},
"outputs": [],
"source": [
"# df11.head()"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"colab_type": "text",
"id": "JdOjJsiowsBa"
},
"outputs": [],
"source": [
"### 2) bokeh 그래프 그리기"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "eOD86LBEwsBc"
},
"outputs": [],
"source": [
"p11 = figure(x_axis_type ='datetime', plot_width=800, plot_height=400, title=\"대캔(고가) : 1단계\")\n",
"p11.line(df11.index, df11['궁'], color ='green', legend ='궁', line_width=2)\n",
"p11.line(df11.index, df11['무항생제'], color ='gray', legend ='무항생제', line_width=2)\n",
"p11.line(df11.index, df11['아이엠마더'], color ='sienna', legend ='아이엠마더', line_width=2)\n",
"p11.line(df11.index, df11['퀸'], color ='purple', legend ='퀸', line_width=2)\n",
"tab11 = Panel(child=p11, title=\"1단계\")\n",
"\n",
"p12 = figure(x_axis_type ='datetime', plot_width=800, plot_height=400, title=\"대캔(고가) : 2단계\")\n",
"p12.line(df12.index, df12['궁'], color ='green', legend ='궁', line_width=2)\n",
"p12.line(df12.index, df12['무항생제'], color ='gray', legend ='무항생제', line_width=2)\n",
"p12.line(df12.index, df12['아이엠마더'], color ='sienna', legend ='아이엠마더', line_width=2)\n",
"tab12 = Panel(child=p12, title=\"2단계\")\n",
"\n",
"p13 = figure(x_axis_type ='datetime', plot_width=800, plot_height=400, title=\"대캔(고가) : 3단계\")\n",
"p13.line(df13.index, df13['궁'], color ='green', legend ='궁', line_width=2)\n",
"p13.line(df13.index, df13['무항생제'], color ='gray', legend ='무항생제', line_width=2)\n",
"p13.line(df13.index, df13['아이엠마더'], color ='sienna', legend ='아이엠마더', line_width=2)\n",
"tab13 = Panel(child=p13, title=\"3단계\")\n",
"\n",
"p14 = figure(x_axis_type ='datetime', plot_width=800, plot_height=400, title=\"대캔(고가) : 4단계\")\n",
"p14.line(df14.index, df14['궁'], color ='green', legend ='궁', line_width=2)\n",
"p14.line(df14.index, df14['아이엠마더'], color ='sienna', legend ='아이엠마더', line_width=2)\n",
"tab14 = Panel(child=p14, title=\"4단계\")\n",
"\n",
"tabs1 = Tabs(tabs=[ tab11, tab12, tab13, tab14 ])\n",
"\n",
"# print('대캔(고가)')\n",
"# show(tabs1)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"대캔(고가) : 제품현황\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('대캔(고가) : 제품현황')\n",
"Image(\"image/분유_캔(고가).PNG\")"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "d5XC4-Guxmk2",
"outputId": "fc8ed0e2-998e-46b4-d64e-1cdc13c13dd7"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" <div class=\"bk-root\" id=\"b55df3e9-3d02-494a-88cd-116b7e459185\"></div>\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"(function(root) {\n",
" function embed_document(root) {\n",
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4\\ub2e8\\uacc4\"},\"id\":\"c8024a1a-607a-4dfa-8be5-6d4b134c8cda\",\"type\":\"Title\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"39c3fb3d-7931-487a-a069-8f0cef3886bf\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"612d7571-4a5d-43f4-8802-6c537b556b79\",\"type\":\"DaysTicker\"},{\"attributes\":{\"callback\":null},\"id\":\"7f2165e5-69bf-48ea-b38f-29470c5538e7\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"11c1d6c6-ec04-4c74-85a5-17e9b897fae8\",\"type\":\"ResetTool\"},{\"attributes\":{\"label\":{\"value\":\"\\uad81\"},\"renderers\":[{\"id\":\"5fae8f83-8803-4797-82d8-3fcaa5fe6092\",\"type\":\"GlyphRenderer\"}]},\"id\":\"cc24cf19-c864-4810-88e8-7acfd1615f76\",\"type\":\"LegendItem\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"db7ee4bc-568d-4026-9fa3-928f74a923e8\",\"type\":\"PanTool\"},{\"id\":\"c7436dd7-9cc2-48a0-b5c2-f620ae369ee3\",\"type\":\"WheelZoomTool\"},{\"id\":\"68aa69e6-7a47-41c2-a47c-a6f1223520a0\",\"type\":\"BoxZoomTool\"},{\"id\":\"3c65a908-fab8-4b2f-b0b2-efa46c340f4d\",\"type\":\"SaveTool\"},{\"id\":\"76dbbad0-66aa-4571-be31-319a9aac0b5d\",\"type\":\"ResetTool\"},{\"id\":\"9a862bba-ff76-450b-8d4e-0a5f61af89fa\",\"type\":\"HelpTool\"}]},\"id\":\"15c37e8c-a414-4c34-b8b4-deddb78883cf\",\"type\":\"Toolbar\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"cd1ac3ec-3666-4d61-b5fe-49ca7b7abeda\",\"type\":\"DaysTicker\"},{\"attributes\":{},\"id\":\"338dbc4b-d1e3-4098-b315-3dc44060319f\",\"type\":\"YearsTicker\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"2c0e633d-f35d-4324-ac7e-ce98cad8979e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"cda11603-0ac4-44af-b142-c24e19c6017d\",\"type\":\"BasicTicker\"}},\"id\":\"4d31df97-f918-49b7-918d-dc870a89550e\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"4ce8889f-1bfe-4f5d-8318-3d9d3fe7dcde\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null},\"id\":\"219e83ac-3a9f-4793-b0ee-eb38f4a31d5c\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"57b44ac2-2ba7-4244-945d-9f8e7e5f880f\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"5e451d50-1beb-45ef-a989-36ac1d7890db\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"label\":{\"value\":\"\\uad81\"},\"renderers\":[{\"id\":\"2e19d107-2700-41a2-a3fb-1039a80b0b8e\",\"type\":\"GlyphRenderer\"}]},\"id\":\"541a01df-1f88-453a-a401-2134d7c0f640\",\"type\":\"LegendItem\"},{\"attributes\":{\"callback\":null},\"id\":\"f8781ece-48ad-4a1c-893c-92ed97b81cf4\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"09d6d4a5-4545-4ef3-8fe0-49cc04fb152c\",\"type\":\"SaveTool\"},{\"attributes\":{\"data_source\":{\"id\":\"e594a347-b53f-47f9-84c1-23e8bd805ee8\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"6add3fd3-34e7-4b20-a9b5-00b0390e927e\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"15ad656c-3b46-4bcd-83d6-6b7857689163\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"258b1e3a-6a45-46c0-8144-94e9db7a4e97\",\"type\":\"CDSView\"}},\"id\":\"47971f9a-c8a2-46f6-bb48-d5f0d0d1ee1f\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"9cc0b7ba-508f-471a-877a-e1fb87ab5d6c\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"70fbaeee-cfd6-4b4a-b041-a89d7c21e895\",\"type\":\"PanTool\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"eb5d909f-a1f3-4390-ada5-65a2ffba1621\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"formatter\":{\"id\":\"434b7a86-f45c-468f-8b79-09ad4c068cd5\",\"type\":\"DatetimeTickFormatter\"},\"plot\":{\"id\":\"2c0e633d-f35d-4324-ac7e-ce98cad8979e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"7358bffd-1d1e-4d80-8347-d77d0cad01de\",\"type\":\"DatetimeTicker\"}},\"id\":\"d9d45c34-7077-4d5c-b9e5-216e33f8f83c\",\"type\":\"DatetimeAxis\"},{\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"id\":\"ad579381-0e19-4b4a-be06-e8e801ed9609\",\"type\":\"AdaptiveTicker\"},{\"id\":\"7d2e32f7-4ff6-4d98-9915-958d41c18e22\",\"type\":\"AdaptiveTicker\"},{\"id\":\"378d0830-0f98-4dc0-9a67-c1691bea128f\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1f960838-0686-499b-9e9b-bdbf4737b570\",\"type\":\"DaysTicker\"},{\"id\":\"d31c5842-8fee-43c6-ab7e-d443a530f7ce\",\"type\":\"DaysTicker\"},{\"id\":\"6922ce0d-f8ea-4942-89c0-eb84887825e5\",\"type\":\"DaysTicker\"},{\"id\":\"f4861e84-f5f7-4c92-9057-3d53952f9fb2\",\"type\":\"DaysTicker\"},{\"id\":\"d1104731-ca48-4fc5-920a-8429288d5223\",\"type\":\"MonthsTicker\"},{\"id\":\"48aa786e-683a-4c47-a3d6-4d8f60776f65\",\"type\":\"MonthsTicker\"},{\"id\":\"b857c125-762b-4614-8149-ed3ac02c2110\",\"type\":\"MonthsTicker\"},{\"id\":\"28bfebaa-b0a2-46db-9022-7bb08a93890e\",\"type\":\"MonthsTicker\"},{\"id\":\"17171d0f-9fa0-43a8-a01e-e9facc042ca2\",\"type\":\"YearsTicker\"}]},\"id\":\"dda9974d-15f0-4b53-a3ac-fce6e9eab2e7\",\"type\":\"DatetimeTicker\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"22c0acd7-422f-46e1-980a-05421257faec\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"aa68c0cc-2f7e-4ad2-a901-97449c5b4f67\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"formatter\":{\"id\":\"37760e2b-6d57-4862-9def-2e7df37570f5\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"2c0e633d-f35d-4324-ac7e-ce98cad8979e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"cda11603-0ac4-44af-b142-c24e19c6017d\",\"type\":\"BasicTicker\"}},\"id\":\"722ca564-ea79-463e-99c7-13b831f6c117\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"49f96ceb-db08-4e28-b55b-ad94f0801b0a\"]},\"title\":\"Bokeh Application\",\"version\":\"0.13.0\"}};\n",
" var render_items = [{\"docid\":\"59f2ee12-6ea2-49bd-ab48-c8cfae3fecb4\",\"roots\":{\"49f96ceb-db08-4e28-b55b-ad94f0801b0a\":\"b55df3e9-3d02-494a-88cd-116b7e459185\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" clearInterval(timer);\n",
" }\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
" clearInterval(timer);\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);"
],
"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
"id": "49f96ceb-db08-4e28-b55b-ad94f0801b0a"
}
},
"output_type": "display_data"
}
],
"source": [
"show(tabs1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KtgHreahxmk5"
},
"source": [
"## 2.캔(중가)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"colab_type": "text",
"id": "_NYbxy5nxmk6"
},
"outputs": [],
"source": [
"### 1) 자료 불러오기"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "KwUArERixmk6"
},
"outputs": [],
"source": [
"##########\n",
"#1단계\n",
"#####\n",
"\n",
"# 변수지정\n",
"STAGE,PACKAGE,VOLUNM,LINE = \"1단계\",\"캔\",\"대\",\"중가\"\n",
"\n",
"# 쿼리설정\n",
"query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
" 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
" 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
" 'and B.VOLUNM2 = ' + \"'\" + VOLUNM + \"' \" +\n",
" 'and B.LINE = ' + \"'\" + LINE + \"' \" +\n",
" \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# 쿼리실행\n",
"df21 = pd.read_sql(query, con)\n",
"\n",
"\n",
"#날짜형식 지정\n",
"df21['DATE'] = pd.to_datetime(df21['DATE'])\n",
"\n",
"#숫자형식 지정\n",
"df21['PRICE'] = pd.Series(df21['PRICE']).str.replace(\",\", \"\")\n",
"df21['PRICE'] = pd.to_numeric(df21['PRICE'])\n",
"\n",
"#자료 피벗팅\n",
"# df = df.pivot('DATE','BRAND','PRICE')\n",
"df21 = pd.pivot_table(df21, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n",
"\n",
"\n",
"##########\n",
"#2단계\n",
"#####\n",
"\n",
"# 변수지정\n",
"STAGE,PACKAGE,VOLUNM,LINE = \"2단계\",\"캔\",\"대\",\"중가\"\n",
"\n",
"# 쿼리설정\n",
"query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
" 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
" 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
" 'and B.VOLUNM2 = ' + \"'\" + VOLUNM + \"' \" +\n",
" 'and B.LINE = ' + \"'\" + LINE + \"' \" +\n",
" \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# 쿼리실행\n",
"df22 = pd.read_sql(query, con)\n",
"\n",
"\n",
"#날짜형식 지정\n",
"df22['DATE'] = pd.to_datetime(df22['DATE'])\n",
"\n",
"#숫자형식 지정\n",
"df22['PRICE'] = pd.Series(df22['PRICE']).str.replace(\",\", \"\")\n",
"df22['PRICE'] = pd.to_numeric(df22['PRICE'])\n",
"\n",
"#자료 피벗팅\n",
"# df = df.pivot('DATE','BRAND','PRICE')\n",
"df22 = pd.pivot_table(df22, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n",
"\n",
"\n",
"##########\n",
"#3단계\n",
"#####\n",
"\n",
"# 변수지정\n",
"STAGE,PACKAGE,VOLUNM,LINE = \"3단계\",\"캔\",\"대\",\"중가\"\n",
"\n",
"# 쿼리설정\n",
"query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
" 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
" 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
" 'and B.VOLUNM2 = ' + \"'\" + VOLUNM + \"' \" +\n",
" 'and B.LINE = ' + \"'\" + LINE + \"' \" +\n",
" \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# 쿼리실행\n",
"df23 = pd.read_sql(query, con)\n",
"\n",
"\n",
"#날짜형식 지정\n",
"df23['DATE'] = pd.to_datetime(df23['DATE'])\n",
"\n",
"#숫자형식 지정\n",
"df23['PRICE'] = pd.Series(df23['PRICE']).str.replace(\",\", \"\")\n",
"df23['PRICE'] = pd.to_numeric(df23['PRICE'])\n",
"\n",
"#자료 피벗팅\n",
"# df = df.pivot('DATE','BRAND','PRICE')\n",
"df23 = pd.pivot_table(df23, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n",
"\n",
"\n",
"\n",
"##########\n",
"#4단계\n",
"#####\n",
"\n",
"# 변수지정\n",
"STAGE,PACKAGE,VOLUNM,LINE = \"4단계\",\"캔\",\"대\",\"중가\"\n",
"\n",
"# 쿼리설정\n",
"query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
" 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
" 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
" 'and B.VOLUNM2 = ' + \"'\" + VOLUNM + \"' \" +\n",
" 'and B.LINE = ' + \"'\" + LINE + \"' \" +\n",
" \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# 쿼리실행\n",
"df24 = pd.read_sql(query, con)\n",
"\n",
"#날짜형식 지정\n",
"df24['DATE'] = pd.to_datetime(df24['DATE'])\n",
"\n",
"#숫자형식 지정\n",
"df24['PRICE'] = pd.Series(df24['PRICE']).str.replace(\",\", \"\")\n",
"df24['PRICE'] = pd.to_numeric(df24['PRICE'])\n",
"\n",
"#자료 피벗팅\n",
"# df = df.pivot('DATE','BRAND','PRICE')\n",
"df24 = pd.pivot_table(df24, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "IYYMshH0xmk8",
"outputId": "46f6c066-8806-44a4-80ed-380c11eaacd4"
},
"outputs": [],
"source": [
"# df24.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"colab_type": "text",
"id": "pBD_71NSxmlA"
},
"outputs": [],
"source": [
"### 2) bokeh 그래프 그리기"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "85i5OI1cxmlA"
},
"outputs": [],
"source": [
"#bokeh 그래프 그리기\n",
"p21 = figure(x_axis_type ='datetime', plot_width=800, plot_height=400, title = VOLUNM+PACKAGE+\"(\" + LINE + \") : 1단계\")\n",
"p21.line(df21.index, df21['명작'], color ='darkorange', legend ='명작', line_width=2)\n",
"p21.line(df21.index, df21['XO'], color ='red', legend ='XO', line_width=2)\n",
"p21.line(df21.index, df21['트루맘'], color ='royalblue', legend ='트루맘', line_width=2)\n",
"p21.line(df21.index, df21['위드맘'], color ='olive', legend ='위드맘', line_width=2)\n",
"tab21 = Panel(child=p21, title=\"1단계\")\n",
"\n",
"p22 = figure(x_axis_type ='datetime', plot_width=800, plot_height=400, title = VOLUNM+PACKAGE+\"(\" + LINE + \") : 2단계\")\n",
"p22.line(df22.index, df22['명작'], color ='darkorange', legend ='명작', line_width=2)\n",
"p22.line(df22.index, df22['XO'], color ='red', legend ='XO', line_width=2)\n",
"p22.line(df22.index, df22['트루맘'], color ='royalblue', legend ='트루맘', line_width=2)\n",
"p22.line(df22.index, df22['위드맘'], color ='olive', legend ='위드맘', line_width=2)\n",
"tab22 = Panel(child=p22, title=\"2단계\")\n",
"\n",
"p23 = figure(x_axis_type ='datetime', plot_width=800, plot_height=400, title = VOLUNM+PACKAGE+\"(\" + LINE + \") : 3단계\")\n",
"p23.line(df23.index, df23['명작'], color ='darkorange', legend ='명작', line_width=2)\n",
"p23.line(df23.index, df23['XO'], color ='red', legend ='XO', line_width=2)\n",
"p23.line(df23.index, df23['트루맘'], color ='royalblue', legend ='트루맘', line_width=2)\n",
"p23.line(df23.index, df23['위드맘'], color ='olive', legend ='위드맘', line_width=2)\n",
"tab23 = Panel(child=p23, title=\"3단계\")\n",
"\n",
"p24 = figure(x_axis_type ='datetime', plot_width=800, plot_height=400, title = VOLUNM+PACKAGE+\"(\" + LINE + \") : 4단계\")\n",
"p24.line(df24.index, df24['명작'], color ='darkorange', legend ='명작', line_width=2)\n",
"p24.line(df24.index, df24['XO'], color ='royalblue', legend ='XO', line_width=2)\n",
"p24.line(df24.index, df24['트루맘'], color ='olive', legend ='트루맘', line_width=2)\n",
"# p24.line(df24.index, df24['위드맘'], color ='red', legend ='위드맘')\n",
"tab24 = Panel(child=p24, title=\"4단계\")\n",
"\n",
"tabs2 = Tabs(tabs=[ tab21, tab22, tab23, tab24 ])\n",
"\n",
"# print('대캔(고가)')\n",
"# show(tabs1)\n"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"대캔(중가) : 제품현황\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(VOLUNM+PACKAGE+'(' + LINE + ') : 제품현황')\n",
"Image(\"image/분유_캔(중가).PNG\")"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "IEYw9HrbxmlC",
"outputId": "f0143b8d-4e81-4b54-d0a9-1247011b1712"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" <div class=\"bk-root\" id=\"e31bf38a-e6b1-44e7-aefd-237be1171466\"></div>\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"(function(root) {\n",
" function embed_document(root) {\n",
" \n",
" var docs_json = 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" var render_items = [{\"docid\":\"48d46d74-52f6-4927-910e-ffa7f88eb675\",\"roots\":{\"8e768a13-3918-4147-bfca-494dd15c5729\":\"e31bf38a-e6b1-44e7-aefd-237be1171466\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" clearInterval(timer);\n",
" }\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
" clearInterval(timer);\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);"
],
"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
"id": "8e768a13-3918-4147-bfca-494dd15c5729"
}
},
"output_type": "display_data"
}
],
"source": [
"# print('대캔(중가)')\n",
"show(tabs2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "JWVSTBb4wsBp"
},
"source": [
"## 3.액상"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"colab_type": "text",
"id": "R9ROdIqYxLBH"
},
"outputs": [],
"source": [
"### 1) 자료 불러오기"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "by7wmQ7jwsBq"
},
"outputs": [],
"source": [
"##########\n",
"#1단계\n",
"#####\n",
"\n",
"# 변수지정\n",
"STAGE,PACKAGE = \"1단계\",\"병\"\n",
"\n",
"# 쿼리설정\n",
"query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
" 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
" 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
" \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# 쿼리실행\n",
"df31 = pd.read_sql(query, con)\n",
"\n",
"\n",
"#날짜형식 지정\n",
"df31['DATE'] = pd.to_datetime(df31['DATE'])\n",
"\n",
"#숫자형식 지정\n",
"df31['PRICE'] = pd.Series(df31['PRICE']).str.replace(\",\", \"\")\n",
"df31['PRICE'] = pd.to_numeric(df31['PRICE'])\n",
"\n",
"#자료 피벗팅\n",
"# df = df.pivot('DATE','BRAND','PRICE')\n",
"df31 = pd.pivot_table(df31, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n",
"\n",
"\n",
"##########\n",
"#2단계\n",
"#####\n",
"\n",
"# 변수지정\n",
"STAGE,PACKAGE = \"2단계\",\"병\"\n",
"\n",
"# 쿼리설정\n",
"query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
" 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
" 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
" \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# 쿼리실행\n",
"df32 = pd.read_sql(query, con)\n",
"\n",
"\n",
"#날짜형식 지정\n",
"df32['DATE'] = pd.to_datetime(df32['DATE'])\n",
"\n",
"#숫자형식 지정\n",
"df32['PRICE'] = pd.Series(df32['PRICE']).str.replace(\",\", \"\")\n",
"df32['PRICE'] = pd.to_numeric(df32['PRICE'])\n",
"\n",
"#자료 피벗팅\n",
"# df = df.pivot('DATE','BRAND','PRICE')\n",
"df32 = pd.pivot_table(df32, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n",
"\n",
"\n",
"##########\n",
"#3단계\n",
"#####\n",
"\n",
"# 변수지정\n",
"STAGE,PACKAGE = \"3단계\",\"병\"\n",
"\n",
"# 쿼리설정\n",
"query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
" 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
" 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
" \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# 쿼리실행\n",
"df33 = pd.read_sql(query, con)\n",
"\n",
"\n",
"#날짜형식 지정\n",
"df33['DATE'] = pd.to_datetime(df33['DATE'])\n",
"\n",
"#숫자형식 지정\n",
"df33['PRICE'] = pd.Series(df33['PRICE']).str.replace(\",\", \"\")\n",
"df33['PRICE'] = pd.to_numeric(df33['PRICE'])\n",
"\n",
"#자료 피벗팅\n",
"# df = df.pivot('DATE','BRAND','PRICE')\n",
"df33 = pd.pivot_table(df33, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n",
"\n",
"\n",
"\n",
"##########\n",
"#4단계\n",
"#####\n",
"\n",
"# # 변수지정\n",
"# STAGE,PACKAGE = \"4단계\",\"병\"\n",
"\n",
"# # 쿼리설정\n",
"# query = (\"SELECT A.DATE || ' ' || A.TIME as DATE, B.BRAND, min(A.PRICE) as PRICE FROM MILKPOWER_MIN A, MP_PRODUCT B where B.PRODUCT = A.PRODUCT \" +\n",
"# 'and B.STAGE = ' + \"'\" + STAGE + \"' \" + \n",
"# 'and A.PACKAGE = ' + \"'\" + PACKAGE + \"' \" +\n",
"# \"group by A.DATE, A.TIME, B.BRAND\")\n",
"\n",
"# # 쿼리실행\n",
"# df34 = pd.read_sql(query, con)\n",
"\n",
"# #날짜형식 지정\n",
"# df34['DATE'] = pd.to_datetime(df34['DATE'])\n",
"\n",
"# #숫자형식 지정\n",
"# df34['PRICE'] = pd.Series(df34['PRICE']).str.replace(\",\", \"\")\n",
"# df34['PRICE'] = pd.to_numeric(df34['PRICE'])\n",
"\n",
"# #자료 피벗팅\n",
"# # df = df.pivot('DATE','BRAND','PRICE')\n",
"# df34 = pd.pivot_table(df34, values = 'PRICE', index = 'DATE', columns= 'BRAND')\n"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "w2xqsp2AwsBt",
"outputId": "0bdecfaa-1ea5-4329-f9b0-fe468e4f586e"
},
"outputs": [],
"source": [
"# df31.tail(2)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"colab_type": "text",
"id": "PDFrkJWiwsBv"
},
"outputs": [],
"source": [
"### 2) poltly 자료로 변환"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "h525lrgrwsBw"
},
"outputs": [],
"source": [
"p31 = figure(x_axis_type ='datetime', plot_width=800, plot_height=400, title = VOLUNM+PACKAGE+\"(\" + LINE + \") : 1단계\")\n",
"p31.line(df31.index, df31['XO'], color ='red', legend ='XO', line_width=2)\n",
"p31.line(df31.index, df31['명작'], color ='darkorange', legend ='명작', line_width=2)\n",
"p31.line(df31.index, df31['베비언스'], color ='cyan', legend ='베비언스', line_width=2)\n",
"p31.line(df31.index, df31['베비언스 산양'], color ='darkgreen', legend ='베비언스 산양', line_width=2)\n",
"p31.line(df31.index, df31['아이엠마더'], color ='sienna', legend ='아이엠마더', line_width=2)\n",
"p31.line(df31.index, df31['액상'], color ='teal', legend ='액상', line_width=2)\n",
"p31.line(df31.index, df31['위드맘'], color ='olive', legend ='위드맘', line_width=2)\n",
"tab31 = Panel(child=p31, title=\"1단계\")\n",
"\n",
"p32 = figure(x_axis_type ='datetime', plot_width=800, plot_height=400, title = VOLUNM+PACKAGE+\"(\" + LINE + \") : 2단계\")\n",
"# p32.line(df32.index, df32['XO'], color ='red', legend ='XO')\n",
"p32.line(df32.index, df32['명작'], color ='darkorange', legend ='명작', line_width=2)\n",
"p32.line(df32.index, df32['베비언스'], color ='cyan', legend ='베비언스', line_width=2)\n",
"p32.line(df32.index, df32['베비언스 산양'], color ='darkgreen', legend ='베비언스 산양', line_width=2)\n",
"p32.line(df32.index, df32['아이엠마더'], color ='sienna', legend ='아이엠마더', line_width=2)\n",
"p32.line(df32.index, df32['액상'], color ='teal', legend ='액상', line_width=2)\n",
"p32.line(df32.index, df32['위드맘'], color ='olive', legend ='위드맘', line_width=2)\n",
"p32.line(df32.index, df32['트루맘'], color ='royalblue', legend ='트루맘', line_width=2)\n",
"tab32 = Panel(child=p32, title=\"2단계\")\n",
"\n",
"p33 = figure(x_axis_type ='datetime', plot_width=800, plot_height=400, title = VOLUNM+PACKAGE+\"(\" + LINE + \") : 3단계\")\n",
"# p33.line(df33.index, df33['XO'], color ='red', legend ='XO')\n",
"p33.line(df33.index, df33['명작'], color ='darkorange', legend ='명작', line_width=2)\n",
"p33.line(df33.index, df33['베비언스'], color ='cyan', legend ='베비언스', line_width=2)\n",
"p33.line(df33.index, df33['베비언스 산양'], color ='darkgreen', legend ='베비언스 산양', line_width=2)\n",
"p33.line(df33.index, df33['아이엠마더'], color ='sienna', legend ='아이엠마더', line_width=2)\n",
"# p33.line(df33.index, df33['액상'], color ='red', legend ='액상')\n",
"# p33.line(df33.index, df33['위드맘'], color ='olive', legend ='위드맘')\n",
"p33.line(df33.index, df33['트루맘'], color ='royalblue', legend ='트루맘', line_width=2)\n",
"tab33 = Panel(child=p33, title=\"3단계\")\n",
"\n",
"tabs3 = Tabs(tabs=[ tab31, tab32, tab33])\n"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"액상\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('액상')\n",
"Image(\"image/분유_액상.PNG\")"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "hTXKca4jxmlQ",
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" <div class=\"bk-root\" id=\"cf9d987c-ab37-49e6-b680-0f04bc717f12\"></div>\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"(function(root) {\n",
" function embed_document(root) {\n",
" \n",
" var docs_json = 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