{ "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6-final" }, "orig_nbformat": 2, "kernelspec": { "name": "python_defaultSpec_1600778749683", "display_name": "Python 3.6.6 64-bit ('win': conda)" } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "source": [ "# 單因子變異數分析 - Python實戰：如何決定多廣告的優化策略？" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "#### 作者：徐子皓(臺灣行銷研究特邀作者)\n", "#### 完整文章介紹鏈接：https://medium.com/p/7c99643043b0/" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "## 一、事前檢驗" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1. shapiro常態性檢定" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "ShapiroResult(statistic=0.9915865659713745, pvalue=0.6819638013839722)" }, "metadata": {}, "execution_count": 7 } ], "source": [ "import scipy.stats as st\n", "st.shapiro(alist)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "ShapiroResult(statistic=0.9929247498512268, pvalue=0.805463969707489)" }, "metadata": {}, "execution_count": 8 } ], "source": [ "st.shapiro(blist)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "ShapiroResult(statistic=0.9802003502845764, pvalue=0.07433854043483734)" }, "metadata": {}, "execution_count": 9 } ], "source": [ "st.shapiro(clist)" ] }, { "source": [ "### 2. levene同質性檢定" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "LeveneResult(statistic=0.6891243668404422, pvalue=0.5026819827621525)" }, "metadata": {}, "execution_count": 10 } ], "source": [ "st.levene(alist, blist, clist, center='mean')" ] }, { "source": [ "## 二、變異數分析" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "0.0955577857122512" }, "metadata": {}, "execution_count": 11 } ], "source": [ "f_value, p_value = st.f_oneway(alist, blist, clist)\n", "p_value" ] }, { "source": [ "## 三、事後檢定" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1. pairwise_tukey事後檢定" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": " A B mean(A) mean(B) diff se T p-tukey \\\n0 廣告1 廣告2 17.135167 15.592417 1.54275 0.781281 1.974642 0.118914 \n1 廣告1 廣告3 17.135167 15.748167 1.38700 0.781281 1.775290 0.178294 \n2 廣告2 廣告3 15.592417 15.748167 -0.15575 0.781281 -0.199352 0.900000 \n\n hedges \n0 0.254121 \n1 0.228466 \n2 -0.025655 ", "text/html": "
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ABmean(A)mean(B)diffseTp-tukeyhedges
0廣告1廣告217.13516715.5924171.542750.7812811.9746420.1189140.254121
1廣告1廣告317.13516715.7481671.387000.7812811.7752900.1782940.228466
2廣告2廣告315.59241715.748167-0.155750.781281-0.1993520.900000-0.025655
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" }, "metadata": {}, "execution_count": 13 } ], "source": [ "from pingouin import pairwise_tukey\n", "m_comp = pairwise_tukey(data=data, dv='消費金額', between='廣告')\n", "m_comp" ] }, { "source": [ "### 2. 整理成可參考報表" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": " A B diff se\n0 廣告1 廣告2 1.54275 0.781281\n1 廣告1 廣告3 1.38700 0.781281\n2 廣告2 廣告3 -0.15575 0.781281", "text/html": "
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ABdiffse
0廣告1廣告21.542750.781281
1廣告1廣告31.387000.781281
2廣告2廣告3-0.155750.781281
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" }, "metadata": {}, "execution_count": 15 } ], "source": [ "table = m_comp.drop(columns = ['mean(A)', 'mean(B)', 'T', 'p-tukey', 'hedges'])\n", "table" ] }, { "source": [ "## 四、進階資料處理" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1. 計算反轉資料" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# 「A」欄反轉資料\n", "add_A = table['B'].tolist()\n", "# 「B」欄反轉資料\n", "add_B = table['A'].tolist()\n", "# 「diff」欄反轉資料\n", "diff = (table['diff'] - 2 * table['diff']).tolist()\n", "# 「se」欄反轉資料\n", "se = table['se'].tolist()\n", "# 將反轉資料合併\n", "table2 = pd.DataFrame(zip(add_A, add_B, diff, se), columns = ['A', 'B', 'diff', 'se'])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": " A B diff se\n0 廣告2 廣告1 -1.54275 0.781281\n1 廣告3 廣告1 -1.38700 0.781281\n2 廣告3 廣告2 0.15575 0.781281", "text/html": "
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ABdiffse
0廣告2廣告1-1.542750.781281
1廣告3廣告1-1.387000.781281
2廣告3廣告20.155750.781281
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" }, "metadata": {}, "execution_count": 18 } ], "source": [ "table2" ] }, { "source": [ "### 2. 將資料整併成new_table" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": " A B diff se\n0 廣告1 廣告2 1.54275 0.781281\n1 廣告1 廣告3 1.38700 0.781281\n2 廣告2 廣告3 -0.15575 0.781281\n3 廣告2 廣告1 -1.54275 0.781281\n4 廣告3 廣告1 -1.38700 0.781281\n5 廣告3 廣告2 0.15575 0.781281", "text/html": "
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ABdiffse
0廣告1廣告21.542750.781281
1廣告1廣告31.387000.781281
2廣告2廣告3-0.155750.781281
3廣告2廣告1-1.542750.781281
4廣告3廣告1-1.387000.781281
5廣告3廣告20.155750.781281
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" }, "metadata": {}, "execution_count": 19 } ], "source": [ "new_table = pd.concat([table, table2], ignore_index=True)\n", "new_table" ] }, { "source": [ "### 3. 計算資料上下界" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": " A B diff se 上界 下界\n0 廣告1 廣告2 1.54275 0.781281 3.074061 0.011439\n1 廣告1 廣告3 1.38700 0.781281 2.918311 -0.144311\n2 廣告2 廣告3 -0.15575 0.781281 1.375561 -1.687061\n3 廣告2 廣告1 -1.54275 0.781281 -0.011439 -3.074061\n4 廣告3 廣告1 -1.38700 0.781281 0.144311 -2.918311\n5 廣告3 廣告2 0.15575 0.781281 1.687061 -1.375561", "text/html": "
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ABdiffse上界下界
0廣告1廣告21.542750.7812813.0740610.011439
1廣告1廣告31.387000.7812812.918311-0.144311
2廣告2廣告3-0.155750.7812811.375561-1.687061
3廣告2廣告1-1.542750.781281-0.011439-3.074061
4廣告3廣告1-1.387000.7812810.144311-2.918311
5廣告3廣告20.155750.7812811.687061-1.375561
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" }, "metadata": {}, "execution_count": 20 } ], "source": [ "new_table['上界'] = new_table['diff'] + new_table['se']*1.96\n", "new_table['下界'] = new_table['diff'] - new_table['se']*1.96\n", "new_table" ] }, { "source": [ "### 4. np.sign用法示意" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "1" }, "metadata": {}, "execution_count": 21 } ], "source": [ "import numpy as np\n", "np.sign(12)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "-1.0" }, "metadata": {}, "execution_count": 22 } ], "source": [ "np.sign(-5.6)" ] }, { "source": [ "### 5. 判斷資料是否顯著" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "justice = []\n", "for i in range(0,new_table.shape[0]):\n", " a = np.sign(new_table.iloc[i,4]) # 上界正負數判斷\n", " b = np.sign(new_table.iloc[i,5]) # 下界正負數判斷\n", " if a == b:\n", " justice.append('Yes')\n", " else:\n", " justice.append('No')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "['Yes', 'No', 'No', 'Yes', 'No', 'No']" }, "metadata": {}, "execution_count": 24 } ], "source": [ "justice" ] }, { "source": [ "### 6. 將「是否顯著」結果回存至new_table中" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": " A B diff se 上界 下界 是否顯著\n0 廣告1 廣告2 1.54275 0.781281 3.074061 0.011439 Yes\n1 廣告1 廣告3 1.38700 0.781281 2.918311 -0.144311 No\n2 廣告2 廣告3 -0.15575 0.781281 1.375561 -1.687061 No\n3 廣告2 廣告1 -1.54275 0.781281 -0.011439 -3.074061 Yes\n4 廣告3 廣告1 -1.38700 0.781281 0.144311 -2.918311 No\n5 廣告3 廣告2 0.15575 0.781281 1.687061 -1.375561 No", "text/html": "
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ABdiffse上界下界是否顯著
0廣告1廣告21.542750.7812813.0740610.011439Yes
1廣告1廣告31.387000.7812812.918311-0.144311No
2廣告2廣告3-0.155750.7812811.375561-1.687061No
3廣告2廣告1-1.542750.781281-0.011439-3.074061Yes
4廣告3廣告1-1.387000.7812810.144311-2.918311No
5廣告3廣告20.155750.7812811.687061-1.375561No
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" }, "metadata": {}, "execution_count": 25 } ], "source": [ "new_table['是否顯著'] = justice\n", "new_table" ] }, { "source": [ "## 五、資料視覺化\t" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1. 基本資料輸入" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import plotly.offline as py\n", "import plotly.graph_objects as go\n", "fig = go.Figure()\n", "for i in range(0, new_table.shape[0]):\n", " if new_table.iloc[i,6] == 'Yes':\n", " color = 'firebrick'\n", " name = '顯著'\n", " else:\n", " color = 'green'\n", " name = '不顯著'\n", " fig.add_trace(go.Scatter(\n", " x = [new_table.iloc[i,5], new_table.iloc[i,2], new_table.iloc[i,4]],\n", " y = [new_table.iloc[i,0] + '-' + new_table.iloc[i,1], new_table.iloc[i,0] + '-' + new_table.iloc[i,1], new_table.iloc[i,0] + '-' + new_table.iloc[i,1]],\n", " mode = \"lines+markers\",\n", " textfont=dict(\n", " family=\"sans serif\",\n", " size=16,\n", " color=color), \n", " line=dict(color=color, width=2),\n", " name = name,\n", " legendgroup = name,\n", " ))" ] }, { "source": [ "### 2. 設定佈景主題" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "legendgroup": "顯著", "line": { "color": "firebrick", "width": 2 }, "mode": "lines+markers", "name": "顯著", "textfont": { "color": "firebrick", "family": "sans serif", "size": 16 }, "type": "scatter", "x": [ 0.011439440747863072, 1.5427500000000105, 3.0740605592521577 ], "y": [ "廣告1-廣告2", "廣告1-廣告2", "廣告1-廣告2" ] }, { "legendgroup": "不顯著", "line": { "color": "green", "width": 2 }, "mode": "lines+markers", "name": "不顯著", "textfont": { "color": "green", "family": "sans serif", "size": 16 }, "type": "scatter", "x": [ -0.14431055925213632, 1.3870000000000111, 2.9183105592521583 ], "y": [ "廣告1-廣告3", "廣告1-廣告3", "廣告1-廣告3" ] }, { "legendgroup": "不顯著", "line": { "color": "green", "width": 2 }, "mode": "lines+markers", "name": "不顯著", "textfont": { "color": "green", "family": "sans serif", "size": 16 }, "type": "scatter", "x": [ -1.6870605592521468, -0.1557499999999994, 1.375560559252148 ], "y": [ "廣告2-廣告3", "廣告2-廣告3", "廣告2-廣告3" ] }, { "legendgroup": "顯著", "line": { "color": "firebrick", "width": 2 }, "mode": "lines+markers", "name": "顯著", "textfont": { "color": "firebrick", "family": "sans serif", "size": 16 }, "type": "scatter", "x": [ -3.0740605592521577, -1.5427500000000105, -0.011439440747863072 ], "y": [ "廣告2-廣告1", "廣告2-廣告1", "廣告2-廣告1" ] }, { "legendgroup": "不顯著", "line": { "color": "green", "width": 2 }, "mode": "lines+markers", "name": "不顯著", "textfont": { "color": "green", "family": "sans serif", "size": 16 }, "type": "scatter", "x": [ -2.9183105592521583, -1.3870000000000111, 0.14431055925213632 ], "y": [ "廣告3-廣告1", "廣告3-廣告1", "廣告3-廣告1" ] }, { "legendgroup": "不顯著", "line": { "color": "green", "width": 2 }, "mode": "lines+markers", "name": "不顯著", "textfont": { "color": "green", "family": "sans serif", "size": 16 }, "type": "scatter", "x": [ -1.375560559252148, 0.1557499999999994, 1.6870605592521468 ], "y": [ "廣告3-廣告2", "廣告3-廣告2", "廣告3-廣告2" ] } ], "layout": { "boxmode": "group", "font": { "color": "lightslategrey", "family": "Courier New, monospace", "size": 20 }, "height": 960, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, 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"standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "One-Way ANOVA 廣告效益分析", "x": 0.5, "xanchor": "center", "y": 0.95 }, "width": 1800 } } }, "metadata": {} } ], "source": [ "fig.update_layout(\n", " title={\n", " 'text': \"One-Way ANOVA 廣告效益分析\",\n", " 'y':0.95,\n", " 'x':0.5,\n", " 'xanchor': 'center',},\n", " width=1800,\n", " height=960,\n", " boxmode='group',\n", " font=dict(\n", " family=\"Courier New, monospace\",\n", " size=20,\n", " color=\"lightslategrey\"\n", " )\n", " ) " ] }, { "source": [ "### 3. 將產出圖片另存" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 另存互動式網頁\n", "py.plot(fig, filename='One-Way ANOVA 廣告效益分析', auto_open=True)\n", "# 另存.png圖檔\n", "fig.write_image(\"C:/Users/user/Desktop/單因子變異數分析1.5/One-Way ANOVA 廣告效益分析.png\")" ] } ] }