CH4-19_徐子皓_無母數檢定 - Python實戰：如果蒐集樣本太少，又想優化廣告成效？..ipynb

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   "source": [
    "# 無母數檢定 - Python實戰：如果蒐集樣本太少，又想優化廣告成效？"
   ]
  },
  {
   "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/3ce91f5d9a2e/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  一、常態檢查"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 將資料依據廣告類別分類"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ad1_list = data[data['廣告代號all'] == 'critei']['利潤'].tolist()\n",
    "ad2_list = data[data['廣告代號all'] == 'KDP']['利潤'].tolist()\n",
    "ad3_list = data[data['廣告代號all'] == '自然流量']['利潤'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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      "text/plain": "[181, 159, 181, 90, 138, 144, 181, 181, 212]"
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     "metadata": {},
     "execution_count": 21
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   "source": [
    "ad1_list[0:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[253, 253, 200, 228, 313, 174, 228, 228, 122]"
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     "metadata": {},
     "execution_count": 22
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   "source": [
    "ad2_list[0:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[145, 181, 145, 138, 82, 145, 145, 90, 206]"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "ad3_list[0:9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. shapiro常態檢定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "ShapiroResult(statistic=0.9818183183670044, pvalue=1.2465230236902158e-11)"
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "import scipy.stats\n",
    "scipy.stats.shapiro(ad1_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "ShapiroResult(statistic=0.9621795415878296, pvalue=3.1705360319920887e-21)"
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "scipy.stats.shapiro(ad2_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "ShapiroResult(statistic=0.9822685122489929, pvalue=2.279967656292392e-21)"
     },
     "metadata": {},
     "execution_count": 29
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   ],
   "source": [
    "scipy.stats.shapiro(ad3_list)"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 事後檢定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "KruskalResult(statistic=4.584369300454998, pvalue=0.10104547153612356)"
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "from scipy.stats.mstats import kruskalwallis\n",
    "kruskalwallis(ad1_list, ad2_list, ad3_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、視覺化分析結果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 導入基本參數"
   ]
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   ],
   "source": [
    "import plotly.offline as py\n",
    "import plotly.graph_objects as go\n",
    "from numpy import mean\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(\n",
    "            x= ['critei','KDP','自然流量'],\n",
    "            y= [mean(ad1_list), mean(ad2_list), mean(ad3_list)],\n",
    "            mode=\"lines+markers\",\n",
    "            textfont=dict(\n",
    "            family=\"sans serif\",\n",
    "            size=16,\n",
    "            color=\"royalblue\"),    \n",
    "            line=dict(color='royalblue', width=2),\n",
    "            ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 加入佈景主題設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "line": {
          "color": "royalblue",
          "width": 2
         },
         "mode": "lines+markers",
         "textfont": {
          "color": "royalblue",
          "family": "sans serif",
          "size": 16
         },
         "type": "scatter",
         "x": [
          "critei",
          "KDP",
          "自然流量"
         ],
         "y": [
          228.21089494163425,
          236.7099832682655,
          233.58544973544974
         ]
        }
       ],
       "layout": {
        "font": {
         "color": "lightslategrey",
         "family": "Courier New, monospace",
         "size": 20
        },
        "height": 960,
        "template": {
         "data": {
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           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
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            "type": "barpolar"
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          "carpet": [
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            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
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            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
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            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
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            "colorscale": [
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              0.8888888888888888,
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            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
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          ],
          "heatmap": [
           {
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             "ticks": ""
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            "colorscale": [
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            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
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            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
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            "colorscale": [
             [
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             [
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              "#46039f"
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             [
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              "#7201a8"
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            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
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            ],
            "type": "histogram2dcontour"
           }
          ],
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            "colorbar": {
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             "ticks": ""
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            "type": "mesh3d"
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          ],
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            "type": "parcoords"
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          "pie": [
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            "automargin": true,
            "type": "pie"
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          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
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              "ticks": ""
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            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
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            "marker": {
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            "type": "scatter3d"
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          ],
          "scattercarpet": [
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            "type": "scattercarpet"
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          "scattergeo": [
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            "type": "scattergl"
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          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
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             "line": {
              "color": "white"
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            "header": {
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              "color": "#C8D4E3"
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         "layout": {
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           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "<b>廣告效益分析</b>",
         "x": 0.5,
         "xanchor": "center",
         "y": 0.95
        },
        "width": 1800,
        "xaxis": {
         "tickmode": "linear",
         "title": {
          "text": "廣告"
         }
        },
        "yaxis": {
         "title": {
          "text": "平均消費金額"
         }
        }
       }
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "fig.update_layout(\n",
    "    title={\n",
    "        'text': \"<b>廣告效益分析</b>\",\n",
    "        'y':0.95,\n",
    "        'x':0.5,\n",
    "        'xanchor': 'center',},\n",
    "    yaxis_title='平均消費金額',\n",
    "    xaxis={\n",
    "        'title': '廣告',\n",
    "        'tickmode': 'linear'\n",
    "        },\n",
    "    width=1800,\n",
    "    height=960,\n",
    "    font=dict(\n",
    "        family=\"Courier New, monospace\",\n",
    "        size=20,\n",
    "        color=\"lightslategrey\"\n",
    "    )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 將產出圖片另存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 另存互動式網頁檔案\n",
    "py.plot(fig, filename='無母數檢定－廣告效益分析', auto_open=True)\n",
    "# 另存圖檔\n",
    "fig.write_image(\"C:/Users/user/Desktop/無母數檢定1.1/無母數檢定－廣告效益分析.png\")"
   ]
  }
 ]
}