Change encoding to include BOM in read_csv in initial settings cell.

Author: KazuhiroM

docker/doc/preprocess_knock_Python.html

@@ -7643,28 +7643,7 @@ <h1 id="%E6%BC%94%E7%BF%92%E5%95%8F%E9%A1%8C">演習問題<a class="anchor-link"
 <div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">dtype</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s1">'customer_id'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'gender_cd'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'postal_cd'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'application_store_cd'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'status_cd'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'category_major_cd'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'category_medium_cd'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'category_small_cd'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'product_cd'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'store_cd'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'prefecture_cd'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'tel_no'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'postal_cd'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="s1">'street'</span><span class="p">:</span> <span class="nb">str</span>
-<span class="p">}</span>
-<span class="n">df_customer</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"./data/customer.csv"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
-<span class="n">df_category</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"./data/category.csv"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
-<span class="n">df_product</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"./data/product.csv"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
-<span class="n">df_receipt</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"./data/receipt.csv"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
-<span class="n">df_store</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"./data/store.csv"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
-<span class="n">df_geocode</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"./data/geocode.csv"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+<div class="highlight hl-ipython3"><pre><span></span> 
 </pre></div>
 </div>
 </div>
@@ -7692,7 +7671,7 @@ <h1 id="%E6%BC%94%E7%BF%92%E5%95%8F%E9%A1%8C">演習問題<a class="anchor-link"
 <div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_category</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<div class="highlight hl-ipython3"><pre><span></span> 
 </pre></div>
 </div>
 </div>

docker/work/preprocess_knock_Python.ipynb

@@ -107,30 +107,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "dtype = {\n",
-    "    'customer_id': str,\n",
-    "    'gender_cd': str,\n",
-    "    'postal_cd': str,\n",
-    "    'application_store_cd': str,\n",
-    "    'status_cd': str,\n",
-    "    'category_major_cd': str,\n",
-    "    'category_medium_cd': str,\n",
-    "    'category_small_cd': str,\n",
-    "    'product_cd': str,\n",
-    "    'store_cd': str,\n",
-    "    'prefecture_cd': str,\n",
-    "    'tel_no': str,\n",
-    "    'postal_cd': str,\n",
-    "    'street': str\n",
-    "}\n",
-    "df_customer = pd.read_csv(\"./data/customer.csv\", dtype=dtype)\n",
-    "df_category = pd.read_csv(\"./data/category.csv\", dtype=dtype)\n",
-    "df_product = pd.read_csv(\"./data/product.csv\", dtype=dtype)\n",
-    "df_receipt = pd.read_csv(\"./data/receipt.csv\", dtype=dtype)\n",
-    "df_store = pd.read_csv(\"./data/store.csv\", dtype=dtype)\n",
-    "df_geocode = pd.read_csv(\"./data/geocode.csv\", dtype=dtype)\n"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -145,9 +122,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "df_category.columns[0]"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",