diff --git a/Code/Day 1_Data_Preprocessing.ipynb b/Code/Day 1_Data_Preprocessing.ipynb
index 8bd9e38..6ddcbda 100644
--- a/Code/Day 1_Data_Preprocessing.ipynb	
+++ b/Code/Day 1_Data_Preprocessing.ipynb	
@@ -11,6 +11,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "搭建anaconda环境，参考 https://zhuanlan.zhihu.com/p/33358809\n",
+    "\n",
     "## 第一步：导入需要的库\n",
     "这两个是我们每次都需要导入的库。NumPy包含数学计算函数。Pandas用于导入和管理数据集。"
    ]
@@ -18,9 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -63,7 +63,9 @@
    ],
    "source": [
     "dataset = pd.read_csv('../datasets/Data.csv')\n",
+    "# 不包括最后一列的所有列\n",
     "X = dataset.iloc[ : , :-1].values\n",
+    "#取最后一列\n",
     "Y = dataset.iloc[ : , 3].values\n",
     "print(\"Step 2: Importing dataset\")\n",
     "print(\"X\")\n",
@@ -108,6 +110,7 @@
    ],
    "source": [
     "from sklearn.preprocessing import Imputer\n",
+    "# axis=0表示按列进行\n",
     "imputer = Imputer(missing_values = \"NaN\", strategy = \"mean\", axis = 0)\n",
     "imputer = imputer.fit(X[ : , 1:3])\n",
     "X[ : , 1:3] = imputer.transform(X[ : , 1:3])\n",
@@ -138,26 +141,26 @@
       "---------------------\n",
       "Step 4: Encoding categorical data\n",
       "X\n",
-      "[[  1.00000000e+00   0.00000000e+00   0.00000000e+00   4.40000000e+01\n",
-      "    7.20000000e+04]\n",
-      " [  0.00000000e+00   0.00000000e+00   1.00000000e+00   2.70000000e+01\n",
-      "    4.80000000e+04]\n",
-      " [  0.00000000e+00   1.00000000e+00   0.00000000e+00   3.00000000e+01\n",
-      "    5.40000000e+04]\n",
-      " [  0.00000000e+00   0.00000000e+00   1.00000000e+00   3.80000000e+01\n",
-      "    6.10000000e+04]\n",
-      " [  0.00000000e+00   1.00000000e+00   0.00000000e+00   4.00000000e+01\n",
-      "    6.37777778e+04]\n",
-      " [  1.00000000e+00   0.00000000e+00   0.00000000e+00   3.50000000e+01\n",
-      "    5.80000000e+04]\n",
-      " [  0.00000000e+00   0.00000000e+00   1.00000000e+00   3.87777778e+01\n",
-      "    5.20000000e+04]\n",
-      " [  1.00000000e+00   0.00000000e+00   0.00000000e+00   4.80000000e+01\n",
-      "    7.90000000e+04]\n",
-      " [  0.00000000e+00   1.00000000e+00   0.00000000e+00   5.00000000e+01\n",
-      "    8.30000000e+04]\n",
-      " [  1.00000000e+00   0.00000000e+00   0.00000000e+00   3.70000000e+01\n",
-      "    6.70000000e+04]]\n",
+      "[[1.00000000e+00 0.00000000e+00 0.00000000e+00 4.40000000e+01\n",
+      "  7.20000000e+04]\n",
+      " [0.00000000e+00 0.00000000e+00 1.00000000e+00 2.70000000e+01\n",
+      "  4.80000000e+04]\n",
+      " [0.00000000e+00 1.00000000e+00 0.00000000e+00 3.00000000e+01\n",
+      "  5.40000000e+04]\n",
+      " [0.00000000e+00 0.00000000e+00 1.00000000e+00 3.80000000e+01\n",
+      "  6.10000000e+04]\n",
+      " [0.00000000e+00 1.00000000e+00 0.00000000e+00 4.00000000e+01\n",
+      "  6.37777778e+04]\n",
+      " [1.00000000e+00 0.00000000e+00 0.00000000e+00 3.50000000e+01\n",
+      "  5.80000000e+04]\n",
+      " [0.00000000e+00 0.00000000e+00 1.00000000e+00 3.87777778e+01\n",
+      "  5.20000000e+04]\n",
+      " [1.00000000e+00 0.00000000e+00 0.00000000e+00 4.80000000e+01\n",
+      "  7.90000000e+04]\n",
+      " [0.00000000e+00 1.00000000e+00 0.00000000e+00 5.00000000e+01\n",
+      "  8.30000000e+04]\n",
+      " [1.00000000e+00 0.00000000e+00 0.00000000e+00 3.70000000e+01\n",
+      "  6.70000000e+04]]\n",
       "Y\n",
       "[0 1 0 0 1 1 0 1 0 1]\n"
      ]
@@ -323,7 +326,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,