Merge pull request #29 from ev-br/patch-1

mention `np.errstate` context manager in exercise 31.

100 Numpy exercises.ipynb

@@ -477,8 +477,8 @@
    "outputs": [],
    "source": [
     "Z = np.dot(np.ones((5,3)), np.ones((3,2)))\n",
-    "print(Z)",
-    "\n\n",
+    "print(Z)\n",
+    "\n",
     "# Alternative solution, in Python 3.5 and above\n",
     "Z = np.ones((5,3)) @ np.ones((3,2))"
    ]
@@ -628,10 +628,16 @@
    "source": [
     "# Suicide mode on\n",
     "defaults = np.seterr(all=\"ignore\")\n",
-    "Z = np.ones(1)/0\n",
+    "Z = np.ones(1) / 0\n",
     "\n",
     "# Back to sanity\n",
-    "_ = np.seterr(**defaults)"
+    "_ = np.seterr(**defaults)\n",
+    "\n",
+    "An equivalent way, with a context manager:\n",
+    "\n",
+    "```python\n",
+    "with np.errstate(divide='ignore'):\n",
+    "    Z = np.ones(1) / 0"
    ]
   },
   {
@@ -1439,11 +1445,11 @@
    "outputs": [],
    "source": [
     "A = np.random.randint(0,10,(3,4,3,4))\n",
-	"# solution by passing a tuple of axes (introduced in numpy 1.7.0)\n",
-	"sum = A.sum(axis=(-2,-1))\n",
-	"print(sum)\n",
-	"# solution by flattening the last two dimensions into one\n",
-	"# (useful for functions that don't accept tuples for axis argument)\n",
+    "# solution by passing a tuple of axes (introduced in numpy 1.7.0)\n",
+    "sum = A.sum(axis=(-2,-1))\n",
+    "print(sum)\n",
+    "# solution by flattening the last two dimensions into one\n",
+    "# (useful for functions that don't accept tuples for axis argument)\n",
     "sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1)\n",
     "print(sum)"
    ]
@@ -1471,7 +1477,7 @@
     "D_counts = np.bincount(S)\n",
     "D_means = D_sums / D_counts\n",
     "print(D_means)\n",
-	"\n",
+    "\n",
     "# Pandas solution as a reference due to more intuitive code\n",
     "import pandas as pd\n",
     "print(pd.Series(D).groupby(S).mean())"
@@ -2334,7 +2340,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.1"
   }
  },
  "nbformat": 4,

100 Numpy exercises.md

@@ -282,12 +282,19 @@ print(np.intersect1d(Z1,Z2))
 ```python
 # Suicide mode on
 defaults = np.seterr(all="ignore")
-Z = np.ones(1)/0
+Z = np.ones(1) / 0
 
 # Back to sanity
 _ = np.seterr(**defaults)
 ```
 
+An equivalent way, with a context manager:
+
+```python
+with np.errstate(divide='ignore'):
+    Z = np.ones(1) / 0
+```
+
 #### 32. Is the following expressions true? (★☆☆)