Merge pull request #142 from silx-kit/review_henri

Review henri

Author: payno

python/numpy/introduction_to_numpy.ipynb

@@ -1434,10 +1434,10 @@
    },
    "outputs": [],
    "source": [
-    "print(\"a:\", a)\n",
-    "print(\"b:\", b)\n",
-    "print(\"c:\", c)\n",
-    "print(\"d:\", d)"
+    "print(\"a:\", a, sep=\"\\n\")\n",
+    "print(\"b:\", b, sep=\"\\n\")\n",
+    "print(\"c:\", c, sep=\"\\n\")\n",
+    "print(\"d:\", d, sep=\"\\n\")"
    ]
   },
   {
@@ -1449,7 +1449,7 @@
     "tags": []
    },
    "source": [
-    "Be careful when using `numpy.copy` as it is shallow, and it will not copy object elements within arrays. For these corner cases, you need to use `copy.deepcopy`."
+    "Be careful when using `numpy.copy` as it is shallow, and it will not copy object elements within arrays. For these corner cases, you need to use `copy.deepcopy`. See the [python copy module](https://docs.python.org/3/library/copy.html)"
    ]
   },
   {
@@ -1491,7 +1491,7 @@
     "tags": []
    },
    "source": [
-    "The ``dtype`` attribute identifies the type of the elements of the array."
+    "The [``dtype``](https://numpy.org/doc/stable/reference/arrays.dtypes.html) attribute identifies the type of the elements of the array."
    ]
   },
   {
@@ -1594,13 +1594,13 @@
    "source": [
     "### More array attributes\n",
     "\n",
-    "* ``ndim``: Number of dimensions\n",
-    "* ``size``: Total number of elements\n",
-    "* ``itemsize``: Size of a single item (in bytes))\n",
-    "* ``strides``: Bytes to step in each dimension\n",
-    "* ``flags``: Contiguity of the data in the buffer\n",
-    "* ``nbytes``: Size in bytes occupied in memory\n",
-    "* ``data``: Read/write buffer containing the data"
+    "* [``ndim``](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.ndim.html): Number of dimensions\n",
+    "* [``size``](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.size.html): Total number of elements\n",
+    "* [``itemsize``](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.itemsize.html): Size of a single item (in bytes))\n",
+    "* [``strides``](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html): Bytes to step in each dimension\n",
+    "* [``flags``](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html): Contiguity of the data in the buffer\n",
+    "* [``nbytes``](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.nbytes.html): Size in bytes occupied in memory\n",
+    "* [``data``](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.data.html): Read/write buffer containing the data"
    ]
   },
   {
@@ -1953,7 +1953,7 @@
    "source": [
     "## Exercise \n",
     "\n",
-    "Suppose we want to shrink the physical size of a 1000x1000 gray-value image by a factor 4, without changing the type of the pixels.\n",
+    "Suppose we want to shrink the physical size of a 100x100 gray-value image by a factor 4, without changing the type of the pixels.\n",
     "A natural approach is to reduce the number of pixels by a factor 2 in each dimension. This operation is called **binning**. It consists in replacing each 2x2 square in the image by one single pixel value. The next question is: which value should be assigned to each pixel in the new image.\n",
     "\n",
     "\n",