add setup data for 06

Author: Ray Bell

01x_lazy.ipynb

@@ -168,6 +168,15 @@
     "Consider reading three CSV files with `pd.read_csv` and then measuring their total length. We will consider how you would do this with ordinary Python code, then build a graph for this process using delayed, and finally execute this graph using Dask, for a handy speed-up factor of more than two (there are only three inputs to parallelize over)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run prep.py -d accounts"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -573,7 +582,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

06_distributed_advanced.ipynb

@@ -237,6 +237,15 @@
     "Generally, any Dask operation that is executed using `.compute()` can be submitted for asynchronous execution using `c.compute()` instead, and this applies to all collections. Here is an example with the calculation previously seen in the Bag chapter. We have replaced the `.compute()` method there with the distributed client version, so, again, we could continue to submit more work (perhaps based on the result of the calculation), or, in the next cell, follow the progress of the computation. A similar progress-bar appears in the monitoring UI page."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run prep.py -d accounts"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -305,6 +314,15 @@
     "In the example here, we repeat a calculation from the Array chapter - notice that each call to `compute()` is roughly the same speed, because the loading of the data is included every time."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run prep.py -d random"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -675,7 +693,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,