Fix pandas-cookbook notebooks

OpenMined · Oct 18, 2023 · 41c1701 · 41c1701
1 parent 38dbdbe
commit 41c1701
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diff --git a/notebooks/tutorials/pandas-cookbook/01-reading-from-a-csv.ipynb b/notebooks/tutorials/pandas-cookbook/01-reading-from-a-csv.ipynb
@@ -510,7 +510,7 @@
     "new_project = sy.Project(\n",
     "    name=\"My pandas project 1\",\n",
     "    description=\"Hi, I would like to plot the Berri 1 column.\",\n",
-    "    members=[guest_domain_client],\n",
+    "    members=[guest_client],\n",
     ")\n",
     "new_project"
    ]
@@ -538,7 +538,7 @@
    },
    "outputs": [],
    "source": [
-    "project.create_code_request(get_column, guest_domain_client)"
+    "project.create_code_request(get_column, guest_client)"
    ]
   },
   {
@@ -550,7 +550,7 @@
    },
    "outputs": [],
    "source": [
-    "assert len(guest_domain_client.code.get_all())==1"
+    "assert len(guest_client.code.get_all())==1"
    ]
   },
   {
@@ -821,14 +821,6 @@
    "source": [
     "node.land()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "32ab7e8e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -847,7 +839,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,

diff --git a/notebooks/tutorials/pandas-cookbook/02-selecting-data-finding-common-complain.ipynb b/notebooks/tutorials/pandas-cookbook/02-selecting-data-finding-common-complain.ipynb
@@ -209,9 +209,9 @@
     "    \"X Coordinate (State Plane)\": lambda x: randint(1,1000000),\n",
     "    \"Y Coordinate (State Plane)\": lambda x: randint(1,1000000),\n",
     "    \"Complaint Type\": lambda x: random.choice([\"Illegal Parking\", \"Noise - Street/Sidewalk\", \"'Animal in a Park'\"]),\n",
-    "    \"Descriptor\": lambda x: random.choice([ 'Branch or Limb Has Fallen Down','Branches Damaged','Broken Fence', 'Broken Glass']),\n",
-    "    \"School Number\": lambda x: random.choice([ 'B073', 'B077', 'B079', 'B080-01', 'B087', 'B099', 'B100', 'B102', 'B109', 'B111']),\n",
-    "    \"Bridge Highway Segment\": lambda x: random.choice([ 'Grand Central Pkwy (Exit 1 E-W)',\n",
+    "    \"Descriptor\": lambda x: random.choice(['Branch or Limb Has Fallen Down','Branches Damaged','Broken Fence', 'Broken Glass']),\n",
+    "    \"School Number\": lambda x: random.choice(['B073', 'B077', 'B079', 'B080-01', 'B087', 'B099', 'B100', 'B102', 'B109', 'B111']),\n",
+    "    \"Bridge Highway Segment\": lambda x: random.choice(['Grand Central Pkwy (Exit 1 E-W)',\n",
     "                                                       'Grand Central Pkwy (Exit 10) - 69th Rd-Jewel Ave (Exit 11)',\n",
     "                                                       'GrandCentral Pkwy/VanWyck Expwy/College Point Blvd (Exit 22 A-E)',\n",
     "                                                       'Hamilton Ave (Exit 2A) - Gowanus Expwy (I-278) (Exit 1)',\n",
@@ -698,7 +698,7 @@
     "new_project = sy.Project(\n",
     "    name=\"Pandas chapter 2\",\n",
     "    description=\"Hi, I would like to plot the histogram of the complaint types.\",\n",
-    "    members=[guest_domain_client],\n",
+    "    members=[guest_client],\n",
     ")\n",
     "new_project"
    ]
@@ -726,7 +726,7 @@
    },
    "outputs": [],
    "source": [
-    "project.create_code_request(get_counts, guest_domain_client)"
+    "project.create_code_request(get_counts, guest_client)"
    ]
   },
   {
@@ -738,7 +738,7 @@
    },
    "outputs": [],
    "source": [
-    "assert len(guest_domain_client.code.get_all())==1"
+    "assert len(guest_client.code.get_all()) == 1"
    ]
   },
   {
@@ -974,14 +974,6 @@
    "source": [
     "node.land()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "247f8f48-7281-4dd5-a6fe-702ed46891cb",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/notebooks/tutorials/pandas-cookbook/03-which-borough-has-the-most-noise-complaints.ipynb b/notebooks/tutorials/pandas-cookbook/03-which-borough-has-the-most-noise-complaints.ipynb
@@ -95,9 +95,9 @@
     "# Make the graphs a bit prettier, and bigger\n",
     "plt.style.use('ggplot')\n",
     "\n",
-    "# This is necessary to show lots of columns in pandas 0.12. \n",
+    "# This is necessary to show lots of columns in pandas 0.12.\n",
     "# Not necessary in pandas 0.13.\n",
-    "pd.set_option('display.width', 5000) \n",
+    "pd.set_option('display.width', 5000)\n",
     "pd.set_option('display.max_columns', 60)\n",
     "\n",
     "plt.rcParams['figure.figsize'] = (15, 5)"
@@ -222,9 +222,9 @@
     "    \"X Coordinate (State Plane)\": lambda x: randint(1,1000000),\n",
     "    \"Y Coordinate (State Plane)\": lambda x: randint(1,1000000),\n",
     "    \"Complaint Type\": lambda x: random.choice([\"Illegal Parking\", \"Noise - Street/Sidewalk\", \"'Animal in a Park'\"]),\n",
-    "    \"Descriptor\": lambda x: random.choice([ 'Branch or Limb Has Fallen Down','Branches Damaged','Broken Fence', 'Broken Glass']),\n",
-    "    \"School Number\": lambda x: random.choice([ 'B073', 'B077', 'B079', 'B080-01', 'B087', 'B099', 'B100', 'B102', 'B109', 'B111']),\n",
-    "    \"Bridge Highway Segment\": lambda x: random.choice([ 'Grand Central Pkwy (Exit 1 E-W)',\n",
+    "    \"Descriptor\": lambda x: random.choice(['Branch or Limb Has Fallen Down','Branches Damaged','Broken Fence', 'Broken Glass']),\n",
+    "    \"School Number\": lambda x: random.choice(['B073', 'B077', 'B079', 'B080-01', 'B087', 'B099', 'B100', 'B102', 'B109', 'B111']),\n",
+    "    \"Bridge Highway Segment\": lambda x: random.choice(['Grand Central Pkwy (Exit 1 E-W)',\n",
     "                                                       'Grand Central Pkwy (Exit 10) - 69th Rd-Jewel Ave (Exit 11)',\n",
     "                                                       'GrandCentral Pkwy/VanWyck Expwy/College Point Blvd (Exit 22 A-E)',\n",
     "                                                       'Hamilton Ave (Exit 2A) - Gowanus Expwy (I-278) (Exit 1)',\n",
@@ -265,7 +265,7 @@
     "mock_data = dict()\n",
     "for col in complaints.columns:\n",
     "    col_vals = complaints[col]\n",
-    "    \n",
+    "\n",
     "    if col in fake_functions:\n",
     "        mock_func = fake_functions[col]\n",
     "    elif len(set(complaints[col])) < 100:\n",
@@ -810,7 +810,7 @@
     "new_project = sy.Project(\n",
     "    name=\"Pandas chapter 3\",\n",
     "    description=\"Hi, I would like to plot the histogram of the noise complaint counts per area.\",\n",
-    "    members=[guest_domain_client],\n",
+    "    members=[guest_client],\n",
     ")\n",
     "new_project"
    ]
@@ -838,7 +838,7 @@
    },
    "outputs": [],
    "source": [
-    "project.create_code_request(get_counts, guest_domain_client)"
+    "project.create_code_request(get_counts, guest_client)"
    ]
   },
   {
@@ -850,7 +850,7 @@
    },
    "outputs": [],
    "source": [
-    "assert len(guest_domain_client.code.get_all())==1"
+    "assert len(guest_client.code.get_all())==1"
    ]
   },
   {
@@ -1086,14 +1086,6 @@
    "source": [
     "node.land()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4374c6ed-b64f-4819-aca4-5570676d6c68",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1112,7 +1104,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,

diff --git a/notebooks/tutorials/pandas-cookbook/04-weekday-bike-most-groupby-aggregate.ipynb b/notebooks/tutorials/pandas-cookbook/04-weekday-bike-most-groupby-aggregate.ipynb
@@ -588,7 +588,7 @@
     "new_project = sy.Project(\n",
     "    name=\"Pandas Chapter 4\",\n",
     "    description=\"Hi, I would like to plot histogram of bikers per weekday\",\n",
-    "    members=[guest_domain_client],\n",
+    "    members=[guest_client],\n",
     ")\n",
     "new_project"
    ]
@@ -616,7 +616,7 @@
    },
    "outputs": [],
    "source": [
-    "project.create_code_request(get_bike_hist, guest_domain_client)"
+    "project.create_code_request(get_bike_hist, guest_client)"
    ]
   },
   {
@@ -628,7 +628,7 @@
    },
    "outputs": [],
    "source": [
-    "assert len(guest_domain_client.code.get_all())==1"
+    "assert len(guest_client.code.get_all()) == 1"
    ]
   },
   {
@@ -876,14 +876,6 @@
    "source": [
     "node.land()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cbcd55e5",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -902,7 +894,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,

diff --git a/notebooks/tutorials/pandas-cookbook/05-combining-dataframes-scraping-weather-data.ipynb b/notebooks/tutorials/pandas-cookbook/05-combining-dataframes-scraping-weather-data.ipynb
@@ -258,11 +258,13 @@
     "for month in range(1, 13):\n",
     "    url = url_template.format(month=month, year=2012)\n",
     "    weather = pd.read_csv(url, parse_dates=True, index_col='Date/Time (LST)')\n",
-    "    mock = pd.DataFrame(index=[Timestamp(get_mock_date(i)) for i in range(len(weather))],\n",
-    "                    data=[get_mock_row(i) for i in range(len(weather))],\n",
-    "                    columns=weather.columns)\n",
+    "    mock = pd.DataFrame(\n",
+    "        index=[Timestamp(get_mock_date(i)) for i in range(len(weather))],\n",
+    "        data=[get_mock_row(i) for i in range(len(weather))],\n",
+    "        columns=weather.columns\n",
+    "    )\n",
     "    \n",
-    "    assets += [sy.Asset(name=f\"weather{month}\", data=weather, mock=mock, mock_is_real=False)]"
+    "    assets.append(sy.Asset(name=f\"weather{month}\", data=weather, mock=mock, mock_is_real=False))"
    ]
   },
   {
@@ -793,7 +795,7 @@
     "new_project = sy.Project(\n",
     "    name=\"Pandas Chapter 5\",\n",
     "    description=\"Hi, get all weather data for 2012\",\n",
-    "    members=[guest_domain_client],\n",
+    "    members=[guest_client],\n",
     ")\n",
     "new_project"
    ]
@@ -821,7 +823,7 @@
    },
    "outputs": [],
    "source": [
-    "project.create_code_request(get_2012_weather, guest_domain_client)"
+    "project.create_code_request(get_2012_weather, guest_client)"
    ]
   },
   {
@@ -833,7 +835,7 @@
    },
    "outputs": [],
    "source": [
-    "assert len(guest_domain_client.code.get_all())==1"
+    "assert len(guest_client.code.get_all())==1"
    ]
   },
   {
@@ -1175,7 +1177,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,

diff --git a/notebooks/tutorials/pandas-cookbook/06-string-operations-which-month-was-the-snowiest.ipynb b/notebooks/tutorials/pandas-cookbook/06-string-operations-which-month-was-the-snowiest.ipynb
@@ -211,8 +211,7 @@
     "    res = dict()\n",
     "    for k, function in mock_functions.items():\n",
     "        res[k] = function()\n",
-    "    return res\n",
-    "    "
+    "    return res\n"
    ]
   },
   {
@@ -247,7 +246,7 @@
     "mock = pd.DataFrame(index=[Timestamp(get_mock_date(i)) for i in range(len(weather_2012_final))],\n",
     "                    data=[get_mock_row(i) for i in range(len(weather_2012_final))],\n",
     "                    columns=weather_2012_final.columns)\n",
-    "    \n"
+    "\n"
    ]
   },
   {
@@ -657,10 +656,10 @@
     "    weather_2012 = df\n",
     "    weather_description = weather_2012['Weather']\n",
     "    is_snowing = weather_description.str.contains('Snow')\n",
-    "    \n",
+    "\n",
     "    median_temperature = weather_2012['Temp (C)'].resample('M').apply(np.median)\n",
     "    snow_frequency = is_snowing.astype(float).resample('M').apply(np.mean)\n",
-    "    \n",
+    "\n",
     "    temperature = weather_2012['Temp (C)'].resample('M').apply(np.median)\n",
     "    is_snowing = weather_2012['Weather'].str.contains('Snow')\n",
     "    snowiness = is_snowing.astype(float).resample('M').apply(np.mean)\n",
@@ -670,7 +669,7 @@
     "    snowiness.name = \"Snowiness\"\n",
     "    stats = pd.concat([temperature, snowiness], axis=1)\n",
     "    stats.columns = [\"temperature\", \"snowiness\"]\n",
-    "    \n",
+    "\n",
     "    return is_snowing.astype(float), median_temperature, snow_frequency, stats"
    ]
   },
@@ -694,7 +693,7 @@
     "new_project = sy.Project(\n",
     "    name=\"Pandas Chapter 6\",\n",
     "    description=\"Hi, I would like to get some insights about snow and temperature for 2012\",\n",
-    "    members=[guest_domain_client],\n",
+    "    members=[guest_client],\n",
     ")\n",
     "new_project"
    ]
@@ -722,7 +721,7 @@
    },
    "outputs": [],
    "source": [
-    "project.create_code_request(snow_and_temperature, guest_domain_client)"
+    "project.create_code_request(snow_and_temperature, guest_client)"
    ]
   },
   {
@@ -734,7 +733,7 @@
    },
    "outputs": [],
    "source": [
-    "assert len(guest_domain_client.code.get_all())==1"
+    "assert len(guest_client.code.get_all()) == 1"
    ]
   },
   {