Update members data

pipelines/true-north/clean_members_list.ipynb

@@ -32,8 +32,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hubspot_export = pd.read_excel(os.path.join(WDIR, 'true-north/True North June data (updated).xlsx'))\n",
-    "additional_members = pd.read_excel(os.path.join(WDIR, 'true-north/true north_additional_members_list_19_06_24.xlsx'))"
+    "data = pd.read_excel(os.path.join(WDIR, 'true-north/True North June Data (All).xlsx'))\n",
+    "# additional_members = pd.read_excel(os.path.join(WDIR, 'true-north/true north_additional_members_list_19_06_24.xlsx'))"
    ]
   },
   {
@@ -42,25 +42,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "additional_members['Create Date'] = '2024-06-20 00:00:00'"
+    "# additional_members['Create Date'] = '2024-06-20 00:00:00'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/5k/p_091y6n6rscp__0p77_ynv80000gn/T/ipykernel_2002/1262414977.py:1: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  data = pd.concat([hubspot_export, additional_members]).reset_index(drop=True)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "data = pd.concat([hubspot_export, additional_members]).reset_index(drop=True)"
+    "# data = pd.concat([hubspot_export, additional_members]).reset_index(drop=True)\n",
+    "# data = hubspot_export"
    ]
   },
   {
@@ -98,8 +90,8 @@
       "text/plain": [
        "['First Name',\n",
        " 'Last Name',\n",
-       " 'Last Activity Date',\n",
        " 'Create Date',\n",
+       " 'Last Activity Date',\n",
        " 'How many people work in your organisation?',\n",
        " 'What sector(s) does your organisation operate in? [Other]',\n",
        " 'What sector(s) does your organisation operate in??',\n",
@@ -116,11 +108,8 @@
        " 'Would you be interested in hearing more from Brabners about the B Corp process?',\n",
        " 'Company Name',\n",
        " 'City',\n",
-       " 'associated Company Domain (Contact Level)',\n",
-       " 'Which theme of the True North report do you most identify with and could support activity around?',\n",
-       " 'Do you feel the True North report identified the key challenges and opportunities facing the region?',\n",
-       " 'Are you interested in attending future True North events?',\n",
        " 'Industry',\n",
+       " 'associated Company Domain (Contact Level)',\n",
        " 'last_updated']"
       ]
      },
@@ -241,11 +230,8 @@
     {
      "data": {
       "text/plain": [
-       "array(['1 - 10', '1,000+', '51 - 100', '11 - 50', '101 - 250',\n",
-       "       '251 - 500', '501 - 1,000', '1 - 10; 11 - 50', nan, '51 - 200',\n",
-       "       '2 - 10', '501 - 1000', '11 - 50 ', 'Self-Employed', '1000+',\n",
-       "       '104 - 250', '51-200', '101-250', '2 - 11', 'Self-employed',\n",
-       "       'no longer in position', 'Self employed'], dtype=object)"
+       "array(['1 - 10', '501 - 1,000', '11 - 50', '101 - 250', '1,000+',\n",
+       "       '51 - 100', '251 - 500', '1 - 10; 11 - 50', nan], dtype=object)"
       ]
      },
      "execution_count": 12,
@@ -304,8 +290,7 @@
     {
      "data": {
       "text/plain": [
-       "array(['0-9', '1000+', '50-249', '10-49', '250+', nan, 'Self-Employed',\n",
-       "       'no longer in position'], dtype=object)"
+       "array(['0-9', '250+', '10-49', '50-249', '1000+', nan], dtype=object)"
       ]
      },
      "execution_count": 14,
@@ -471,7 +456,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,

src/overview/membership/_data/cumsum.csv

@@ -1,15 +1,16 @@
 year,month_formatted,start_of_month,individuals,orgs
-2023.33,2023-05,2023-05-01,37,37
-2023.41,2023-06,2023-06-01,40,39
-2023.49,2023-07,2023-07-01,48,46
-2023.58,2023-08,2023-08-01,58,56
-2023.66,2023-09,2023-09-01,82,79
-2023.75,2023-10,2023-10-01,134,129
-2023.83,2023-11,2023-11-01,139,133
-2023.91,2023-12,2023-12-01,146,140
-2024.0,2024-01,2024-01-01,158,151
-2024.08,2024-02,2024-02-01,164,157
-2024.16,2024-03,2024-03-01,175,168
-2024.25,2024-04,2024-04-01,179,172
-2024.33,2024-05,2024-05-01,184,177
-2024.41,2024-06,2024-06-01,245,230
+2023.33,2023-05,2023-05-01,70,66
+2023.41,2023-06,2023-06-01,74,69
+2023.49,2023-07,2023-07-01,110,100
+2023.58,2023-08,2023-08-01,129,117
+2023.66,2023-09,2023-09-01,162,148
+2023.75,2023-10,2023-10-01,215,199
+2023.83,2023-11,2023-11-01,221,202
+2023.91,2023-12,2023-12-01,228,208
+2024.0,2024-01,2024-01-01,239,219
+2024.08,2024-02,2024-02-01,244,224
+2024.16,2024-03,2024-03-01,255,235
+2024.25,2024-04,2024-04-01,259,239
+2024.33,2024-05,2024-05-01,264,244
+2024.41,2024-06,2024-06-01,271,251
+2024.5,2024-07,2024-07-01,274,254

src/overview/membership/_data/sector_word_counts.csv

@@ -1,15 +1,13 @@
 name,count
-services,35
-construction,21
-manufacturing,20
-education,20
-professional,18
-business,17
-engineering,17
-technology,16
-development,15
+services,23
+education,21
+construction,20
+manufacturing,18
+technology,17
+engineering,16
+professional,16
 finance,15
-marketing,12
-real estate,11
-social,10
-recruitment,10
+business,15
+development,14
+support,12
+marketing,10

src/overview/membership/_data/top_locations.csv

@@ -1,6 +1,6 @@
 location,count
-Greater Manchester,39
-Lancashire,22
+Greater Manchester,27
+Merseyside,22
 West Yorkshire,20
-Merseyside,20
-Cheshire,12
+Lancashire,19
+Cheshire,14

src/overview/membership/_data/true_north_members_list.csv

@@ -1,10 +1,10 @@
 name,value,footnote,post,url
-Total members,245,People,,/overview/membership
-Total companies,229,Unique companies,,/overview/membership
-Membership increase,33.2,Since last month,%,/overview/membership
-Companies with 0-9 employees,32.1,Of members work in companies of this size,%,
-Top industry,6.7,Of members work in Civic and Social Organizations,%,
+Total members,274,People,,/overview/membership
+Total companies,253,Unique companies,,/overview/membership
+Membership increase,1.1,Since last month,%,/overview/membership
+Companies with 0-9 employees,45.7,Of members work in companies of this size,%,
+Top industry,,Of members work in nan,%,
 Geographic reach,4,placeholder,,
 Advisory council,11,Members represent the network on the True North advisory council,,https://www.brabners.com/insights/true-north/true-north-advisory-council-launches
 Northern stars,5,Companies have been featured as Northern Stars,,/overview/northern-stars
-B Corps,12.6,Of member's organisations are B Corps or are joining,%,/themes/sustainable-growth/b-corporations
+B Corps,13.2,Of member's organisations are B Corps or are joining,%,/themes/sustainable-growth/b-corporations

src/overview/membership/_data/updated.yaml

@@ -1 +1 @@
-2024-06-28 15:45:10
+2024-07-11 17:33:12

src/overview/membership/index.vto

@@ -29,7 +29,6 @@ updated: 2024-06-26 16:33:35
       { name: "Total companies" },
       { name: "Membership increase" },
       { name: "Companies with 0-9 employees" },
-      { name: "Top industry" },
       { name: "Advisory council" },
       { name: "Northern stars" },
       { name: "B Corps" }