\n",
" | unique | \n",
- " 163 | \n",
- " 159 | \n",
+ " 160 | \n",
+ " 147 | \n",
" 2 | \n",
" 2 | \n",
" 2 | \n",
@@ -404,7 +404,7 @@
"count 245 245 \n",
"top 2024-06-20 00:00:00 2024-06-20 00:00:00 \n",
"freq 60.0 60.0 \n",
- "unique 163 159 \n",
+ "unique 160 147 \n",
"top_percent_of_count 24.5 24.5 \n",
"\n",
" Do you feel the True North report identified the key challenges and opportunities facing the region? \\\n",
@@ -737,11 +737,11 @@
"3 Greater Manchester\n",
"4 West Yorkshire\n",
" ... \n",
- "240 Blackburn\n",
- "241 Chester\n",
- "242 Helsby\n",
- "243 NaN\n",
- "244 Liverpool\n",
+ "240 Helsby\n",
+ "241 NaN\n",
+ "242 Liverpool\n",
+ "243 Merseyside\n",
+ "244 NaN\n",
"Name: City, Length: 245, dtype: object"
]
},
diff --git a/pipelines/truenorth/clean_members_list.ipynb b/pipelines/truenorth/clean_members_list.ipynb
index 5b94296..21ec1db 100644
--- a/pipelines/truenorth/clean_members_list.ipynb
+++ b/pipelines/truenorth/clean_members_list.ipynb
@@ -54,7 +54,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/var/folders/5k/p_091y6n6rscp__0p77_ynv80000gn/T/ipykernel_77050/1262414977.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",
+ "/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"
]
}
@@ -67,13 +67,31 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Add a \"last_updated\" column which uses the 'last activity date' column if it exists, otherwise the create date."
+ "The dates should all start in May 2023 or later."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
+ "outputs": [],
+ "source": [
+ "data['Create Date'] = pd.to_datetime(data['Create Date'])\n",
+ "start_date = pd.Timestamp('2023-05-01')\n",
+ "data.loc[data['Create Date'] < start_date, 'Create Date'] = start_date"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Add a \"last_updated\" column which uses the 'last activity date' column if it exists, otherwise the create date."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -106,7 +124,7 @@
" 'last_updated']"
]
},
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -127,7 +145,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -149,7 +167,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -173,7 +191,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -184,7 +202,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -196,7 +214,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -217,7 +235,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -230,7 +248,7 @@
" 'no longer in position', 'Self employed'], dtype=object)"
]
},
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -241,7 +259,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -280,7 +298,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -290,7 +308,7 @@
" 'no longer in position'], dtype=object)"
]
},
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -308,7 +326,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -329,7 +347,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -379,7 +397,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -403,7 +421,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
@@ -413,7 +431,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -429,7 +447,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
diff --git a/src/overview/membership/_data/cumsum.csv b/src/overview/membership/_data/cumsum.csv
index d1ff9fb..4e1b1fb 100644
--- a/src/overview/membership/_data/cumsum.csv
+++ b/src/overview/membership/_data/cumsum.csv
@@ -1,10 +1,4 @@
year,month_formatted,start_of_month,individuals,orgs
-2022.16,2022-03,2022-03-01,21,21
-2022.5,2022-07,2022-07-01,22,22
-2022.58,2022-08,2022-08-01,25,25
-2022.75,2022-10,2022-10-01,27,27
-2022.91,2022-12,2022-12-01,31,31
-2023.16,2023-03,2023-03-01,33,33
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
diff --git a/src/overview/membership/index.vto b/src/overview/membership/index.vto
index 14d2aa1..42eac4c 100644
--- a/src/overview/membership/index.vto
+++ b/src/overview/membership/index.vto
@@ -8,6 +8,7 @@ nicesection: True North
About this data
The data below was generated from questionnaire responses. As the questionnaire has changed over time, some fields were incomplete for some responses.
+ All percentages are calculated as a fraction of the people who answered that question.
{{# You can read the details on how we processed this data. #}}
@@ -39,8 +40,9 @@ nicesection: True North
Membership growth
+ The True North Network started in May 2023.
The chart below shows the cumulative number of members, and the cumulutive number of unique organisations,
- in the True North network. In some months no one joined the network, so the data points are not evenly spaced.
+ who have joined the True North network since then.
{{ comp.oi.chart.line({ config: {
data: cumsum,
@@ -69,7 +71,8 @@ nicesection: True North
Membership sectors
- This chart shows the frequency of words mentioned in the question "What sector do you operate in?".
+ This chart shows the frequency of words mentioned in the question "What sector do you operate in?". Note that "services" generally pairs
+ with other words, which explains its high frequency.
{{ comp.oi.chart.bar ({ config: {
data: sector_word_counts,