Roles#
This page shows the roles of Open Seeds people
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objects as go
baseurl = "https://raw.githubusercontent.com/open-life-science/open-life-science.github.io/main/_data/artifacts/openseeds/"
cohort_nb = 8
people_df = pd.read_csv(f"{baseurl}people.csv", index_col=0).fillna("")
roles_df = {}
roles = []
for r in ["role", "participant", "mentor", "expert", "speaker", "facilitator", "organizer"]:
role = r.capitalize()
roles.append(role)
roles_df[role] = pd.read_csv(f"{baseurl}/roles/{r}.csv", index_col=0)
Roles over cohorts#
red_roles = ["Participant", "Mentor", "Facilitator", "Speaker", "Expert"]
role_count_df = pd.DataFrame(columns = red_roles)
for r in red_roles:
role_count_df[r] = (
roles_df[r]
.drop(columns = ["city", "country", "country_3", "first-name", "last-name", "pronouns", "continent", "longitude", "latitude"])
.count(axis=0)
.rename(index=str.upper)
)
role_count_df.loc["Total", r] = len(roles_df[r])
role_count_df = role_count_df.astype("int64")
role_count_df
| Participant | Mentor | Facilitator | Speaker | Expert | |
|---|---|---|---|---|---|
| OLS-1 | 29 | 20 | 0 | 18 | 37 |
| OLS-2 | 52 | 36 | 0 | 24 | 65 |
| OLS-3 | 66 | 34 | 1 | 22 | 63 |
| OLS-4 | 34 | 32 | 7 | 18 | 46 |
| OLS-5 | 71 | 35 | 5 | 17 | 26 |
| OLS-6 | 41 | 32 | 7 | 19 | 39 |
| OLS-7 | 50 | 32 | 10 | 18 | 4 |
| OLS-8 | 69 | 38 | 15 | 8 | 22 |
| Total | 383 | 138 | 30 | 95 | 170 |
role_count_df = role_count_df.drop(index="Total")
fig, ax = plt.subplots()
fig.set_dpi(300)
(role_count_df
.plot(ax=ax, colormap='tab20c'))
plt.xlabel('Cohorts')
plt.ylabel('Number')
ax.legend(
loc='center left',
bbox_to_anchor=(1, 0.5),
frameon=False,
fontsize='x-small')
<matplotlib.legend.Legend at 0x7f5f8c28ed10>
fig, ax = plt.subplots()
fig.set_dpi(300)
(role_count_df
.plot.bar(ax=ax, colormap='tab20c', stacked=True))
#plt.xlabel('Cohorts')
plt.ylabel('Number')
ax.legend(
# loc='center left',
# bbox_to_anchor=(1, 0.5),
# frameon=False,
fontsize='x-small')
<matplotlib.legend.Legend at 0x7f5f84117490>
Role transfer between cohorts#
Flow per cohorts#
cohorts = list(role_count_df.index)
sources = []
targets = []
values = []
for i in range(len(cohorts)-1):
for s in red_roles:
source = f"{cohorts[i].lower()}-{s.lower()}"
left_query = f"`{source}` != ''"
# identify numbers of people that came back in next cohort
for t in red_roles:
target = f"{cohorts[i+1].lower()}-{t.lower()}"
query = f"`{source}` != '' and `{target}` != ''"
value = len(people_df.query(query))
sources.append(source)
targets.append(target)
values.append(value)
left_query += f" and `{target}` == ''"
# identify numbers of people that did not came back in next cohort
value = len(people_df.query(left_query))
sources.append(source)
targets.append(f"{cohorts[i+1].lower()}-alumni")
values.append(value)
cohort_flow = pd.DataFrame({"source": sources, "target": targets, "value": values})
cohort_flow
| source | target | value | |
|---|---|---|---|
| 0 | ols-1-participant | ols-2-participant | 0 |
| 1 | ols-1-participant | ols-2-mentor | 5 |
| 2 | ols-1-participant | ols-2-facilitator | 0 |
| 3 | ols-1-participant | ols-2-speaker | 2 |
| 4 | ols-1-participant | ols-2-expert | 5 |
| ... | ... | ... | ... |
| 205 | ols-7-expert | ols-8-mentor | 2 |
| 206 | ols-7-expert | ols-8-facilitator | 0 |
| 207 | ols-7-expert | ols-8-speaker | 1 |
| 208 | ols-7-expert | ols-8-expert | 2 |
| 209 | ols-7-expert | ols-8-alumni | 0 |
210 rows × 3 columns
labels = [f"ols-{c}-{r.lower()}" for c in range(1,len(cohorts)+1) for r in red_roles + ["alumni"]]
# get index in labels for sources and targets
source_ids = [labels.index(e) for e in sources]
target_ids = [labels.index(e) for e in targets]
# prepare label metadata for printing
colors = {
"participant": "#3182bd",
"mentor": "#fd8d3c",
"expert": "#d9d9d9",
"speaker": "#dadaeb",
"facilitator": "#a1d99b",
"alumni": "grey"
}
y = {
"participant": 0.99,
"mentor": 0.8,
"expert": 0.6,
"speaker": 0.4,
"facilitator": 0.2,
"alumni": 0.01
}
pos = (1-.2)/(len(cohorts)-1)
label_colors = [colors[l.split("-")[-1]] for l in labels]
label_x = [pos*(int(l.split("-")[1])-1)+0.01 for l in labels]
label_y = [y[l.split("-")[-1]] for l in labels]
labels = [l.replace("-", " ").replace("ols ", "OLS-") for l in labels]
label_df = pd.DataFrame({"labels": labels, "colors": label_colors, "x": label_x, "y": label_y})
fig = go.Figure(data=[go.Sankey(
arrangement="snap",
node = dict(
pad = 15,
thickness = 20,
line = dict(color = "black", width = 0.5),
label = label_df.labels,
color = label_df.colors
),
link = dict(
source = source_ids,
target = target_ids,
value = values
))])
fig.show()
Flow overview over all cohorts#
There is a normalizaton step to be sure people coming back in several roles are counting no more that 1 time
cohorts = [x.lower() for x in list(role_count_df.index)]
labels = [x.lower() for x in red_roles] + ["alumni"]
role_flow_df = pd.DataFrame({
"source": [l for subl in [[l]*len(labels) for l in labels] for l in subl],
"target": labels*len(labels),
"value": 0
})
for index, row in role_flow_df.iterrows():
if row.source == "alumni":
continue
elif row.target == "alumni":
for i in range(len(cohorts)-1):
source = f"{cohorts[i]}-{row.source}"
# build query with any role in the cohort and no role after in the following cohort
query = f"`{source}` != ''"
for r in red_roles:
target = f"{cohorts[i+1]}-{r.lower()}"
query += f"and `{target}` == ''"
role_flow_df.at[index, "value"] += len(people_df.query(query))
else:
for i in range(len(cohorts)-1):
source = f"{cohorts[i]}-{row.source}"
target = f"{cohorts[i+1]}-{row.target}"
# build query the source role for a cohort and target role for the following cohort
query = f"`{source}` != '' and `{target}` != ''"
query_res = people_df.query(query)
# check for each result how many roles in cohort and following cohort to normalize the value
value = 0
new_role_nb = len(q_r[f"{cohorts[i+1]}-role"].split(","))
for q_i, q_r in query_res.iterrows():
value += 1/(new_role_nb)
role_flow_df.at[index, "value"] += value
role_flow_df
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[11], line 30
28 # check for each result how many roles in cohort and following cohort to normalize the value
29 value = 0
---> 30 new_role_nb = len(q_r[f"{cohorts[i+1]}-role"].split(","))
31 for q_i, q_r in query_res.iterrows():
32 value += 1/(new_role_nb)
NameError: name 'q_r' is not defined
Participants re-joining (%)
role = 'participant'
role_nb = role_flow_df.query(f"source == '{ role }'").value.sum()
role_wo_alumni = role_flow_df.query(f"source == '{ role }' and target != 'alumni'").value.sum()
100 * role_wo_alumni / role_nb
29.346314325452017
Mentors re-joining (%)
role = 'mentor'
role_nb = role_flow_df.query(f"source == '{ role }'").value.sum()
role_wo_alumni = role_flow_df.query(f"source == '{ role }' and target != 'alumni'").value.sum()
100 * role_wo_alumni / role_nb
51.21886837277442
label_df = pd.DataFrame({
"labels": [l.capitalize() for l in labels] * 2,
"colors": [colors[l] for l in labels] * 2,
"x": [0.1]*len(labels)+[0.9]*len(labels),
"y": [0.1]*(len(labels)*2)})
fig = go.Figure(data=[go.Sankey(
arrangement="snap",
node = dict(
pad = 15,
thickness = 20,
line = dict(color = "black", width = 0.5),
label = label_df.labels,
color = label_df.colors,
),
link = dict(
source = [labels.index(e) for e in role_flow_df.source],
target = [labels.index(e)+len(labels) for e in role_flow_df.target],
value = role_flow_df.value
))])
fig.update_layout(
font_size=18,
width=950,
height=400,
margin=go.layout.Margin(
l=10, #left margin
r=10, #right margin
b=20, #bottom margin
t=20, #top margin
)
)
fig.show()