def gini_accum(data, index):
#~ data = raw_data.set_index([raw_data['timestamp'].dt.to_period('M'), raw_data.index])
monthly_data = data.groupby(pd.Grouper(key='timestamp', freq='MS'))
if index is not None:
gini_accum_df = pd.Series(index=index)
else:
gini_accum_df = pd.Series(index=monthly_data.size().index)
indices = gini_accum_df.index
i = 0
accum_data = pd.DataFrame()
for name, group in monthly_data:
# Accumulate data so far
accum_data = accum_data.append(group)
# Get contributions per contributor, sort them
# and make it a list to call to gini_coeff()
values = contributions_per_author(accum_data) \
.tolist()
n_users = len(values)
if (n_users) < MINIMAL_USERS_GINI:
gini_accum_df[indices[i]] = np.NaN
else:
gini_accum_df[indices[i]] = ineq.gini_corrected(values, n_users)
i = i + 1
return gini_accum_df
def calculate_gini_user_talk_edits(self):
if 'user_talks' in self.graph.vs.attributes() and 'gini_user_talks'\
not in self.graph.attributes():
gini = ineq.gini_corrected(self.graph.vs['user_talks'])
if gini is not np.nan:
self.graph['gini_user_talks'] = f"{gini:.2f}"
else:
self.graph['gini_user_talks'] = 'nan'
def calculate_gini_closeness(self):
if 'closeness' in self.graph.vs.attributes() and 'gini_closeness'\
not in self.graph.attributes():
gini = ineq.gini_corrected(self.graph.vs['closeness'])
if gini is not np.nan:
self.graph['gini_closeness'] = f"{gini:.2f}"
else:
self.graph['gini_closeness'] = 'nan'
def calculate_gini_betweenness(self):
if 'betweenness' in self.graph.vs.attributes() and 'gini_betweenness'\
not in self.graph.attributes():
gini = ineq.gini_corrected(self.graph.vs['betweenness'])
value = 'nan'
if gini is not np.nan:
value = f"{gini:.2f}"
self.graph['gini_betweenness'] = value
def calculate_gini_degree(self):
if self.graph.is_directed() and 'gini_indegree' not in\
self.graph.attributes():
gini = ineq.gini_corrected(self.graph.vs['indegree'])
value = 'nan'
if gini is not np.nan:
value = value = f"{gini:.2f}"
self.graph['gini_indegree'] = value
gini = ineq.gini_corrected(self.graph.vs['outdegree'])
value = 'nan'
if gini is not np.nan:
value = value = f"{gini:.2f}"
self.graph['gini_outdegree'] = value
elif 'gini_degree' not in self.graph.attributes():
gini = ineq.gini_corrected(self.graph.vs['degree'])
value = 'nan'
if gini is not np.nan:
value = value = f"{gini:.2f}"
self.graph['gini_degree'] = value