def analyze_numlangedition_per_profession(self):
classes = self.people["class"].unique()
print(self.people["class"].nunique())
# print classes
res = pd.DataFrame(columns=("class", "mean-men", "sem-men", "mean-women", "sem-women"))
for pclass in classes:
people = self.people[self.people["class"] == pclass]
print(pclass)
classname = pclass.split("/")[-1]
print(classname)
resdict = self.analyze_num_lang_edition(classname, people)
res = res.append(resdict, ignore_index=True)
ut.plot_shaded_lines(
res["class"].values,
res["mean-women"].values,
res["mean-men"].values,
res["sem-women"].values,
res["sem-women"].values,
"mean num editions",
"professions",
"img/numedition_gender_deacdes.png",
)
def analyze_numlangedition_per_profession(self):
classes = self.people["class"].unique()
print(self.people["class"].nunique())
#print classes
res = pd.DataFrame(columns=('class', 'mean-men', "sem-men",
'mean-women', 'sem-women'))
for pclass in classes:
people = self.people[self.people["class"] == pclass]
print(pclass)
classname = pclass.split("/")[-1]
print(classname)
resdict = self.analyze_num_lang_edition(classname, people)
res = res.append(resdict, ignore_index=True)
ut.plot_shaded_lines(res["class"].values, res["mean-women"].values,
res["mean-men"].values, res["sem-women"].values,
res["sem-women"].values, 'mean num editions',
'professions',
'img/numedition_gender_deacdes.png')
def analyze_numlangedition_per_decade(self):
interval = 10
decade = 1000
# decades_of_interest = np.arange(0, 2010, interval)
res = pd.DataFrame(columns=("class", "mean-men", "sem-men", "mean-women", "sem-women"))
# for decade in decades_of_interest:
print(self.people.shape)
print(self.people.head(n=1))
# print (self.people[(self.people.birth_year < 1000) & (self.people.birth_year > 0)].shape)
people = self.people[(self.people.birth_year < decade) & (self.people.birth_year >= 0)]
print(people.shape)
resdict = self.analyze_num_lang_edition("0-1000", people)
res = res.append(resdict, ignore_index=True)
while decade < 2015:
end = decade + interval
people = self.people[(self.people.birth_year >= decade) & (self.people.birth_year < end)]
print(decade)
print(decade + interval)
print(people.shape)
resdict = self.analyze_num_lang_edition(str(decade) + "-" + str(end), people)
res = res.append(resdict, ignore_index=True)
decade = end
ut.plot_shaded_lines(
res["class"].values,
res["mean-women"].values,
res["mean-men"].values,
res["sem-women"].values,
res["sem-women"].values,
"Mean Num Editions",
"Birth Year",
"img/numedition_gender_deacdes" + self.pre + "-" + self.post + ".png",
)
def analyze_numlangedition_per_decade(self):
interval = 10
decade = 1000
#decades_of_interest = np.arange(0, 2010, interval)
res = pd.DataFrame(columns=('class', 'mean-men', "sem-men",
'mean-women', 'sem-women'))
#for decade in decades_of_interest:
print(self.people.shape)
print(self.people.head(n=1))
#print (self.people[(self.people.birth_year < 1000) & (self.people.birth_year > 0)].shape)
people = self.people[(self.people.birth_year < decade)
& (self.people.birth_year >= 0)]
print(people.shape)
resdict = self.analyze_num_lang_edition("0-1000", people)
res = res.append(resdict, ignore_index=True)
while (decade < 2015):
end = decade + interval
people = self.people[(self.people.birth_year >= decade)
& (self.people.birth_year < end)]
print(decade)
print(decade + interval)
print(people.shape)
resdict = self.analyze_num_lang_edition(
str(decade) + "-" + str(end), people)
res = res.append(resdict, ignore_index=True)
decade = end
ut.plot_shaded_lines(
res["class"].values, res["mean-women"].values,
res["mean-men"].values, res["sem-women"].values,
res["sem-women"].values, 'Mean Num Editions', 'Birth Year',
'img/numedition_gender_deacdes' + self.pre + "-" + self.post +
'.png')
