def freq_plot_to_show(results):
"""
displays bar plot of relative frequency of all words in results
:param results: dict of results from dunning_total or similar, i.e. in the form {'word': {
'freq_corp1': int, 'freq_corp2': int, 'freq_total': int}}
:return: None, displays bar plot of relative frequency of all words in results
"""
load_graph_settings(False)
results_dict = dict(results)
words = []
female_rel_freq = []
male_rel_freq = []
for term, data in results_dict.items():
words.append(term)
female_rel_freq.append(data['freq_corp1'] / data['freq_total'])
male_rel_freq.append(-1 * data['freq_corp2'] / data['freq_total'])
opacity = 0.4
colors = ['b']
ax = sns.barplot(male_rel_freq, words, palette=colors, alpha=opacity)
sns.despine(ax=ax, bottom=True, left=True)
plt.show()
def box_gender_pronoun_freq(freq_dict, my_pal, title, x="N/A"):
"""
Takes in a frequency dictionary (from **freq_by_author_gender**, **freq_by_date**,
or **freq_by_location**) and exports its values as a bar-and-whisker graph
:param freq_dict: dictionary of frequencies
:param my_pal: palette to be used
:param title: title of exported graph
:param x: name of x-vars
:return: None
"""
plt.clf()
groups = []
val = []
for k, v in freq_dict.items():
temp = [k]*len(v)
groups.extend(temp)
val.extend(v)
df = pd.DataFrame({x: groups, 'Frequency': val})
df = df[[x, 'Frequency']]
common.load_graph_settings()
sns.boxplot(x=df[x], y=df['Frequency'],
palette=my_pal).set_title("Relative Frequency of Female Pronouns to Total Pronouns")
plt.xticks(rotation=90)
# plt.show()
filepng = "visualizations/" + title + ".png"
filepdf = "visualizations/" + title + ".pdf"
plt.savefig(filepng, bbox_inches='tight')
plt.savefig(filepdf, bbox_inches='tight')
def box_plots(inst_data, my_pal, title, x="N/A"):
"""
Takes in a frequency dictionaries and exports its values as a bar-and-whisker graph
:param inst_data: Dictionary containing instance distance data (from one of the other functions in the module)
:param my_pal: str, seaborn palette to be used
:param title: str, filename of exported graph
:param x: name of x-vars
:return: None
"""
plt.clf()
groups = []
val = []
for k, v in inst_data.items():
temp1 = []
for el in v:
if el[1] <= 60:
temp1.append(el[1])
temp2 = [k.replace("_", " ").capitalize()] * len(temp1)
val.extend(temp1)
groups.extend(temp2)
df = pnds.DataFrame({x: groups, 'Median Female Instance Distance': val})
df = df[[x, 'Median Female Instance Distance']]
common.load_graph_settings()
sns.boxplot(x=df[x],
y=df['Median Female Instance Distance'],
palette=my_pal).set_title(title)
plt.xticks(rotation=90)
# plt.show()
filepng = "visualizations/" + title + ".png"
filepdf = "visualizations/" + title + ".pdf"
plt.savefig(filepng, bbox_inches='tight')
plt.savefig(filepdf, bbox_inches='tight')
def score_plot_to_show(results):
"""
displays bar plot of dunning scores for all words in results
:param results: dict of results from dunning_total or similar, i.e. in the form {'word': {
'dunning': float}}
:return: None, displays bar plot of dunning scores for all words in results
"""
load_graph_settings(False)
results_dict = dict(results)
words = []
dunning_score = []
for term, data in results_dict.items():
words.append(term)
dunning_score.append(data['dunning'])
opacity = 0.4
colors = ['r' if entry >= 0 else 'b' for entry in dunning_score]
ax = sns.barplot(dunning_score, words, palette=colors, alpha=opacity)
sns.despine(ax=ax, bottom=True, left=True)
plt.show()