def create_and_save_graph(df):
# Filling NaN with 0
df = df.fillna(0)
# Selecting 11th row and 1st column
df1_label = df.iloc[10:, :1]
# Converting dataframe into list
df1_label = df1_label.values.tolist()
plt.gcf().subplots_adjust(bottom=0.15)
trend_graph_path = os.path.join(os.path.dirname(__file__), 'shivam.png')
# Select data for Grand Total Plot
df1 = df.iloc[10:, 1:9]
df1 = df1.iloc[0:, 1:]
# Plot first graph
ax = df1.T.plot(kind='line',
marker='s',
x_compat=True,
rot=0,
alpha=0.75,
grid=True)
# Data for second graph
df = df.iloc[:10, :9]
df = df.drop(["Category"], axis=1)
df_label = df.iloc[:, 0]
df_label = df_label.values.tolist()
df = df.iloc[0:, 1:]
ax = df.T.plot(kind='bar',
width=0.9,
legend=False,
figsize=(60, 4),
ax=ax,
rot=0,
alpha=0.75,
grid=True,
colormap='Paired')
x_offset = -0.02
y_offset = 70.02
for p in ax.patches:
b = p.get_bbox()
val = int(b.y1 + b.y0)
ax.annotate(val, ((b.x0 + b.x1) / 2 + x_offset, b.y1 + y_offset),
rotation=90)
patches, labels = ax.get_legend_handles_labels()
labels = df1_label + df_label
ax.legend(patches,
labels,
loc='upper center',
bbox_to_anchor=(0.5, -0.05),
ncol=11,
fancybox=True,
shadow=True,
prop={'size': 10})
plt.savefig(trend_graph_path, bbox_inches='tight', dpi=200)
def annotate_value(df, current_feature, value, axes):
"""
Annotate given value of given feature on chart. Assumed it is available.
"""
x_val = df[current_feature].values.tolist().index(value)
y_val = value
#print x_val,y_val
axes.annotate('Outlier!',
xy=(x_val, y_val * 0.95),
xytext=(x_val, y_val * 0.80),
arrowprops=dict(facecolor='black'))
return axes
def add_labels(ax, bars):
"""
Description:
Inputs:
Outputs:
"""
for bar in bars:
height = bar.get_height()
ax.annotate('{}'.format(height),
xy=(bar.get_x() + bar.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
def club_tags(unencoded_clubs_list):
"""
Creates a bar graph showing of the number of occurrances of each possible club tag.
By using a dictionary to count club tag occurrances, the function then creates a
matplotlib bar graph using numpy representations of the dictionary information.
The graph is formatted and pushed as a Response type for view on the server.
Returns:
--------
Response
The graph
"""
club_dict = {}
for clubs in unencoded_clubs_list:
for tag in clubs.get_category():
if tag in club_dict:
club_dict[tag] = club_dict[tag] + 1
else:
club_dict[tag] = 1
x = np.zeros(len(club_dict))
index_counter = 0
for tag in club_dict:
x[index_counter] = club_dict.get(tag)
index_counter = index_counter + 1
fig = plt.figure()
ax = fig.add_subplot()
bar = ax.bar(np.arange(len(club_dict)), x)
labels = club_dict.keys()
ax.set_xticks(np.arange(len(club_dict)))
ax.set_xticklabels(labels, rotation='45', ha='right')
ax.set_xlabel('Club Tags')
ax.set_ylabel('Number of Occurrances')
ax.set_title('Number of Club Tag Occurrances')
for rect in bar:
height = rect.get_height()
ax.annotate('{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3),
textcoords="offset points",
ha='center',
va='bottom')
plt.tight_layout()
output = io.BytesIO()
FigureCanvas(fig).print_png(output)
return Response(output.getvalue(), mimetype='image/png')
def clubs_per_user(user_list):
"""
Creates a bar graph of the number of clubs per user.
Using a dictionary to gather each user's name and the number of club each
user is in, numpy representations of the data is used to create a matplotlib
bar graph. The graph is then formatted and pushed for view on the server.
Returns:
--------
Response
The graph
"""
user_club_dict = {}
for user in user_list:
name = user.get_user_name()
user_club_dict[name] = len(user.get_user_clubs())
x = np.zeros(len(user_club_dict))
index_counter = 0
for user in user_club_dict:
x[index_counter] = user_club_dict.get(user)
index_counter = index_counter + 1
fig = plt.figure()
ax = fig.add_subplot()
bar = ax.bar(np.arange(len(user_club_dict)), x)
labels = user_club_dict.keys()
ax.set_xticks(np.arange(len(user_club_dict)))
ax.set_xticklabels(labels, rotation='45', ha='right')
ax.set_xlabel('User Name')
ax.set_ylabel('Number of Clubs')
ax.set_title('Number of Clubs per User')
for rect in bar:
height = rect.get_height()
ax.annotate('{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3),
textcoords="offset points",
ha='center',
va='bottom')
plt.tight_layout()
output = io.BytesIO()
FigureCanvas(fig).print_png(output)
return Response(output.getvalue(), mimetype='image/png')