def q05_feature_engineering_part3(path):
tss,df=q01_load_data(path)
df.info()
df['hour']=df['Datetime'].dt.hour
df['month']=df['Datetime'].dt.strftime('%b')
sns.factorplot(x='month', y='Demand', data=df, kind='box', order=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug','Sep', 'Oct', 'Nov', 'Dec'], size=8, aspect=float(16/7))
def q03_time_plot(path):
shape, data = q01_load_data(path)
plt.figure(figsize=(16, 7))
plt.plot(data.Datetime, data.Demand)
plt.title('Electricity Demand in Australia for a year')
plt.xlabel('Time')
plt.ylabel('Demand')
plt.show()
def q03_time_plot(path):
path = 'data/elecdemand.csv'
shape, df = q01_load_data(path)
plt.plot(df['Datetime'], df['Demand'])
plt.title('Electricity Demand for Australia for a year')
plt.xlabel('Year-Month')
plt.ylabel('Demand')
plt.show()
def q05_feature_engineering_part4(path):
df_shape, df = q01_load_data(path)
df['hour'] = df['Datetime'].dt.hour
df['month'] = df['Datetime'].dt.strftime('%b')
df['Peakhours'] = ((df['hour'] >= 6) & (df['hour'] < 20)) * 1
df['Peakmonths'] = df['month'].apply(
lambda x: 1 if x in ['Feb', 'May', 'Jun', 'Jul', 'Aug'] else 0)
return df
def q04_boxplot(path):
shp,df=q01_load_data(path)
plt.figure(figsize=(16, 7))
sns.factorplot(x='WorkDay', y='Demand', data=df, kind='box', size=8, aspect=float(16/7))
plt.xlabel('Workday')
plt.ylabel('Demand')
plt.title('Each workday demand in Australia')
def q03_time_plot(path):
shp, df = q01_load_data(path)
plt.figure(figsize=(16, 6))
plt.plot(df['Datetime'], df['Demand'])
plt.xlabel('Time')
plt.ylabel('Demand')
plt.title('Electricity Demand in Australia for a year')
plt.show()
def q02_data_splitter(path):
path = 'data/elecdemand.csv'
shape, df = q01_load_data(path)
tscv = TimeSeriesSplit(n_splits=2)
com_idx = []
for train_index, valid_index in tscv.split(df):
com_idx.append((train_index, valid_index))
return com_idx
def q05_feature_engineering(path):
shape,data = q01_load_data(path)
corr = data['Temperature'].corr(data['Demand'])
plt.figure(figsize=(16,6))
plt.scatter(data['Temperature'],data['Demand'])
plt.title('Temperature vs. Demand')
plt.xlabel('Temperature')
plt.ylabel('Demand')
plt.show()
def q05_feature_engineering_part4(path):
shape, data = q01_load_data(path)
data['hour'] = data['Datetime'].dt.hour
data['month'] = data['Datetime'].dt.strftime('%b')
data['Peakhours'] = data['hour'].apply(lambda x: 1
if x in range(6, 20) else 0)
data['Peakmonths'] = data['month'].apply(
lambda x: 1 if x in ['Feb', 'May', 'Jun', 'Jul', 'Aug'] else 0)
return data
def q05_feature_engineering_part2(path):
df_shape, df = q01_load_data(path)
df['hour'] = df['Datetime'].dt.hour
hours = []
for i in range(24):
one = df[df['hour'] == i]['Demand'].values
hours.append(one)
plt.boxplot(hours, labels=[str(i) for i in range(24)])
plt.show()
def q05_feature_engineering(path):
path = 'data/elecdemand.csv'
shape, df = q01_load_data(path)
corr, p_value = pearsonr(df['Temperature'], df['Demand'])
plt.scatter(df['Temperature'], df['Demand'])
plt.xlabel('Temperature')
plt.ylabel('Demand')
plt.title('Temperature vs Demand')
plt.show()
def q02_data_splitter(path):
seed = 9
shape, df = q01_load_data(path)
tssf = TimeSeriesSplit(n_splits=3)
trainl = ()
validl = ()
for train_index, valid_index in tssf.split(df):
trainl = trainl + tuple(train_index)
validl = validl + tuple(valid_index)
return [[trainl, trainl], [validl, validl]]
def q05_feature_engineering_part3(path):
df_shape, df = q01_load_data(path)
df['month'] = df['Datetime'].dt.strftime('%b')
demands = []
months = [
'Jan', 'Feb', 'Mar', 'Apr', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov',
'Dec'
]
for month in months:
temp = df[df['month'] == month]['Demand']
demands.append(list(temp))
plt.boxplot(demands, labels=months)
def q05_feature_engineering_part3(path):
shape, data = q01_load_data(path)
data['Month'] = data['Datetime'].dt.strftime('%b')
sns.factorplot(x='Month',
y='Demand',
data=data,
kind='box',
size=8,
aspect=(16 / 9))
plt.title('Change in Demand of Electricity wrt Month')
plt.xlabel('Month')
plt.ylabel('Demand')
plt.show()
def q04_boxplot(path):
shape, data = q01_load_data(path)
data.head()
plt.figure(figsize=(16, 7))
sns.factorplot(x='WorkDay',
y='Demand',
data=data,
kind='box',
size=8,
aspect=float(16 / 7))
plt.xlabel('Workday')
plt.ylabel('Demand')
plt.title('Change in Electricity Demand wrt to Demand')
plt.show()
def q05_feature_engineering_part2(path):
shape, data = q01_load_data(path)
data['hour'] = data['Datetime'].dt.hour
hour = []
for i in range(24):
hour_demand = data[data['hour'] == i]['Demand'].values
hour.append(hour_demand)
plt.figure(figsize=(16, 6))
plt.boxplot(hour, labels=[str(i) for i in range(24)])
plt.xlabel('Hour')
plt.ylabel('Demand')
plt.title('Change in Electricity demand wrt to Hour')
plt.show()
def q05_feature_engineering_part2(path):
shape, data = q01_load_data(path)
'write your solution here'
data['hour'] = data['Datetime'].dt.hour
plt.figure(figsize=(16, 6))
hours = []
for i in range(24):
one = data[data['hour'] == i]['Demand'].values
hours.append(one)
plt.boxplot(hours, labels=[str(i) for i in range(24)])
plt.xlabel('Hour')
plt.ylabel('Demand')
plt.title('Change in Electricity demand wrt to Hour')
def q05_feature_engineering_part3(path):
path = 'data/elecdemand.csv'
shape, data = q01_load_data(path)
data['hour'] = data['Datetime'].dt.hour
data['month'] = data['Datetime'].dt.month
plt.figure(figsize=(16, 6))
demand_hours = []
for i in range(1, 25):
one = data[data['hour'] == i]['Demand'].values
demand_hours.append(one)
demand_months = []
for j in range(1, 13):
demand_months.append(data[data['month'] == j]['Demand'].values)
plt.subplot(211)
plt.boxplot(demand_hours, labels=[str(i) for i in range(1, 25)])
plt.xlabel('Hour')
plt.ylabel('Demand')
plt.title('Change in Electricity demand wrt to Hour')
plt.subplot(212)
plt.boxplot(demand_months, labels=[str(i) for i in range(1, 13)])
plt.xlabel('Months')
plt.ylabel('Demand')
plt.title('Change in Electricity demand wrt to months')
plt.show()
def q04_boxplot(path):
path = 'data/elecdemand.csv'
shape, df = q01_load_data(path)
df.boxplot(column=['Demand'], by=['WorkDay'])
plt.show()
def q04_boxplot(path):
df_shape, df = q01_load_data(path)
sns.boxplot(x='WorkDay', y='Demand', data=df)
def q03_time_plot(path):
df_shape,df = q01_load_data(path)
plt.plot(df['Datetime'],df['Demand'])
def q02_data_splitter(path):
np.random.seed(9)
shape, data = q01_load_data(path)
tscv = TimeSeriesSplit(n_splits=2)
split_data = list(tscv.split(data))
return split_data
def q05_feature_engineering(path):
df_shape, df = q01_load_data(path)
plt.scatter(df['Temperature'], df['Demand'])
def q05_feature_engineering(path):
tss, df = q01_load_data(path)
p_corf = np.corrcoef(df['Temperature'], df['Demand'])
plt.scatter(df['Temperature'], df['Demand'])
plt.show()
def q02_data_splitter(path):
np.random.seed(9)
df_shape,df = q01_load_data(path)
time_series_Split = TimeSeriesSplit(n_splits=2)
train = time_series_Split.split(X=df)
return list(train)
