In [5]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
import matplotlib as mpl
import matplotlib.dates as mdates
import datetime
# Set the matplotlib settings (eventually this will go at the top of the graph_util)
mpl.rcParams['axes.labelsize'] = 16
mpl.rcParams['axes.titlesize'] = 20
mpl.rcParams['legend.fontsize'] = 16
mpl.rcParams['font.size'] = 16.0
mpl.rcParams['figure.figsize'] = [15,10]
mpl.rcParams['xtick.labelsize'] = 16
mpl.rcParams['ytick.labelsize'] = 16
# Set the style for the graphs
plt.style.use('bmh')
# Additional matplotlib formatting settings
months = mdates.MonthLocator()
# This formats the months as three-letter abbreviations
months_format = mdates.DateFormatter('%b')
In [6]:
def area_cost_distribution(df, fiscal_year_col, utility_col_list, filename):
# Inputs include the dataframe, the column name for the fiscal year column, and the list of column names for the
# different utility bills. The dataframe should already include the summed bills for each fiscal year.
fig, ax = plt.subplots()
# Take costs for each utility type and convert to percent of total cost by fiscal year
df['total_costs'] = df[utility_col_list].sum(axis=1)
percent_columns = []
for col in utility_col_list:
percent_col = "Percent " + col
percent_columns.append(percent_col)
df[percent_col] = df[col] / df.total_costs
# Create stacked area plot
ax.stackplot(df[fiscal_year_col], df[percent_columns].T, labels=percent_columns)
# Format the y axis to be in percent
ax.yaxis.set_major_formatter(FuncFormatter('{0:.0%}'.format))
# Format the x-axis to include all fiscal years
plt.xticks(np.arange(df[fiscal_year_col].min(), df[fiscal_year_col].max()+1, 1.0))
# Add title and axis labels
plt.title('Annual Utility Cost Distribution')
plt.ylabel('Utility Cost Distribution')
plt.xlabel('Fiscal Year')
# Add legend
plt.legend()
# Make sure file goes in the proper directory
folder_and_filename = 'output/images/' + filename
# Save and show
plt.savefig(folder_and_filename)
plt.show()
In [7]:
def area_use_distribution(df, fiscal_year_col, utility_col_list, filename):
# Inputs include the dataframe, the column name for the fiscal year column, and the list of column names for the
# different utility bills. The dataframe should already include the summed bills for each fiscal year.
fig, ax = plt.subplots()
# Take usage for each utility type and convert to percent of total cost by fiscal year
df['total_use'] = df[utility_col_list].sum(axis=1)
percent_columns = []
for col in utility_col_list:
percent_col = "Percent " + col
percent_columns.append(percent_col)
df[percent_col] = df[col] / df.total_use
# Create stacked area plot
ax.stackplot(df[fiscal_year_col], df[percent_columns].T, labels=percent_columns)
# Format the y axis to be in percent
ax.yaxis.set_major_formatter(FuncFormatter('{0:.0%}'.format))
# Format the x-axis to include all fiscal years
plt.xticks(np.arange(df[fiscal_year_col].min(), df[fiscal_year_col].max()+1, 1.0))
# Add title and axis labels
plt.title('Annual Energy Usage Distribution')
plt.ylabel('Annual Energy Usage Distribution')
plt.xlabel('Fiscal Year')
# Add legend
plt.legend()
# Make sure file goes in the proper directory
folder_and_filename = 'output/images/' + filename
# Save and show
plt.savefig(folder_and_filename)
plt.show()
In [8]:
def create_stacked_bar(df, fiscal_year_col, column_name_list, filename):
# Parameters include the dataframe, the name of the column where the fiscal year is listed, a list of the column names
# with the correct data for the chart, and the filename where the output should be saved.
# Create the figure
plt.figure()
# Set the bar width
width = 0.50
# Create the stacked bars. The "bottom" is the sum of all previous bars to set the starting point for the next bar.
previous_col_name = 0
for col in column_name_list:
short_col_name = col.split(" Cost")[0]
short_col_name = plt.bar(df[fiscal_year_col], df[col], width, label=short_col_name, bottom=previous_col_name)
previous_col_name = previous_col_name + df[col]
# label axes
plt.ylabel('Utility Cost [$]')
plt.xlabel('Fiscal Year')
plt.title('Total Annual Utility Costs')
# Make one bar for each fiscal year
plt.xticks(np.arange(df[fiscal_year_col].min(), df[fiscal_year_col].max()+1, 1.0),
np.sort(list(df[fiscal_year_col].unique())))
# Set the yticks to go up to the total cost in increments of 100,000
df['total_cost'] = df[column_name_list].sum(axis=1)
plt.yticks(np.arange(0, df.total_cost.max(), 100000))
plt.legend()
# Make sure file goes in the proper directory
folder_and_filename = 'output/images/' + filename
# Save and show
plt.savefig(filename)
plt.show()
In [9]:
def energy_use_stacked_bar(df, fiscal_year_col, column_name_list, filename):
# Parameters include the dataframe, the name of the column where the fiscal year is listed, a list of the column names
# with the correct data for the chart, and the filename where the output should be saved.
# Create the figure
plt.figure()
# Set the bar width
width = 0.50
# Create the stacked bars. The "bottom" is the sum of all previous bars to set the starting point for the next bar.
previous_col_name = 0
for col in column_name_list:
short_col_name = col.split(" [MMBTU")[0]
short_col_name = plt.bar(df[fiscal_year_col], df[col], width, label=short_col_name, bottom=previous_col_name)
previous_col_name = previous_col_name + df[col]
# label axes
plt.ylabel('Annual Energy Usage [MMBTU]')
plt.xlabel('Fiscal Year')
plt.title('Total Annual Energy Usage')
# Make one bar for each fiscal year
plt.xticks(np.arange(df[fiscal_year_col].min(), df[fiscal_year_col].max()+1, 1.0),
np.sort(list(df[fiscal_year_col].unique())))
# Set the yticks to go up to the total usage in increments of 1,000
df['total_use'] = df[column_name_list].sum(axis=1)
plt.yticks(np.arange(0, df.total_use.max(), 1000))
plt.legend()
# Make sure file goes in the proper directory
folder_and_filename = 'output/images/' + filename
# Save and show
plt.savefig(folder_and_filename)
plt.show()
In [10]:
def usage_pie_charts(df, use_or_cost_cols, chart_type, filename):
# df: A dataframe with the fiscal_year as the index and needs to include the values for the passed in list of columns.
# use_or_cost_cols: a list of the energy usage or energy cost column names
# chart_type: 1 for an energy use pie chart, 2 for an energy cost pie chart
# Get the three most recent complete years of data
complete_years = df.query("month_count == 12.0")
sorted_completes = complete_years.sort_index(ascending=False)
most_recent_complete_years = sorted_completes[0:3]
years = list(most_recent_complete_years.index.values)
# Create percentages from usage
most_recent_complete_years = most_recent_complete_years[use_or_cost_cols]
most_recent_complete_years['Totals'] = most_recent_complete_years.sum(axis=1)
for col in use_or_cost_cols:
most_recent_complete_years[col] = most_recent_complete_years[col] / most_recent_complete_years.Totals
most_recent_complete_years = most_recent_complete_years.drop('Totals', axis=1)
for col in use_or_cost_cols:
if most_recent_complete_years[col].iloc[0] == 0:
most_recent_complete_years = most_recent_complete_years.drop(col, axis=1)
# Create a pie chart for each of 3 most recent complete years
for year in years:
year_df = most_recent_complete_years.query("fiscal_year == @year")
plt.figure()
fig, ax = plt.subplots()
ax.pie(list(year_df.iloc[0].values), labels=list(year_df.columns.values), autopct='%1.1f%%',
shadow=True, startangle=90)
# Create the title based on whether it is an energy use or energy cost pie chart.
if chart_type == 1:
title = "FY " + str(year) + " Energy Usage [MMBTU]"
else:
titel = "FY " + str(year) + " Energy Cost [$]"
plt.title(title)
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
# Make sure file goes in the proper directory
folder_and_filename = 'output/images/' + filename + str(year)
# Save and show
plt.savefig(folder_and_filename)
plt.show()
In [11]:
def create_monthly_profile(df, graph_column_name, yaxis_name, color_choice, filename):
# Parameters:
# df: A dataframe with the fiscal_year, fiscal_mo, and appropriate graph column name ('kWh', 'kW', etc.)
# graph_column_name: The name of the column containing the data to be graphed on the y-axis
# yaxis_name: A string that will be displayed on the y-axis
# color_choice: 'blue', 'red', or 'green' depending on the desired color palette.
# Additional matplotlib formatting settings
months = mdates.MonthLocator()
# This formats the months as three-letter abbreviations
months_format = mdates.DateFormatter('%b')
# Get five most recent years
recent_years = (sorted(list(df.index.levels[0].values), reverse=True)[0:5])
# Reset the index of the dataframe for more straightforward queries
df_reset = df.reset_index()
def get_date(row):
# Converts the fiscal year and fiscal month columns to a datetime object for graphing
# Year is set to 2016-17 so that the charts overlap; otherwise they will be spread out by year.
# The "year trick" allows the graph to start from July so the seasonal energy changes are easier to identify
if row['fiscal_mo'] > 6:
year_trick = 2016
else:
year_trick = 2017
return datetime.date(year=year_trick, month=row['fiscal_mo'], day=1)
# This creates a new date column with data in the datetime format for graphing
df_reset['date'] = df_reset[['fiscal_year', 'fiscal_mo']].apply(get_date, axis=1)
# Create a color dictionary of progressively lighter colors of three different shades and convert to dataframe
color_dict = {'blue': ['#08519c', '#3182bd', '#6baed6', '#bdd7e7', '#eff3ff'],
'red': ['#a50f15', '#de2d26', '#fb6a4a', '#fcae91', '#fee5d9'],
'green': ['#006d2c', '#31a354', '#74c476', '#bae4b3', '#edf8e9']
}
color_df = pd.DataFrame.from_dict(color_dict)
# i is the counter for the different colors
i=0
# Create the plots
fig, ax = plt.subplots()
for year in recent_years:
# Create df for one year only so it's plotted as a single line
year_df = electric_pivot_monthly_reset.query("fiscal_year == @year")
year_df = year_df.sort_values(by='date')
# Plot the data
ax.plot_date(year_df['date'], year_df[graph_column_name], fmt='-', color=color_df.iloc[i][color_choice],
label=str(year_df.fiscal_year.iloc[0]))
# Increase counter by one to use the next color
i += 1
# Format the dates
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(months_format)
fig.autofmt_xdate()
# Add the labels
plt.xlabel('Month of Year')
plt.ylabel(yaxis_name)
plt.legend()
# Make sure file goes in the proper directory
folder_and_filename = 'output/images/' + filename
# Save and show
plt.savefig(folder_and_filename)
plt.show()