Electricity production plants - Visualisation Plots¶

Data origins from https://opendata.swiss/en/dataset/elektrizitatsproduktionsanlagen and we use the following .csv files.

ElectricityProductionPlant.csv
MainCategoryCatalogue.csv
SubCategoryCatalogue.csv
PlantCategoryCatalogue.csv

Loading data¶

We use pd.read_csv to read the csv files into a DataFrame.

After reading we set the index to the corresponding column which makes it easier to join tables and join the (sub)-category names in order to have all information in one epp table

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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

plt.rcParams["figure.figsize"] = (20, 10)

epp = pd.read_csv('../data/ch.bfe.elektrizitaetsproduktionsanlagen/ElectricityProductionPlant.csv', parse_dates=['BeginningOfOperation']).set_index('xtf_id')
mainCat = pd.read_csv('../data/ch.bfe.elektrizitaetsproduktionsanlagen/MainCategoryCatalogue.csv').set_index('Catalogue_id')
subCat = pd.read_csv('../data/ch.bfe.elektrizitaetsproduktionsanlagen/SubCategoryCatalogue.csv').set_index('Catalogue_id')
plantCat = pd.read_csv('../data/ch.bfe.elektrizitaetsproduktionsanlagen/PlantCategoryCatalogue.csv').set_index('Catalogue_id')

lang='de'

epp = epp.merge(mainCat[lang].rename("MainCategoryName"),how='left', left_on='MainCategory', right_index=True)
epp = epp.merge(subCat[lang].rename("SubCategoryName"),how='left', left_on='SubCategory', right_index=True)
epp = epp.merge(plantCat[lang].rename("PlantCategoryName"),how='left', left_on='PlantCategory', right_index=True)

display(epp.head())

	Address	PostCode	Municipality	Canton	BeginningOfOperation	InitialPower	TotalPower	MainCategory	SubCategory	PlantCategory	_x	_y	MainCategoryName	SubCategoryName	PlantCategoryName
xtf_id
5646	Rue des Creusets 41	1948	Fionnay	VS	1958-03-07	1872000.0	1872000.0	maincat_1	subcat_1	plantcat_6	2589880.0	1097661.0	Wasserkraft	Wasserkraft	Pumpspeicherkraftwerk
5686	Binenweg 5	3904	Naters	VS	1969-09-01	349576.0	349576.0	maincat_1	subcat_1	plantcat_7	2644115.0	1131390.0	Wasserkraft	Wasserkraft	Speicherkraftwerk
5726	Robbia 504G	7741	San Carlo	GR	1910-11-03	29150.0	29150.0	maincat_1	subcat_1	plantcat_2	2801863.0	1136379.0	Wasserkraft	Wasserkraft	Ausleitkraftwerk
5727	Via Principale 16	7744	Campocologno	GR	1907-03-01	55000.0	55000.0	maincat_1	subcat_1	plantcat_7	2808646.0	1123676.0	Wasserkraft	Wasserkraft	Speicherkraftwerk
5730	Büdemli 65B	7240	Küblis	GR	1922-01-01	44200.0	44200.0	maincat_1	subcat_1	plantcat_7	2778481.0	1198505.0	Wasserkraft	Wasserkraft	Speicherkraftwerk

Pie chart¶

Exercise: Plot the energy mix in a pie chart a Series

Create a pie chart that displays the relative contribution of the main categories to the total power produced in Switzerland.
Create a pie chart that displays the relative contribution of the main categories to the total number of power plants in Switzerland.
(Optional) Can you plot both figures as subplots in one plot?
(Optional) Plot the relative contribution for the sub categories for maincat_2.

Hints

To prepare the necessary DataFrame you can use the DataFrame.groupby() function and sum on the TotalPower column: epp.groupby('MainCategoryName')['TotalPower'].sum()
To plot you can use directly the function Series.plot.pie() Pandas Doc
To count instead of sum a grouped value use .count()

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Bar chart¶

Exercise: Plot the energy data by canton

Use sns.countplot to create a bar chart that displays the number of fossil fuel power plants (epp.MainCategory == 'maincat_4') for each canton. Can you also display the different sub-categories?
Create a stacked bar chart that displays the contribution to alternative energies (epp.MainCategory == 'maincat_2') for each canton.
(Optional) Can you create a similar bar chart with seaborn, i.e. sns.barplot()? P
(Optional) Play around with the different seaborn themes darkgrid, whitegrid, dark, white, and ticks.

Hints

With sns.countplot you can provide your entier dataset
To prepare the necessary DataFrame you can use the DataFrame.groupby() function and sum on the TotalPower column: epp.groupby(['Canton', 'SubCategoryName'], as_index=False)['TotalPower'].sum(). Furthermore, you can use DataFrame.pivot_table() to explode the sub categories into columns.

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History Plots¶

Exercise: Plot the energy mix over time

Step 1: Create a line plot over the time that cumulates the energy that is produced until then.

Step 2: Create a line plot with multiple lines. Each line represents the cumulative sum of the energy produced by one main category

Step 3: Create an area plot with the same data as in step 2. With the area plot you can show the individual as well as the total values.

Hints

Step 1:
- With df = epp.groupby('BeginningOfOperation')['TotalPower' ].sum().sort_index() you can create a DataFrame with the added TotalPower for each BeginningOfOperations date
- use the df.cumsum function to return the cumulative sum that you can then plot directly with pandas into a df.plot.line plot.
Step 2:
- Do the groupby not only by BeginningOfOperation but by ['BeginningOfOperation', 'MainCategoryName']. Use as_index=False to have a DataFrame as a result of the grouping and not a Series
- use DataFrame.pivot_table() to explode the main categories into columns.
- use data.replace(np.nan, 0) to replace missing values with 0

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