end of day2

src/T15_EnergyPlots.py

@@ -1,5 +1,44 @@
 # 1. Energie nach Quartal
 #   alle energieformen in einen plot
+import matplotlib.pyplot as plt
+import pandas as pd
+energy_df = pd.read_csv("../data/germany_energy_mix_2019_2024.csv")
+# 1) Neue spalte
+energy_df["Year Quarter"] = energy_df["Year"].astype(str) + " " + energy_df['Quarter']
+energy_df.drop(["Year", "Quarter"], axis=1, inplace=True)
+quarterly_data = energy_df.pivot_table(
+    index="Year Quarter",
+    columns="Energy_Source",
+    values="Generation_TWh",
+    aggfunc="sum",
+    fill_value=0,
+)
+
+print(quarterly_data)
+colors = {
+    'Solar': '#FFD700',           # Gold
+    'Wind_Onshore': '#87CEEB',    # Sky Blue
+    'Wind_Offshore': '#4682B4',   # Steel Blue
+    'Hydroelectric': '#00CED1',   # Dark Turquoise
+    'Biomass': '#32CD32',         # Lime Green
+    'Natural_Gas': '#FF6347',     # Tomato
+    'Coal_Hard': '#2F4F4F',       # Dark Slate Gray
+    'Coal_Lignite': '#696969',    # Dim Gray
+    'Nuclear': '#FF4500',         # Orange Red
+    'Oil': '#8B4513'              # Saddle Brown
+}
+
+def energy_plots():
+    quarterly_data.plot(color=colors)
+    plt.show()
+
+    quarterly_data.plot.area(color=colors, stacked=True)
+    plt.show()
+
+    quarterly_data.plot.box()
+    plt.show()
+
+# .area(stacked=True)
 
 # 2. In 2 Plots übereinander
 #   - greenenergies
@@ -9,4 +48,10 @@
 # 3. Alle energien nach type in einem barplot
 #          - pie-plot
 
+all_power = quarterly_data.sum()
+print(all_power.T)
+p2 = all_power.reset_index()
+print(p2)
+all_power.plot.pie(colors=list(colors.values()))
+plt.show()
 # df.plot.pie