frames for day3

src/T15_EnergyPlots.py

@@ -2,6 +2,8 @@
 #   alle energieformen in einen plot
 import matplotlib.pyplot as plt
 import pandas as pd
+from pandas.core.config_init import max_cols
+
 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']
@@ -43,7 +45,51 @@ def energy_plots():
 # 2. In 2 Plots übereinander
 #   - greenenergies
 #   - nongreenenergies
+green_sources = ['Solar', 'Wind_Onshore', 'Wind_Offshore', 'Hydroelectric', 'Biomass']
+nongreen_sources = ['Natural_Gas', 'Coal_Hard', 'Coal_Lignite', 'Nuclear', 'Oil']
 
+fig, (ax1, ax2) = plt.subplots(2, 1,
+                               figsize=(15, 12),
+                               sharex=True)  # als pdf, png speichern
+
+# top-plot die grünen Energien
+for source in green_sources:
+    ax1.plot(quarterly_data.index,
+             quarterly_data[source],
+             color=colors.get(source, "green"),
+             label=source,
+             marker="o", linewidth=2, markersize=4)
+
+for source in nongreen_sources:
+    ax2.plot(quarterly_data.index,
+             quarterly_data[source],
+             color=colors.get(source, "brown"),
+             label=source,
+             marker="o", linewidth=2, markersize=4)
+
+ax1.set_title("Erneuerbaren Energien")
+ax2.set_title("Nichterneuerbaren Energien")
+
+#           jede spalte 1 max = series => max -> einen wert
+max_value = quarterly_data.max().max() # * 1.05
+max_value = max_value + (10 - max_value % 10)
+min_value = quarterly_data.min().min()
+print(max_value)
+for ax in [ax1, ax2]:
+    ax.set_ylim(min_value, max_value)
+    ax.grid(True, alpha=0.3)
+    ax.legend()
+    ax.set_ylabel("EnergieProduktion [TWh]")
+
+# nur jedes 2te quartal
+labels = ax2.get_xticklabels()
+for i, label in enumerate(labels):
+    if i % 2 == 1:
+        label.set_visible(False)
+
+fig.tight_layout()  # enger zusammen
+fig.savefig("energy_by_quarter.pdf")  # pdf, svg, eps
+plt.show()
 
 # 3. Alle energien nach type in einem barplot
 #          - pie-plot

src/T16_plotly_expls.py

@@ -0,0 +1,100 @@
+import pandas as pd
+import plotly   # pip/conda install plotly
+# pip install kaleido
+# javascript welches den plot enthält
+import plotly.io as pio
+
+pio.renderers.default = "browser"
+# T14_plotten.py
+cities = {"Stadt": ["London", "Berlin", "Madrid", "Rom", "Paris", "Wien", "Bukarest", "Hamburg", "Budapest", "Warsaw", "Barcelona", "München", "Mailand"],
+          "Population": [8615246, 3562166, 3165235, 2874038, 2273305, 1805681, 1803425, 1760433, 1754000, 1740119, 1602386, 1493900, 1350680],
+          "Land": ["England", "Deutschland", "Spanien", "Italien", "Frankreich", "Österreich", "Romanien", "Deutschland", "Ungarn", "Polen", "Spanien", "Deutschland", "Italien"]}
+cities_df = pd.DataFrame(cities)
+areas = [1572, 892, 604,  1285, 105,415, 228, 755, 525, 517, 101, 310, 182]
+cities_df = pd.DataFrame(cities)
+cities_df["Flaeche"] = areas
+
+# **kwargs
+fig = cities_df.plot(x="Stadt", y=["Population", "Flaeche"], backend="plotly")
+fig.show()
+
+
+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
+}
+pd.options.plotting.backend = 'plotly'  # backend gesetzt
+fig = quarterly_data.plot()
+for i, (col, color) in enumerate(colors.items()):
+    print(quarterly_data.columns.get_loc(col))
+
+    fig.data[quarterly_data.columns.get_loc(col)].line.color = color
+
+fig.show()
+
+# klassische Ansatz
+import plotly.graph_objs as go
+fig = go.Figure()
+
+for column in quarterly_data.columns:
+    fig.add_trace(go.Scatter(
+        x=quarterly_data.index,
+        y=quarterly_data[column],
+        mode='lines',
+        name=column,
+        line={'color': colors.get(column, "#000000")}
+    ))
+
+fig.update_layout(
+    title="Quarterly energy production",
+    xaxis_title="Year Quarter",
+    yaxis_title="Generation [TWh]"
+)
+
+fig.show()
+
+# '2019 Q1'  Biomass  5.2
+# '2019 Q1'  Solar    9.4
+print(quarterly_data)
+#melted = quarterly_data.reset_index().melt(id_vars="Year Quarter", value_vars=[])
+melted = quarterly_data.melt(ignore_index=False)
+fig = melted.plot(x=melted.index,
+            y=melted["value"],
+            colors='Energy_Source',
+            color_discrete_map=colors)
+fig.show()
+
+fig = go.Figure()
+fig.add_trace(go.Bar(
+    x=melted.index,
+    y=melted["value"],
+    )
+)
+fig.update_layout(
+    title="Bars of energy",
+    xaxis_title="Year Quarter",
+    yaxis_title="Generation [TWh]"
+)
+
+fig.show()

src/T17_merging_of_frames.py

@@ -0,0 +1,22 @@
+import pandas as pd
+
+
+kunden1 = pd.DataFrame({
+    'kunden_id': [1, 2],
+    'name': ['Anna', 'Ben'],
+    'stadt': ['Berlin', 'München']
+})
+
+kunden2 = pd.DataFrame({
+    'kunden_id': [3, 4],
+    'name': ['Clara', 'David'],
+    'stadt': ['Hamburg', 'Köln']
+})
+
+
+bestellungen = pd.DataFrame({
+    'bestell_id': [101, 102, 103, 104],
+    'kunden_id': [1, 2, 1, 5],
+    'produkt': ['Laptop', 'Maus', 'Tastatur', 'Monitor'],
+    'preis': [800, 25, 50, 300]
+})