day2 intermediate code3

src/T12_Datetimes.py

@@ -34,12 +34,44 @@ daily = beverages_by_date.resample("8h").bfill()
 print(daily.loc["2024-02-8":"2024-02-14"])
 
 # übung mit zeiten
-solar_df = pd.read_csv("../data/Balkonkraftwerk.csv", index_col=0)
+solar_df = pd.read_csv("../data/Balkonkraftwerk.csv",
+                       index_col=0)
 solar_df.index = pd.to_datetime(solar_df.index)
 print(solar_df)
 print(solar_df.columns)
 
-# 1) Wie sieht es im durchschnitt jeden Tag aus (D)
-# 2) An welchen Tagen war die effizientz > 35%
+print(solar_df.index)
+
+# 1) Wie sieht es im Durchschnitt jeden Tag aus (D)
+daily = solar_df.resample("d").mean()
+print(daily)
+print(daily[daily["efficiency_percent"] > 37])
+# 2) An welchen Tagen war die durchschnittliche effizientz > 35%
+# 2)                          maximum              > 70%
 # 3) Stündliche Werte interpolieren  (h)  (1h), (3h)
+
+hourly = solar_df.resample("h").ffill()
+print(hourly)
+
 #    - Komisch
+
+print(hourly.loc["2024-05-02"])
+
+print()
+daily = beverages_by_date.resample("8h").bfill()
+print(daily.loc["2024-02-8":"2024-02-14"])
+
+values = pd.DataFrame({
+    "times": ["2025-08-01 05:00:00", "2025-08-01 09:00:00", "2025-08-01 11:00:00"],
+    "values": [1, 2, 10]
+})
+values.set_index("times", inplace=True)
+values.index = pd.to_datetime(values.index)
+print("-"*100)
+print(values)
+values = values.resample("h").interpolate()
+print(values)
+values.index = values.index.tz_localize('Europe/Berlin')
+print(values)
+values.index = values.index.tz_convert("Us/Central")
+print(values)

src/T13_GroupbyExtra.py

@@ -0,0 +1,25 @@
+import numpy as np
+import pandas as pd
+
+names = ('Ortwin', 'Mara', 'Siegrun', 'Sylvester', 'Metin', 'Adeline', 'Utz', 'Susan', 'Gisbert', 'Senol')
+data = {'Monday': np.array([0, 9, 2, 3, 7, 3, 9, 2, 4, 9]),
+        'Tuesday': np.array([2, 6, 3, 3, 5, 5, 7, 7, 1, 0]),
+        'Wednesday': np.array([6, 1, 1, 9, 4, 0, 8, 6, 8, 8]),
+        'Thursday': np.array([1, 8, 6, 9, 9, 4, 1, 7, 3, 2]),
+        'Friday': np.array([3, 5, 6, 6, 5, 2, 2, 4, 6, 5]),
+        'Saturday': np.array([8, 4, 8, 2, 3, 9, 3, 4, 9, 7]),
+        'Sunday': np.array([0, 8, 7, 8, 9, 7, 2, 0, 5, 2])}
+
+# T property für .transpose()
+data_df = pd.DataFrame(data, index=names).transpose()  # achtung hier wird transponiert (spalten zu zeilen und zeilen zu spalten)
+print(data_df)
+
+def categorize_day(day):
+    if day in {"Saturday", "Sunday"}:
+        return "Weekend"
+    return "Weekday"
+
+# die spalte nach welcher gruppiert wird in diesem Fall auf den Index gesetzt wird
+res = data_df.groupby(categorize_day).sum()
+print(res)
+

src/T14_Plotten.py

@@ -0,0 +1,70 @@
+# ältere Bibliothek
+# - statische Plots
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# interaktion
+# import plotly
+
+
+# plotten direkt von pandas
+#  -> backend: matplotlib
+
+
+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"]}
+
+areas = [1572, 892, 604,  1285, 105,415, 228, 755, 525, 517, 101, 310, 182]
+cities_df = pd.DataFrame(cities)
+cities_df["Flaeche"] = areas
+print(cities_df)
+
+#cities_df.plot(x="Stadt", y=["Population", "Flaeche"])
+#plt.xticks(range(len(cities_df)), cities_df["Stadt"], rotation=70)
+#plt.show()
+
+fig, ax = plt.subplots()
+cities_df.plot(x="Stadt", y=["Population", "Flaeche"], ax=ax)
+ax.set_xticks(range(len(cities_df)), cities_df["Stadt"], rotation=70)
+fig.suptitle("Area und Flaeche in einem")
+
+fig, ax = plt.subplots(2, 1, sharex=True)  # ax ein array mit 2 elementen
+cities_df.plot(x="Stadt", y="Population", ax=ax[0])
+cities_df.plot(x="Stadt", y="Flaeche", ax=ax[1])
+fig.suptitle("2 axes in einer figure")
+for i in range(2):
+    ax[i].set_xticks(range(len(cities_df)), cities_df["Stadt"], rotation=70)
+
+fig, ax = plt.subplots()
+
+area_axes = ax.twinx()
+
+width = 0.35
+
+x_pos = range(len(cities_df))  # 0, 1, 2, ..., 12
+ax.bar([x - width/2 for x in x_pos], cities_df["Population"], width=width, color="blue", label="Population")
+area_axes.bar([x + width/2 for x in x_pos], cities_df["Flaeche"], width=width, color="orange", label="Flaeche")
+
+ax.set_xticks(range(len(cities_df)), cities_df["Stadt"], rotation=70)
+fig.suptitle("Area und Flaeche in einem")
+handles1, labels1 = ax.get_legend_handles_labels()
+handles2, labels2 = area_axes.get_legend_handles_labels()
+
+ax.legend(handles1 + handles2, labels1 + labels2)
+
+plt.show()
+
+
+
+
+

src/T15_EnergyPlots.py

@@ -0,0 +1,12 @@
+# 1. Energie nach Quartal
+#   alle energieformen in einen plot
+
+# 2. In 2 Plots übereinander
+#   - greenenergies
+#   - nongreenenergies
+
+
+# 3. Alle energien nach type in einem barplot
+#          - pie-plot
+
+# df.plot.pie