Added example 11 Power Plant Monitoring

11_PowerPlantRemoteMonitoring/example11.py

+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+
+# read the downloaded csv file
+df = pd.read_csv("opendata-kfue-odl_7t.csv", sep=';', decimal=',', on_bad_lines='warn', encoding="ISO-8859-1")
+
+# Get the rows for the abbreviation to name matching
+df_colnames = df.take(range(171,255))
+# drop all columns except the first
+df_colnames.drop(columns=df.columns[1:], inplace=True)
+
+# Create a dictionary to translate from abbreviation to the actual name
+col_dict = {}
+for row in df_colnames.iloc[:,0]:
+    place_abbreviation = row[:12]
+    # We are not interested in Weather data so we dont need them in the dictionary
+    if "MET" not in place_abbreviation and "TIS" not in place_abbreviation:
+        place = str(row[15:65]).strip().removeprefix("KF"+ chr(ord("Ü"))+"-Sonde")
+        # print(place_abbreviation, " = ", place)
+        col_dict[place_abbreviation] = place
+
+# Extract the data rows and drop the last column because it is an empty column
+df_data = df.take(range(1, 169))
+df_data.drop(columns=df.columns[-1], inplace=True)
+
+# Remove the unused columns (everything except Radiation)
+for col in df_data.columns:
+    if "MET" in col or "TIS" in col:
+        df_data.drop(columns=col, inplace=True)
+    elif col != "Datum/Uhrzeit":
+        # The values are in scientific notation (7,095e-02) and need to be converted
+        # Convert all values to strings where the ',' is replaced by the decimal indicator '.'
+        # Further use pandas library to convert them to numeric values
+        # the method apply uses a given function on every element of the specified column of the dataframe
+        df_data[col]= df_data[col].apply(lambda x: str(x).replace(",", ".")).apply(pd.to_numeric, errors='coerce')
+
+# Drop all columns where all values consist of NaN
+df_data.dropna(axis=1, how='all', inplace=True)
+
+# Create a Boxplot
+df_data.drop(columns=df.columns[0], inplace=True)
+plt.figure(figsize=(18,18))
+plt.title("Power plant monitoring")
+plt.boxplot(df_data.iloc[:,1:], tick_labels=[col_dict[x] for x in df_data.columns[1:]])
+plt.xticks(rotation=90)
+# \u00B5 is equal to the micro character
+plt.ylabel("\u00B5S/h")
+plt.grid()
+plt.savefig("boxplot.png")

README.md

@@ -10,4 +10,5 @@ Example # | Name | Description | Input format | Output | Source
 05 | Park bench map | Show a map of all park benches in the city of Norderstedt | SHP (shape file) | Table, Open Street Map overlay | https://opendata.schleswig-holstein.de/dataset/parkbanke
 06 | Tax office map | Create a formatted HTML and map containing all tax offices in Schleswig-Holstein | RDF | HTML table, Open Street Map overlay | https://opendata.schleswig-holstein.de/dataset/finanzamter-2024-01-28
 08 | Water height diagram | Create a line diagram of the water height of the Stoer near Willenscharen for 2023 | CSV | Diagram (PNG) | https://opendata.schleswig-holstein.de/dataset/wasserstand-pegel-willenscharen-stor1
-10 | Redispatch SH-Netz AG | Show a scatter plot of the number of EEG plants of a commune and the amount of time the electricity supply needed to be reduced  | CSV | Scatterplot (PNG) | https://opendata.schleswig-holstein.de/dataset/redispatch-2022-08
+10 | Redispatch SH-Netz AG | Show a scatter plot of the number of EEG plants of a commune and the amount of time the electricity supply needed to be reduced  | CSV | Scatterplot (PNG) | https://opendata.schleswig-holstein.de/dataset/redispatch-2022-08
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+11 | Power plant monitoring | Show a box plot (mean, variance, outliers) of the measures of genitron stations at different locations over a one week time window | CSV | Boxplot (PNG) | https://opendata.schleswig-holstein.de/dataset/kfu-messwerte-2024-07-29
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