Add source example files

source/pandas/01-pandas-files/01-json-demo-communes.py

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+import pandas as pd
+from matplotlib import pyplot as plt
+import matplotlib
+
+if __name__ == '__main__':
+    # Configure Pandas to only display floats not in scientific notation
+    pd.options.display.float_format = "{:.2f}".format
+    # Read a list of FR lowest administrative divisions
+    # See france-communes-readme.md for field informations
+    df = pd.read_json("../data/json/france-communes.json")
+    # Get the mean of columns as a new row, and only keep the population column
+    df1 = df.copy()
+    df1.loc["mean"] = df1.mean(skipna=True, numeric_only=True)[["population"]]
+    # Show dataframe
+    print(df1.to_string(max_cols=9))
+
+    # Get only cities in the dept 94
+    df2: pd.DataFrame = df.copy()
+    # Filter rows by using criteria
+    df2 = df2.loc[(df2["departement"] == "94") & (df2["population"] > 0) & (df2["nom"].str.contains(""))]
+    # Sort data by columns
+    df2.sort_values("nom", inplace=True)
+    # Remove columns from the dataframe
+    df2.drop(columns=["chefLieu", "commune"], inplace=True)
+    print(df2.to_string(max_cols=10))
+
+    # Show lines
+    matplotlib.use("QtCairo")
+    plt.rcParams["figure.dpi"] = 96
+    gp = df2.plot.line(x="nom", y="population")
+    plt.show()
+
+    # Get postal codes from line 37237
+    row = df2.loc[37237]
+    print(row["codesPostaux"], type(row["codesPostaux"]))
+