Add code sample to show how Hazelcast works with Pandas DataFrames. (#…

…604)

Added code sample to show how Hazelcast works with Pandas DataFrames.

examples/pandas/pandas_example.py

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+"""
+You can store Pandas DataFrame objects in Hazelcast cluster and retrieve with low-latency.
+There are multiple serialization methods for DataFrame objects.
+Hazelcast uses pickle serialization as default for various Python objects including DataFrame.
+So, you can simply put your data directly using default pickle serialization without any conversions.
+
+patients.put(1, df)
+
+Alternatively, you can convert your DataFrame to JSON, CSV or a dict using to_json(), to_csv() and to_dict() methods.
+Note that JSON or CSV serializations returns string representation of DataFrame in requested format.
+
+Convert to CSV: df = df.to_csv()
+Convert to JSON: df = df.to_json()
+Convert to dict: df = df.to_dict()
+
+If you prefer to use these converted representations, you need to re-create DataFrame object since they are not stored
+as DataFrame object in Hazelcast cluster. For CSV, JSON and dict conversions, use following methods while retrieving:
+
+Create from CSV: df = pd.from_csv(StringIO(patients.get(1)))
+Create from JSON: df = pd.read_json(patients.get(1))
+Create from dict object: df = pd.DataFrame(patients.get(1))
+
+In addition to methods above, you can write your own custom serializer for DatFrame objects. For more information about
+Pyton client serialization methods, see https://hazelcast.readthedocs.io/en/stable/serialization.html#
+"""
+
+import hazelcast
+from hazelcast.core import HazelcastJsonValue
+from matplotlib import pyplot as plt
+import numpy as np
+import pandas as pd
+
+# Create Hazelcast client
+client = hazelcast.HazelcastClient()
+
+# Get an IMap for storing patient's DataFrame objects
+patients = client.get_map("patients").blocking()
+
+# Store the blood pressure and heart rate data of fifty patients in a DataFrame and load it to Hazelcast cluster as JSON
+for pid in range(0, 50):
+    # Create DataFrame with random values
+    df = pd.DataFrame(
+        data={
+            "blood_pressure": np.random.randint(80, 120, size=(75,)),
+            "heart_rate": np.random.randint(60, 100, size=(75,)),
+        },
+        index=pd.date_range("2023-01-15", periods=75, freq="H"),
+    )
+    # Load DataFrame to Hazelcast cluster as HazelcastJsonValue
+    patients.put(pid, HazelcastJsonValue(df.to_json()))
+
+pid = np.random.randint(0, 50)
+# Retrieve the data of a random patient
+df = pd.read_json(patients.get(pid).to_string())
+
+# Plot the data
+df.plot(use_index=True, y=["blood_pressure", "heart_rate"], figsize=(15, 5), kind="line")
+plt.title(f"Blood Pressure and Heart Rate Plot of Patient-{pid}")
+plt.show()