Deepak-code-visualization

gec-section-c/deepak.py

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+from pyspark.sql import SparkSession
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Start Spark Session
+spark = SparkSession.builder.appName("Superstore Analysis").getOrCreate()
+
+# Load CSV using Spark
+df_spark = spark.read.csv(r"../dataset/sample-superstore.csv", header=True, inferSchema=True)
+
+# Convert Spark DataFrame to Pandas
+df = df_spark.toPandas()
+
+# Fix datatypes
+df["Order Date"] = pd.to_datetime(df["Order Date"], errors="coerce")
+df["Sales"] = pd.to_numeric(df["Sales"], errors="coerce")
+df["Profit"] = pd.to_numeric(df["Profit"], errors="coerce")
+
+# Create subplots
+fig, axes = plt.subplots(3, 1, figsize=(12, 15))
+
+# 1. Sales by Product Category
+category_sales = df.groupby("Category")["Sales"].sum()
+axes[0].bar(category_sales.index, category_sales.values, color="green", edgecolor="black")
+axes[0].set_title("Total Sales by Product Category")
+axes[0].set_xlabel("Product Category")
+axes[0].set_ylabel("Total Sales")
+axes[0].tick_params(axis='x', rotation=45)
+
+# 2. Profit vs Sales
+axes[1].scatter(df["Sales"], df["Profit"], alpha=0.5, color="purple")
+axes[1].set_title("Profit vs Sales")
+axes[1].set_xlabel("Sales")
+axes[1].set_ylabel("Profit")
+
+# 3. Sales Trend Over Time
+sales_trend = df.groupby("Order Date")["Sales"].sum()
+axes[2].plot(sales_trend.index, sales_trend.values, color="blue")
+axes[2].set_title("Sales Trend Over Time")
+axes[2].set_xlabel("Date")
+axes[2].set_ylabel("Sales")
+
+# Adjust layout
+plt.tight_layout()
+plt.show()