# Let us start by turning off extra warning messages for clarity.
import warnings
warnings.filterwarnings("ignore")

# To work with data files, let us import the pandas library.
import pandas as pd

# Data setup
# Let us download a simple retail sales CSV from the web.
csv_url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/shampoo.csv"
df = pd.read_csv(csv_url)

# Show the first few rows to preview our data
print(df.head())
# Show the shape of the table
print("Shape:", df.shape)

# Let us plot the sales data to spot trends over time.
import matplotlib.pyplot as plt
df["Sales"].plot(title="Shampoo Sales Over Time")
plt.xlabel("Month")
plt.ylabel("Sales")
plt.show()

# Read a local CSV file (make sure your file path is correct).
local_path = "mydata.csv"
try:
    df_local = pd.read_csv(local_path)
    print(df_local.head())
except FileNotFoundError:
    print("File not found. Please check the file path!")

    

# Writing DataFrame back to a new CSV file
df.to_csv("shampoo_sales_copy.csv", index=False)
print("Data saved to shampoo_sales_copy.csv")

# Reading and writing Excel files
excel_path = "shampoo_sales_example.xlsx"
df.to_excel(excel_path, index=False)
print(f"Saved as {excel_path}")

df_from_excel = pd.read_excel(excel_path)
print(df_from_excel.head())

# Writing to JSON
df.to_json("shampoo_sales.json", orient="records", lines=True)
print("Saved to shampoo_sales.json")

# Reading JSON back in
df_json = pd.read_json("shampoo_sales.json", orient="records", lines=True)
print(df_json.head())

# Handling CSV files with missing or extra columns
# To demonstrate handling, we create a new sample CSV directly.
import io
csv_sample = "Month,Sales\n1-01,266.0\n2-01,145.9"
# This CSV has only 2 columns, but we request 3 column names.
try:
    df_missing = pd.read_csv(io.StringIO(csv_sample), names=["Month", "Sales", "Store"], header=0)
    print(df_missing.head())
except pd.errors.ParserError as e:
    print("Error reading CSV:", e)

    

# Try replacing missing values if any
df_filled = df.fillna(0)
print(df_filled.head())

# Filtering rows that meet a condition
over_350 = df[df["Sales"] > 350]
print(over_350)

# Select only a few columns for a new table
small_table = df[["Month", "Sales"]].copy()
print(small_table.head())

# Exporting part of the data to Excel
small_table.to_excel("mini_sales.xlsx", index=False)
print("Exported smaller table to mini_sales.xlsx")

# Merging two DataFrames
extra_df = pd.DataFrame({
    "Month": ["3-01", "7-01"],
    "Return Rate": [2.1, 1.7]
})
merged = pd.merge(df, extra_df, on="Month", how="left")
print(merged.head(8))

# Challenge: Let us ask the user to read their own CSV file
filename = input("Type the CSV file name to read: ")
try:
    user_df = pd.read_csv(filename)
    print("Here is your data:")
    print(user_df.head())
except FileNotFoundError:
    print("File not found. Please check the file name and try again!")

    

# Mini-Project Part 1: Monthly Averages
df["Sales"] = pd.to_numeric(df["Sales"], errors="coerce")
average = df['Sales'].mean()
print(f"Average shampoo sales: {average:.2f}")

# Mini-Project Part 2: Export the result to JSON
import json
output = {'average_sales': average}
with open("average_sales.json", "w") as f:
    json.dump(output, f)
print("Saved the average sales to average_sales.json")

# Troubleshooting tip: What if your file is not found?
try:
    df_try = pd.read_csv("wrongname.csv")
except FileNotFoundError:
    print("Oops. The file name is wrong or not in this folder.")

    

# Useful pandas options for exporting to Excel or CSV
df.to_csv("shampoo_sales_utf8.csv", encoding="utf-8", index=False)
df.to_excel("shampoo_sales_no_index.xlsx", index=False, sheet_name="SalesData")
print("Saved with special options.")