import pandas as pd

# Read Excel
df = pd.read_excel('file.xlsx')  # Default: first sheet
all_sheets = pd.read_excel('file.xlsx', sheet_name=None)  # All sheets as dict

# Analyze
df.head()      # Preview data
df.info()      # Column info
df.describe()  # Statistics

# Write Excel
df.to_excel('output.xlsx', index=False)

# Bad: Calculating in Python and hardcoding result
total = df['Sales'].sum()
sheet['B10'] = total  # Hardcodes 5000

# Bad: Computing growth rate in Python
growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0]['Revenue']
sheet['C5'] = growth  # Hardcodes 0.15

# Bad: Python calculation for average
avg = sum(values) / len(values)
sheet['D20'] = avg  # Hardcodes 42.5

# Good: Let Excel calculate the sum
sheet['B10'] = '=SUM(B2:B9)'

# Good: Growth rate as Excel formula
sheet['C5'] = '=(C4-C2)/C2'

# Good: Average using Excel function
sheet['D20'] = '=AVERAGE(D2:D19)'

# Using openpyxl for formulas and formatting
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment

wb = Workbook()
sheet = wb.active

# Add data
sheet['A1'] = 'Hello'
sheet['B1'] = 'World'
sheet.append(['Row', 'of', 'data'])

# Add formula
sheet['B2'] = '=SUM(A1:A10)'

# Formatting
sheet['A1'].font = Font(bold=True, color='FF0000')
sheet['A1'].fill = PatternFill('solid', start_color='FFFF00')
sheet['A1'].alignment = Alignment(horizontal='center')

# Column width
sheet.column_dimensions['A'].width = 20

wb.save('output.xlsx')

# Using openpyxl to preserve formulas and formatting
from openpyxl import load_workbook

# Load existing file
wb = load_workbook('existing.xlsx')
sheet = wb.active  # or wb['SheetName'] for specific sheet

# Working with multiple sheets
for sheet_name in wb.sheetnames:
    sheet = wb[sheet_name]
    print(f"Sheet: {sheet_name}")

# Modify cells
sheet['A1'] = 'New Value'
sheet.insert_rows(2)  # Insert row at position 2
sheet.delete_cols(3)  # Delete column 3

# Add new sheet
new_sheet = wb.create_sheet('NewSheet')
new_sheet['A1'] = 'Data'

wb.save('modified.xlsx')

python recalc.py <excel_file> [timeout_seconds]

python recalc.py output.xlsx 30

{
  "status": "success",           // or "errors_found"
  "total_errors": 0,              // Total error count
  "total_formulas": 42,           // Number of formulas in file
  "error_summary": {              // Only present if errors found
    "#REF!": {
      "count": 2,
      "locations": ["Sheet1!B5", "Sheet1!C10"]
    }
  }
}

import pandas as pd

excel_file = pd.ExcelFile("workbook.xlsx")

for sheet_name in excel_file.sheet_names:
    df = pd.read_excel(excel_file, sheet_name=sheet_name)
    print(f"{sheet_name}: {len(df)} rows")

import pandas as pd

df = pd.read_excel("sales_data.xlsx")

pivot = pd.pivot_table(
    df,
    values="sales",
    index="region",
    columns="product",
    aggfunc="sum",
    fill_value=0
)

pivot.to_excel("pivot_report.xlsx")

df = pd.read_excel("sales.xlsx")

# Group and sum
sales_by_region = df.groupby("region")["sales"].sum()

# Multiple aggregations
summary = df.groupby("region").agg({
    "sales": "sum",
    "quantity": "mean",
    "profit": ["min", "max"]
})

# Simple filter
high_sales = df[df["sales"] > 10000]

# Multiple conditions
filtered = df[(df["region"] == "West") & (df["sales"] > 5000)]

# Calculate new columns
df["profit_margin"] = (df["revenue"] - df["cost"]) / df["revenue"]

# Sort
df_sorted = df.sort_values("sales", ascending=False)

import pandas as pd

df = pd.read_excel("messy_data.xlsx")

# Remove duplicates
df = df.drop_duplicates()

# Handle missing values
df = df.fillna(0)           # Fill with value
df = df.dropna()            # Drop rows with missing values
df = df.dropna(subset=["important_col"])  # Drop only if specific column is null

# Remove whitespace from strings
df["name"] = df["name"].str.strip()

# Convert data types
df["date"] = pd.to_datetime(df["date"])
df["amount"] = pd.to_numeric(df["amount"], errors="coerce")

# Save cleaned data
df.to_excel("cleaned_data.xlsx", index=False)

import pandas as pd

# Concatenate files vertically (stack rows)
df1 = pd.read_excel("sales_q1.xlsx")
df2 = pd.read_excel("sales_q2.xlsx")
combined = pd.concat([df1, df2], ignore_index=True)

# Merge on common column (like SQL JOIN)
customers = pd.read_excel("customers.xlsx")
sales = pd.read_excel("sales.xlsx")

merged = pd.merge(sales, customers, on="customer_id", how="left")

merged.to_excel("merged_data.xlsx", index=False)

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_excel("data.xlsx")

# Bar chart
df.plot(x="category", y="value", kind="bar")
plt.title("Sales by Category")
plt.xlabel("Category")
plt.ylabel("Sales")
plt.tight_layout()
plt.savefig("bar_chart.png")
plt.close()

# Pie chart
df.set_index("category")["value"].plot(kind="pie", autopct="%1.1f%%")
plt.title("Market Share")
plt.ylabel("")
plt.savefig("pie_chart.png")
plt.close()

# Line chart
df.plot(x="date", y="revenue", kind="line")
plt.savefig("trend.png")
plt.close()

import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import PatternFill, Font

df = pd.DataFrame({
    "Product": ["A", "B", "C"],
    "Sales": [100, 200, 150]
})

df.to_excel("formatted.xlsx", index=False)

wb = load_workbook("formatted.xlsx")
ws = wb.active

# Define fills
red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid")
green_fill = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid")

# Apply conditional formatting
for row in range(2, len(df) + 2):
    cell = ws[f"B{row}"]
    if cell.value < 150:
        cell.fill = red_fill
    else:
        cell.fill = green_fill

# Bold headers
for cell in ws[1]:
    cell.font = Font(bold=True)

wb.save("formatted.xlsx")

import pandas as pd

# Read only specific columns
df = pd.read_excel("large.xlsx", usecols=["A", "C", "E"])

# Read in chunks for very large files
for chunk in pd.read_excel("huge.xlsx", chunksize=10000):
    # Process each chunk
    process(chunk)

# Specify dtypes to avoid inference overhead
df = pd.read_excel("data.xlsx", dtype={"id": str, "amount": float})

# For openpyxl with large files
from openpyxl import load_workbook
wb = load_workbook("large.xlsx", read_only=True)  # Read-only mode

import pandas as pd

df = pd.DataFrame({"Product": ["Widget A", "Widget B"], "Sales": [100, 200]})

writer = pd.ExcelWriter("output.xlsx", engine="openpyxl")
df.to_excel(writer, sheet_name="Sales", index=False)

worksheet = writer.sheets["Sales"]

for column in worksheet.columns:
    max_length = 0
    column_letter = column[0].column_letter
    for cell in column:
        try:
            if len(str(cell.value)) > max_length:
                max_length = len(str(cell.value))
        except:
            pass
    worksheet.column_dimensions[column_letter].width = max_length + 2

writer.close()