1import pandas as pd
 2
 3# Read Excel file
 4df = pd.read_excel("data.xlsx", sheet_name="Sheet1")
 5
 6# Display first few rows
 7print(df.head())
 8
 9# Basic statistics
10print(df.describe())

1import pandas as pd
2
3# Read all sheets
4excel_file = pd.ExcelFile("workbook.xlsx")
5
6for sheet_name in excel_file.sheet_names:
7    df = pd.read_excel(excel_file, sheet_name=sheet_name)
8    print(f"\n{sheet_name}:")
9    print(df.head())

 1import pandas as pd
 2
 3df = pd.read_excel("sales.xlsx")
 4
 5# Group by and aggregate
 6sales_by_region = df.groupby("region")["sales"].sum()
 7print(sales_by_region)
 8
 9# Filter data
10high_sales = df[df["sales"] > 10000]
11
12# Calculate metrics
13df["profit_margin"] = (df["revenue"] - df["cost"]) / df["revenue"]
14
15# Sort by column
16df_sorted = df.sort_values("sales", ascending=False)

 1import pandas as pd
 2
 3df = pd.DataFrame({
 4    "Product": ["A", "B", "C"],
 5    "Sales": [100, 200, 150],
 6    "Profit": [20, 40, 30]
 7})
 8
 9# Write to Excel
10writer = pd.ExcelWriter("output.xlsx", engine="openpyxl")
11df.to_excel(writer, sheet_name="Sales", index=False)
12
13# Get worksheet for formatting
14worksheet = writer.sheets["Sales"]
15
16# Auto-adjust column widths
17for column in worksheet.columns:
18    max_length = 0
19    column_letter = column[0].column_letter
20    for cell in column:
21        if len(str(cell.value)) > max_length:
22            max_length = len(str(cell.value))
23    worksheet.column_dimensions[column_letter].width = max_length + 2
24
25writer.close()

 1import pandas as pd
 2
 3df = pd.read_excel("sales_data.xlsx")
 4
 5# Create pivot table
 6pivot = pd.pivot_table(
 7    df,
 8    values="sales",
 9    index="region",
10    columns="product",
11    aggfunc="sum",
12    fill_value=0
13)
14
15print(pivot)
16
17# Save pivot table
18pivot.to_excel("pivot_report.xlsx")

 1import pandas as pd
 2import matplotlib.pyplot as plt
 3
 4df = pd.read_excel("data.xlsx")
 5
 6# Create bar chart
 7df.plot(x="category", y="value", kind="bar")
 8plt.title("Sales by Category")
 9plt.xlabel("Category")
10plt.ylabel("Sales")
11plt.tight_layout()
12plt.savefig("chart.png")
13
14# Create pie chart
15df.set_index("category")["value"].plot(kind="pie", autopct="%1.1f%%")
16plt.title("Market Share")
17plt.ylabel("")
18plt.savefig("pie_chart.png")

 1import pandas as pd
 2
 3df = pd.read_excel("messy_data.xlsx")
 4
 5# Remove duplicates
 6df = df.drop_duplicates()
 7
 8# Handle missing values
 9df = df.fillna(0)  # or df.dropna()
10
11# Remove whitespace
12df["name"] = df["name"].str.strip()
13
14# Convert data types
15df["date"] = pd.to_datetime(df["date"])
16df["amount"] = pd.to_numeric(df["amount"], errors="coerce")
17
18# Save cleaned data
19df.to_excel("cleaned_data.xlsx", index=False)

 1import pandas as pd
 2
 3# Read multiple files
 4df1 = pd.read_excel("sales_q1.xlsx")
 5df2 = pd.read_excel("sales_q2.xlsx")
 6
 7# Concatenate vertically
 8combined = pd.concat([df1, df2], ignore_index=True)
 9
10# Merge on common column
11customers = pd.read_excel("customers.xlsx")
12sales = pd.read_excel("sales.xlsx")
13
14merged = pd.merge(sales, customers, on="customer_id", how="left")
15
16merged.to_excel("merged_data.xlsx", index=False)

 1import pandas as pd
 2from openpyxl import load_workbook
 3from openpyxl.styles import PatternFill, Font
 4
 5# Create Excel file
 6df = pd.DataFrame({
 7    "Product": ["A", "B", "C"],
 8    "Sales": [100, 200, 150]
 9})
10
11df.to_excel("formatted.xlsx", index=False)
12
13# Load workbook for formatting
14wb = load_workbook("formatted.xlsx")
15ws = wb.active
16
17# Apply conditional formatting
18red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid")
19green_fill = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid")
20
21for row in range(2, len(df) + 2):
22    cell = ws[f"B{row}"]
23    if cell.value < 150:
24        cell.fill = red_fill
25    else:
26        cell.fill = green_fill
27
28# Bold headers
29for cell in ws[1]:
30    cell.font = Font(bold=True)
31
32wb.save("formatted.xlsx")