Excel Analysis

Creating Excel files

Write data to Excel with formatting:

import pandas as pd

df = pd.DataFrame({
    "Product": ["A", "B", "C"],
    "Sales": [100, 200, 150],
    "Profit": [20, 40, 30]
})

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

# Get worksheet for formatting
worksheet = writer.sheets["Sales"]

# Auto-adjust column widths
for column in worksheet.columns:
    max_length = 0
    column_letter = column[0].column_letter
    for cell in column:
        if len(str(cell.value)) > max_length:
            max_length = len(str(cell.value))
    worksheet.column_dimensions[column_letter].width = max_length + 2

writer.close()

Pivot tables

Create pivot tables programmatically:

import pandas as pd

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

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

print(pivot)

# Save pivot table
pivot.to_excel("pivot_report.xlsx")

Charts and visualization

Generate charts from Excel data:

import pandas as pd
import matplotlib.pyplot as plt

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

# Create 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("chart.png")

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

Data cleaning

Clean and prepare Excel data:

import pandas as pd

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

# Remove duplicates
df = df.drop_duplicates()

# Handle missing values
df = df.fillna(0)  # or df.dropna()

# Remove whitespace
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)

Merging and joining

Combine multiple Excel files:

import pandas as pd

# Read multiple files
df1 = pd.read_excel("sales_q1.xlsx")
df2 = pd.read_excel("sales_q2.xlsx")

# Concatenate vertically
combined = pd.concat([df1, df2], ignore_index=True)

# Merge on common column
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)

Advanced formatting

Apply conditional formatting and styles:

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

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

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

# Load workbook for formatting
wb = load_workbook("formatted.xlsx")
ws = wb.active

# Apply conditional formatting
red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid")
green_fill = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid")

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")

Performance tips

• Use read_excel with usecols to read specific columns only
• Use chunksize for very large files
• Consider using engine='openpyxl' or engine='xlrd' based on file type
• Use dtype parameter to specify column types for faster reading

Available packages

• pandas - Data analysis and manipulation (primary)
• openpyxl - Excel file creation and formatting
• xlrd - Reading older .xls files
• xlsxwriter - Advanced Excel writing capabilities
• matplotlib - Chart generation