Excel Analysis

Quick start

Read Excel files with pandas:

import pandas as pd

Read Excel file

df = pd.read_excel("data.xlsx", sheet_name="Sheet1")

Display first few rows

print(df.head())

Basic statistics

print(df.describe())

Reading multiple sheets

Process all sheets in a workbook:

import pandas as pd

Read all sheets

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"\n{sheet_name}:") print(df.head())

Data analysis

Perform common analysis tasks:

import pandas as pd

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

Group by and aggregate

sales_by_region = df.groupby("region")["sales"].sum() print(sales_by_region)

Filter data

high_sales = df[df["sales"] > 10000]

Calculate metrics

df["profit_margin"] = (df["revenue"] - df["cost"]) / df["revenue"]

Sort by column

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

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