Excel Data Analysis Skill

Quick Start — Reading Excel Files

import openpyxl

wb = openpyxl.load_workbook('file.xlsx', data_only=True)
for sheet_name in wb.sheetnames:
    sheet = wb[sheet_name]
    print(f"Sheet: {sheet_name} ({sheet.max_row} rows x {sheet.max_column} cols)")
    for row in sheet.iter_rows(min_row=1, max_row=min(5, sheet.max_row), values_only=True):
        print(row)

Data Analysis with pandas

import pandas as pd

# Read all sheets
all_sheets = pd.read_excel('file.xlsx', sheet_name=None)
for name, df in all_sheets.items():
    print(f"\n=== {name} ===")
    print(f"Shape: {df.shape}")
    print(df.describe())
    print(df.head())

Extracting Statistics

import pandas as pd

df = pd.read_excel('file.xlsx')

# Summary statistics for numeric columns
for col in df.select_dtypes(include='number').columns:
    print(f"{col}: min={df[col].min()}, max={df[col].max()}, "
          f"mean={df[col].mean():.2f}, median={df[col].median():.2f}")

# Value counts for categorical columns
for col in df.select_dtypes(include='object').columns:
    print(f"\n{col} distribution:")
    print(df[col].value_counts().head(10))

Detecting Formulas

import openpyxl

wb = openpyxl.load_workbook('file.xlsx')  # data_only=False to see formulas
sheet = wb.active
for row in sheet.iter_rows():
    for cell in row:
        if isinstance(cell.value, str) and cell.value.startswith('='):
            print(f"{cell.coordinate}: {cell.value}")

Analysis Guidelines

Data Quality Assessment

• •Check columns with >20% empty cells as potentially unreliable
• •Flag mixed-type columns (numbers mixed with text)
• •Identify outliers using standard deviation from mean
• •Report data completeness percentage

Cross-Sheet Analysis

• •Look for relationships between sheets (shared column names, ID references)
• •Compare metrics across sheets (different time periods, regions)
• •Synthesize findings across sheets

Numerical Presentation

• •Always include specific numbers ("increased 23%", not just "increased")
• •Use comparisons: ratios, percentages, year-over-year changes
• •Round appropriately: financial to 2 decimals, percentages to 1 decimal
• •Present ranges when uncertain: "between X and Y"

Report Integration

• •Lead with the most impactful numerical findings
• •Create data narratives: "The data shows X, which suggests Y"
• •Cite which sheet and approximate row range data comes from
• •Recommend visualizations: "This data would benefit from a bar chart"

Presentation Slide Guidance

• •Use stat_callout slides for key metrics (KPIs, totals, percentages)
• •Use comparison slides for before/after or category comparisons
• •Use chart slides with actual data_labels and data_values from the spreadsheet
• •Limit tables on slides to 5 rows × 4 columns maximum