Excel Data Analysis Workflow

End-to-end workflow for structured Excel analysis. Each step maps to a capability sub-skill that can be loaded for detailed patterns.

Workflow

Step 1 — Count rows across all sheets (lightweight, no full load)

Count rows per sheet without loading data into memory. Use openpyxl read_only mode — this works for any file size.

import openpyxl, gc

wb = openpyxl.load_workbook(file_path, read_only=True, data_only=True)
total_rows = 0
sheet_info = {}
for name in wb.sheetnames:
    ws = wb[name]
    row_count = sum(1 for _ in ws.iter_rows(min_row=2, values_only=True))
    total_rows += row_count
    sheet_info[name] = row_count
    print(f"Sheet '{name}': {row_count} rows")
wb.close()
print(f"总行数={total_rows}")

⚠️ Do NOT use pd.read_excel() to count rows — it loads all data into memory, which will OOM on large files.

→ capability: excel-reading/multi-sheet-reading

Step 2 — Large file gate (CRITICAL — choose strategy by row count)

For >= 100k rows:

read_file(path="<skills_base>/sn-da-large-file-analysis/SKILL.md")

Then use stream_excel_to_parquet() from that skill — it reads via openpyxl iter_rows in 50k-row chunks with constant memory.

For 10k – 100k rows (only):

import pandas as pd
parquet_path = "/tmp/_auto_parquet.parquet"
df = pd.read_excel(file_path, sheet_name=target_sheet)
df.to_parquet(parquet_path, engine="pyarrow")
del df; gc.collect()
df = pd.read_parquet(parquet_path)

→ capability: excel-reading/large-excel-reading

Step 3 — Inspect schema & data types

Preview target sheet structure. For large files (>= 10k rows), only read a small sample — never full load just to inspect.

# For any file size — read only first N rows for inspection
df_head = pd.read_excel(file_path, sheet_name=target_sheet, nrows=20)
print(f"Columns: {df_head.columns.tolist()}")
print(f"Dtypes:\n{df_head.dtypes}")
print(df_head.head(10))

→ capability: excel-reading/range-reading

Step 4 — Data cleaning

Handle missing values, normalize text, clean invalid characters.

# Missing values
null_count = df[col].isna().sum()

# Text cleaning: keep only Chinese characters
import re
def clean_text(val):
    if pd.isna(val): return val
    return "".join(re.findall(r"[\u4e00-\u9fff]", str(val))) or ""

df[col] = df[col].apply(clean_text)

⚠️ Large file rule: When total_rows >= 100k, do NOT use df.apply(lambda...). Use vectorized operations or np.where() instead. See sn-da-large-file-analysis skill for the vectorized cheat sheet.

→ capabilities:

• excel-data-cleaning/missing-value-handling
• excel-data-cleaning/invalid-data-cleaning
• excel-data-cleaning/text-normalization

Step 5 — Filter & extract

Apply condition or category filters, aggregate results.

# Condition filter
mask = df[col].astype(str).str.strip() == target_value
filtered = df[mask]

# Category extraction (for headerless layouts)
df_raw = pd.read_excel(file_path, sheet_name=sheet, header=None)
# Walk rows to find category markers, collect items until next marker

→ capabilities:

• excel-data-filtering/condition-filtering
• excel-data-filtering/category-filtering
• excel-data-filtering/threshold-filtering

Step 6 — Export results

Save filtered/cleaned data as Excel or CSV. Provide download link.

output_path = "/mnt/data/result.xlsx"
result_df.to_excel(output_path, index=False)
print(f"[Download](sandbox:{output_path})")

→ capabilities:

• excel-result-export/single-sheet-export
• excel-result-export/formatted-export

Key rules

• Always count rows first — gate large-file logic on the 10k threshold.
• >= 100k rows → MUST load sn-da-large-file-analysis skill — do not attempt to handle with pd.read_excel().
• Column names may contain spaces (e.g. '是否通 过') — use exact string indexing.
• Headerless sheets — use header=None and positional indexing.
• Prohibited on large files (>= 100k rows):
  • pd.read_excel() for full load (use streaming read → Parquet)
  • df.apply(lambda...) or df.iterrows() (use vectorized ops or itertuples())
  • fc-list, find ... fonts, subprocess to search fonts, or pip install (use fixed font paths below)
  • Printing all unique values or full DataFrames (use .head(), .value_counts().head())

CJK Font Setup (mandatory for charts)

When generating charts with matplotlib, copy this block as-is. Do NOT search for fonts.

import os
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm

_FONT_PATHS = [
    '/mnt/afs_agents/SimHei.ttf',
    '/mnt/afs_agents/mnt/data/SimHei.ttf',
    os.path.expanduser('~/.fonts/SimHei.ttf'),
    '/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc',
    '/usr/share/fonts/SimHei.ttf',
]
for _p in _FONT_PATHS:
    if os.path.exists(_p):
        fm.fontManager.addfont(_p)
        matplotlib.rcParams['font.family'] = fm.FontProperties(fname=_p).get_name()
        break
matplotlib.rcParams['axes.unicode_minus'] = False

How to load sub-skills

Each workflow step references one or more capability sub-skills. When you need the detailed code pattern for a step, load the sub-skill on demand:

read_file(path="<base_path>/<category>/<sub-skill-name>/SKILL.md")

Rules:

• Only load the sub-skill(s) needed for your current step.
• Do NOT load all sub-skills at once — it wastes context.
• The top-level workflow (this file) is your guide; sub-skills provide detailed implementation patterns.

Available capability sub-skills

Base path: <skills_root>/sn-da-excel-workflow/capability/{category}/{sub-skill}/SKILL.md

excel-reading — 读取与解析

excel-data-cleaning — 数据清洗

excel-data-filtering — 数据筛选

excel-data-analysis — 数据分析

excel-data-statistics — 统计计算

excel-data-visualization — 数据可视化

excel-cell-coloring — 单元格着色

excel-conditional-formatting — 条件格式

excel-result-export — 结果导出

excel-table-styling — 表格样式