Merge branch 'master' into onprem
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朱潮
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---
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name: excel-analysis
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description: Analyze Excel spreadsheets, create pivot tables, generate charts, and perform data analysis. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.
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---
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# Excel Analysis
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## Quick start
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Read Excel files with pandas:
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```python
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import pandas as pd
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# Read Excel file
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df = pd.read_excel("data.xlsx", sheet_name="Sheet1")
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# Display first few rows
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print(df.head())
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# Basic statistics
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print(df.describe())
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```
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## Reading multiple sheets
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Process all sheets in a workbook:
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```python
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import pandas as pd
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# Read all sheets
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excel_file = pd.ExcelFile("workbook.xlsx")
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for sheet_name in excel_file.sheet_names:
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df = pd.read_excel(excel_file, sheet_name=sheet_name)
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print(f"\n{sheet_name}:")
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print(df.head())
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```
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## Data analysis
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Perform common analysis tasks:
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```python
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import pandas as pd
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df = pd.read_excel("sales.xlsx")
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# Group by and aggregate
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sales_by_region = df.groupby("region")["sales"].sum()
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print(sales_by_region)
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# Filter data
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high_sales = df[df["sales"] > 10000]
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# Calculate metrics
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df["profit_margin"] = (df["revenue"] - df["cost"]) / df["revenue"]
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# Sort by column
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df_sorted = df.sort_values("sales", ascending=False)
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```
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## Creating Excel files
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Write data to Excel with formatting:
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```python
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import pandas as pd
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df = pd.DataFrame({
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"Product": ["A", "B", "C"],
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"Sales": [100, 200, 150],
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"Profit": [20, 40, 30]
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})
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# Write to Excel
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writer = pd.ExcelWriter("output.xlsx", engine="openpyxl")
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df.to_excel(writer, sheet_name="Sales", index=False)
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# Get worksheet for formatting
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worksheet = writer.sheets["Sales"]
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# Auto-adjust column widths
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for column in worksheet.columns:
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max_length = 0
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column_letter = column[0].column_letter
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for cell in column:
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if len(str(cell.value)) > max_length:
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max_length = len(str(cell.value))
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worksheet.column_dimensions[column_letter].width = max_length + 2
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writer.close()
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```
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## Pivot tables
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Create pivot tables programmatically:
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```python
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import pandas as pd
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df = pd.read_excel("sales_data.xlsx")
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# Create pivot table
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pivot = pd.pivot_table(
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df,
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values="sales",
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index="region",
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columns="product",
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aggfunc="sum",
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fill_value=0
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)
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print(pivot)
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# Save pivot table
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pivot.to_excel("pivot_report.xlsx")
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```
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## Charts and visualization
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Generate charts from Excel data:
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```python
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import pandas as pd
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import matplotlib.pyplot as plt
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df = pd.read_excel("data.xlsx")
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# Create bar chart
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df.plot(x="category", y="value", kind="bar")
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plt.title("Sales by Category")
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plt.xlabel("Category")
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plt.ylabel("Sales")
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plt.tight_layout()
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plt.savefig("chart.png")
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# Create pie chart
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df.set_index("category")["value"].plot(kind="pie", autopct="%1.1f%%")
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plt.title("Market Share")
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plt.ylabel("")
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plt.savefig("pie_chart.png")
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```
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## Data cleaning
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Clean and prepare Excel data:
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```python
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import pandas as pd
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df = pd.read_excel("messy_data.xlsx")
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# Remove duplicates
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df = df.drop_duplicates()
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# Handle missing values
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df = df.fillna(0) # or df.dropna()
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# Remove whitespace
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df["name"] = df["name"].str.strip()
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# Convert data types
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df["date"] = pd.to_datetime(df["date"])
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df["amount"] = pd.to_numeric(df["amount"], errors="coerce")
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# Save cleaned data
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df.to_excel("cleaned_data.xlsx", index=False)
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```
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## Merging and joining
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Combine multiple Excel files:
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```python
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import pandas as pd
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# Read multiple files
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df1 = pd.read_excel("sales_q1.xlsx")
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df2 = pd.read_excel("sales_q2.xlsx")
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# Concatenate vertically
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combined = pd.concat([df1, df2], ignore_index=True)
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# Merge on common column
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customers = pd.read_excel("customers.xlsx")
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sales = pd.read_excel("sales.xlsx")
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merged = pd.merge(sales, customers, on="customer_id", how="left")
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merged.to_excel("merged_data.xlsx", index=False)
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```
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## Advanced formatting
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Apply conditional formatting and styles:
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```python
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import pandas as pd
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from openpyxl import load_workbook
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from openpyxl.styles import PatternFill, Font
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# Create Excel file
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df = pd.DataFrame({
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"Product": ["A", "B", "C"],
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"Sales": [100, 200, 150]
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})
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df.to_excel("formatted.xlsx", index=False)
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# Load workbook for formatting
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wb = load_workbook("formatted.xlsx")
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ws = wb.active
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# Apply conditional formatting
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red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid")
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green_fill = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid")
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for row in range(2, len(df) + 2):
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cell = ws[f"B{row}"]
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if cell.value < 150:
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cell.fill = red_fill
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else:
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cell.fill = green_fill
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# Bold headers
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for cell in ws[1]:
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cell.font = Font(bold=True)
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wb.save("formatted.xlsx")
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```
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## Performance tips
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- Use `read_excel` with `usecols` to read specific columns only
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- Use `chunksize` for very large files
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- Consider using `engine='openpyxl'` or `engine='xlrd'` based on file type
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- Use `dtype` parameter to specify column types for faster reading
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## Available packages
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- **pandas** - Data analysis and manipulation (primary)
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- **openpyxl** - Excel file creation and formatting
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- **xlrd** - Reading older .xls files
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- **xlsxwriter** - Advanced Excel writing capabilities
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- **matplotlib** - Chart generation
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@ -1,6 +1,6 @@
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---
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name: r2-upload
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description: Uploads files to Cloudflare R2, AWS S3, or any S3-compatible storage and returns a public or temporary URL. Use when you need to publish assets, share files, or provide upload helpers to other skills.
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description: Upload files and return a public or temporary URL. Use when you need to publish assets, share files, or provide upload helpers to other skills.
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compatibility: Requires Python 3.8+ and PyYAML. Uses AWS SigV4 signing (no external SDK required).
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metadata:
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author: foundra
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