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# Introduction
As a data analyst, your job is to go from raw numbers to conclusions that guide business decisions. But let’s be honest: How much of your day is spent formatting reports the third time, cross-referencing data from different departments, or preparing updates to the same dashboard? If you’re like most analysts, that’s probably too much.
The reality is that data analysts spend about 50% of their time on repetitive formatting, report preparation, and data reconciliation tasks—time that is actually taken away from analytical work.
This article covers five Python scripts designed for data analysts’ biggest pain points. Let’s begin!
# 1. Automatic report formatter
A word of pain: Your stakeholders want reports that look professional, not raw data dumps. You can adjust column widths, add conditional formatting, create summary rows, and make sure everything aligns perfectly. A new data point means re-optimizing everything.
What does the script do?: takes your analyzed data and turns it into polished, boardroom-ready Excel reports with conditional formatting, summary statistics, formatted headers, and auto-adjust columns. Consistent styling is applied to all your reports so you never have to manually format them.
How it works: Uses a script OpenPix L Applying Professional Style Rules to Excel Files It automatically calculates summary rows, applies color scales to highlight important values, highlights numbers as percentages based on currency or column names, and adjusts column widths based on content. You define your styling preferences once, and they are applied consistently every time.
⏩ Get the automated report formatter script
# 2. Regarding cross-source data
A word of pain: Your sales data is in the CRM, inventory counts come from the warehouse system, and finance has its own spreadsheet. Each analysis requires matching records in these sources, to deal with matching IDs, different date formats, and spelling variations in user names.
What does the script do?: Matches and reconciles records from different data sources using names, flexible date parsing, and fuzzy matching for multiple ID formats. Flags for review create contrasts and a unified dataset that you can actually analyze.
How it works: The script uses a fuzzy string matching algorithm to find possible matches even when the names do not exactly align. It standardizes dates from different formats, normalizes text fields (case, spacing, and special characters), and generates a match confidence score. Records that do not match well are flagged for manual review with side-by-side comparisons.
⏩ Get scripts related to cross-source data
# 3. Metric Dashboard Generator
A word of pain: Your manager wants to see KPIs updated weekly, stakeholders need monthly trend charts, and the executive team wants more quarter-to-quarter comparisons. You’re creating the same visualizations over and over with slightly different data, manually updating the labels, and adjusting the axes each time.
What does the script do?: Generates a full HTML dashboard with interactive charts showing your key metrics, trends, comparisons and performance indicators. Automatically update with new data and save to a file that you can email or publish internally.
How it works: Uses a script From the plot To create interactive visualizations that work in any browser. It calculates period-to-period changes, identifies trends, highlights outliers, and organizes everything into a clean, professional dashboard. The HTML file is self-contained—no dependencies are required to view it.
⏩ Get the metrics dashboard generator script
# 4. Schedule data refresher
A word of pain: You pull data from the same sources every morning to update your analysis. Log into the database, run the query, export to CSV, load it into Python, merge with other data sources, and save the result. It’s the same exact sequence every single day, stealing the first 30 minutes of your morning.
What does the script do?: Connects to your data sources on a schedule, pulls fresh data, performs your standard transformations, and saves the latest datasets ready for analysis. Set it once and your data is always current when you need it.
How it works: Scheduled execution in a script (using Schedule) with a database connection (using sqlalchemy) to automate data retrieval. It handles connection retries, logs all operations, sends notifications on failures, and maintains a timestamp log so you know exactly when the data was last refreshed.
⏩ Get the scheduled data refresh script
# 5. Smart Chart Generator
A word of pain: Sometimes you need to create several nearly identical charts showing performance by region, product, or time period. Each chart needs consistent formatting, appropriate labels, and specific styling to match the company’s branding. Manually means hours of copy-pasting and tweaking each one.
What does the script do?: Generates dozens of formatted charts from your data in seconds. Creates separate visualizations for each category, applies consistent styling, and saves them as high-quality images ready for presentations or reports.
How it works: The script iterates through the obvious errors in your data, generating standard visualizations using matplotlib And Seaborneapplies custom styling (color, font, layout) based on your preferences, and exports images ready for publication. You can generate a full deck of charts rather than creating three manually.
⏩ Get the Smart Chart Generator script
# The result
I hope you find this article helpful!
These five scripts identify specific challenges that data analysts face on a daily basis.
- An automated report formatter instantly turns raw analysis into polished Excel reports
- Cross-source data linking from different systems intelligently finds and integrates records from different systems
- Metric Dashboard Generator creates interactive HTML dashboards that update automatically
- Scheduled data refresher eliminates manual data pull from database and APIs
- Smart Chart Generator generates hundreds of consistently formatted visualizations in seconds
The key is to start small. Choose whichever script addresses your most annoying recurring task, test it against your actual data, and adjust it to meet your needs.
Your time is too valuable to spend on tasks that a script can handle. When you focus on finding insights that actually matter. Happy analysis!
Bala Priya c is a developer and technical writer from India. She loves working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include devops, data science, and natural language processing. She enjoys reading, writing, coding and coffee! Currently, she is working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces and more. Bala also engages resource reviews and coding lessons.