15 Prompts for Data Science: Data Analysis, Visualization, and Pandas

## Introduction Data science is as much about asking the right questions as it is about writing code. The real skill lies in translating business problems into precise, reproducible data manipulations. Over the past decade, Python's Pandas library has become the de facto tool for data wrangling, while Matplotlib and Seaborn remain the workhorses for visualization. But even experienced analysts sometimes waste hours crafting the perfect transformation or debugging a plot. This is where structured prompts come in: they act as mental scaffolds, turning vague requests into actionable code. In this article, I share 15 carefully designed prompts for data analysis, Pandas operations, and visualization that will save you time and reduce errors. Each prompt includes a concrete example so you can see the result immediately. Whether you are cleaning messy survey data, building financial dashboards, or preparing a report for stakeholders, these prompts will help you work faster and more confidently. ## 1. Basic Prompts: Data Loading and Inspection ### Prompt 1: Load Data and Display Summary Statistics - **Task**: Load a CSV file, show the first five rows, and print descriptive statistics for all numeric columns. - **Prompt**: "Load the file `sales.csv` into a Pandas DataFrame, display the head, and call `describe()` on the numeric columns." - **Example**: ```python import pandas as pd df = pd.read_csv('sales.csv') print(df.head()) print(df.describe()) ``` ### Prompt 2: Check for Missing Values - **Task**: Identify which columns contain null values and count them. - **Prompt**: "Count missing values per column in `df` and list columns with more than 5% missing." - **Example**: ```python missing = df.isnull().sum() missing_percent = (missing / len(df)) * 100 print(missing[missing > 0]) print(missing_percent[missing_percent > 5]) ``` ### Prompt 3: Filter Rows Based on Condition - **Task**: Return all rows where the `revenue` column exceeds 1000 and the `region` is 'Europe'. - **Prompt**: "Filter `df` for rows where `revenue > 1000` and `region == 'Europe'`." - **Example**: ```python filtered = df[(df['revenue'] > 1000) & (df['region'] == 'Europe')] print(filtered.head()) ``` ## 2. Intermediate Prompts: Data Transformation with Pandas ### Prompt 4: Group By and Aggregate - **Task**: Group data by `category` and calculate sum, mean, and count for `sales`. - **Prompt**: "Group `df` by `category` and compute sum, mean, and count of `sales`." - **Example**: ```python grouped = df.groupby('category')['sales'].agg(['sum', 'mean', 'count']) print(grouped) ``` ### Prompt 5: Apply a Custom Function to a Column - **Task**: Create a new column `price_category` that labels values as 'low', 'medium', or 'high' based on quantiles. - **Prompt**: "Add a column `price_category` using `pd.cut` with three equal-width bins on `price`." - **Example**: ```python df['price_category'] = pd.cut(df['price'], bins=3, labels=['low', 'medium', 'high']) print(df[['price', 'price_category']].head()) ``` ### Prompt 6: Merge Two DataFrames on a Key - **Task**: Combine `orders` and `customers` DataFrames on `customer_id` using a left join. - **Prompt**: "Merge `orders` with `customers` on `customer_id` using a left join." - **Example**: ```python merged = pd.merge(orders, customers, on='customer_id', how='left') print(merged.head()) ``` ### Prompt 7: Pivot Table for Summary Matrix - **Task**: Create a pivot table showing average revenue per region and product category. - **Prompt**: "Build a pivot table from `df` with `region` as index, `category` as columns, and `revenue` as values using mean." - **Example**: ```python pivot = pd.pivot_table(df, values='revenue', index='region', columns='category', aggfunc='mean') print(pivot) ``` ## 3. Advanced Prompts: Visualization with Matplotlib and Seaborn ### Prompt 8: Histogram with Custom Bins - **Task**: Plot the distribution of `age` with 30 bins and a Kernel Density Estimate (KDE) overlay. - **Prompt**: "Use Seaborn's `histplot` to show distribution of `age` with 30 bins and KDE." - **Example**: ```python import seaborn as sns import matplotlib.pyplot as plt sns.histplot(df['age'], bins=30, kde=True) plt.title('Age Distribution') plt.show() ``` ### Prompt 9: Box Plot to Detect Outliers - **Task**: Compare `salary` distributions across `department` using a box plot. - **Prompt**: "Create a Seaborn box plot with `department` on x-axis and `salary` on y-axis." - **Example**: ```python sns.boxplot(x='department', y='salary', data=df) plt.title('Salary by Department') plt.xticks(rotation=45) plt.show() ``` ### Prompt 10: Correlation Heatmap - **Task**: Compute and visualize correlation matrix for numeric columns. - **Prompt**: "Plot a heatmap of the correlation matrix using Seaborn with annotations." - **Example**: ```python corr = df.select_dtypes(include='number').corr() sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f') plt.title('Correlation Heatmap') plt.show() ``` ### Prompt 11: Time Series Line Plot - **Task**: Plot monthly sales trend with a rolling average of 3 months. - **Prompt**: "Convert `date` to datetime, set as index, resample by month, plot sales and rolling mean." - **Example**: ```python df['date'] = pd.to_datetime(df['date']) df.set_index('date', inplace=True) monthly = df['sales'].resample('M').sum() monthly.plot(label='Monthly Sales') monthly.rolling(window=3).mean().plot(label='3-Month Rolling Avg') plt.legend() plt.title('Monthly Sales Trend') plt.show() ``` ## 4. Expert Prompts: Performance and Automation ### Prompt 12: Efficient Column Transformation with `apply` vs Vectorization - **Task**: Convert a column of strings to lowercase using vectorized operations instead of `apply`. - **Prompt**: "Use `.str.lower()` on `name` column to avoid slow `apply`." - **Example**: ```python # Slow df['name_lower'] = df['name'].apply(lambda x: x.lower()) # Fast (vectorized) df['name_lower'] = df['name'].str.lower() ``` ### Prompt 13: Chained Operations with `pipe` - **Task**: Apply a sequence of cleaning steps (drop nulls, filter, rename) using `pipe`. - **Prompt**: "Chain `dropna`, `query`, and `rename` using `.pipe()` for readability." - **Example**: ```python def drop_nulls(df): return df.dropna() def filter_europe(df): return df[df['region'] == 'Europe'] def rename_cols(df): return df.rename(columns={'sales': 'revenue'}) cleaned = (df.pipe(drop_nulls) .pipe(filter_europe) .pipe(rename_cols)) ``` ### Prompt 14: Parallel Processing with `swifter` - **Task**: Apply a slow function to a large DataFrame in parallel. - **Prompt**: "Use `swifter.apply` to parallelize a custom function on a DataFrame." - **Example**: ```python import swifter def complex_calc(x): # some heavy computation return x ** 2 + 1 df['result'] = df['value'].swifter.apply(complex_calc) ``` ### Prompt 15: Automated Report Generation with Jinja2 and Matplotlib - **Task**: Generate an HTML report containing summary stats and plots from a DataFrame. - **Prompt**: "Create a function that renders a Jinja2 template with `df.describe()` and a saved plot." - **Example**: ```python from jinja2 import Template import matplotlib.pyplot as plt # Save plot plt.figure() df['age'].hist() plt.savefig('age_hist.png') # Render HTML template = Template(''' Report {{ table }} ''') html = template.render(table=df.describe().to_html()) with open('report.html', 'w') as f: f.write(html) ``` ## Conclusion Prompts are not magic; they are structured thinking. By breaking down a data science task into clear steps—load, inspect, transform, visualize, and automate—you reduce cognitive load and improve reproducibility. The 15 prompts in this collection cover the most common operations in Pandas, Matplotlib, and Seaborn. Start by copying them directly, then modify the parameters to fit your data. Over time, you will internalize these patterns and develop your own library of prompts. The best data scientists are not those who know every function, but those who can quickly translate a business question into a sequence of precise operations. Use these prompts as a foundation, and you will spend less time debugging and more time discovering insights. ## Further Reading - Pandas official documentation: [https://pandas.pydata.org/docs/](https://pandas.pydata.org/docs/) - Seaborn tutorial: [https://seaborn.pydata.org/tutorial.html](https://seaborn.pydata.org/tutorial.html) - Matplotlib cheatsheets: [https://matplotlib.org/cheatsheets/](https://matplotlib.org/cheatsheets/)

15 Prompts for Data Science: Data Analysis, Visualization, and Pandas

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