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Capabilities

Data Analyst provides comprehensive statistical analysis capabilities through natural language queries. The system converts business questions into Python code using professional statistical libraries, enabling sophisticated data analysis without requiring programming knowledge.

Data Exploration Features​

Column Analysis​

  • Data Profiling: Examine column names, data types, and basic characteristics
  • Data Quality: Assess completeness, uniqueness, and consistency
  • Value Distributions: Understand the range and frequency of values
  • Missing Data: Identify and quantify missing or null values

Filtering and Grouping​

  • Conditional Filtering: Filter data based on specific criteria or value ranges
  • Date-based Filtering: Extract data for specific dates, months, or time periods
  • Category Grouping: Group data by client, region, product, or other dimensions
  • Multi-level Grouping: Analyze data across multiple grouping dimensions

Advanced Statistics​

  • Correlation Analysis: Identify statistical relationships between variables
  • Hypothesis Testing: T-tests, Chi-Square and ANOVA
  • Statistical Significance: P-values, confidence intervals, and effect size calculations
  • Distribution Testing: Normality tests, goodness-of-fit tests, and distribution comparisons

Available Python Libraries​

Core Data Analysis​

  • Pandas: Advanced data manipulation, filtering, grouping, and aggregation

  • NumPy: Mathematical operations, statistical calculations, and array processing

  • SciPy/Statsmodels: Statistical functions and hypothesis testing

  • Matplotlib/Seaborn: Data visualization and chart generation capabilities

Statistical Analysis Types​

Descriptive Statistics​

  • Summary Statistics: Calculate means, medians, modes, and standard deviations
  • Aggregation Functions: Generate totals, sums, averages, and counts
  • Distribution Analysis: Understand data distributions and frequency patterns
  • Percentile Analysis: Identify quartiles, percentiles, and ranking metrics
"Calculate mean, median, and standard deviation for transaction amounts"
"Show distribution of clients by transaction volume"
"What is the correlation between transaction count and total debit?"
"Provide statistical summary of balance changes over time"

Comparative Analysis​

  • Group Comparisons: Compare performance across different segments or categories
  • Ranking Operations: Identify top/bottom performers by various metrics
  • Cross-tabulation: Analyze relationships between categorical variables
  • Trend Analysis: Compare values across different time periods
"Compare performance across different regions"
"Rank clients by average transaction size"
"Show transaction patterns by month"
"Identify top performers in each category"

Financial Analysis​

  • Transaction Analysis: Calculate total debits, credits, and net flows
  • Balance Tracking: Monitor account balances and balance changes over time
  • Client Profiling: Analyze client transaction patterns and behavior
  • Currency Analysis: Group and analyze transactions by currency type
"Calculate total debit and credit for each client"
"Show net flow by currency type"
"Which transaction types contribute most to overall volume?"
"Analyze balance trends for top 10 clients"

Hypothesis Testing & Statistical Tests​

  • Parametric Tests: Compare means between groups with normal distributions
  • Non-parametric Tests: Compare groups without normality assumptions
  • Independence Tests: Test relationships between categorical variables
  • Normality Testing: Verify if data follows normal distribution patterns
"Use ANOVA to see if the average debit amount differs across the top 3 clients."
"Test if there's a significant difference in transaction amounts between currencies"
"Perform a chi-square test on transaction types by client categories"
"Test if client transaction volumes follow a normal distribution"

Analysis Output Formats​

Table Generation​

  • Structured Tables: Professional formatting with clear headers and data alignment
  • Statistical Summaries: Organized presentation of calculated metrics and analysis
  • Downloadable Results: Tables available for download in CSV format
  • Formatted Numbers: Proper number formatting for financial and statistical data

Table Features​

  • Column Headers: Clear, descriptive column names for easy interpretation
  • Data Alignment: Proper alignment of numerical and text data
  • Sorting Options: Results can be organized by different criteria
  • Summary Rows: Totals and subtotals where appropriate