Data Analyzer

Load structured CSV, Excel, or JSON data to compute stats, detect anomalies, analyze trends and correlations, and generate summary reports with chart suggest...

Data Analyzer — 数据分析工具

Load, analyze, and report on structured data from CSV, Excel, and JSON files. Compute statistics, detect anomalies, identify trends, and generate reports with visualization recommendations.

Workflow

1. Load data     → Read the file, inspect structure
2. Profile       → Column types, missing values, basic stats
3. Analyze       → Statistics, trends, anomalies, correlations
4. Report        → Summary with visual recommendations

Step 1 — Data Loading

Supported Formats

Format	How to Read	Notes
CSV	Read the file directly, parse header row + data rows	Check delimiter (comma, tab, semicolon). Handle quoted fields.
Excel (.xlsx)	Read via openpyxl or pandas. If unavailable, convert to CSV first.	Handle multiple sheets. Note which sheet was used.
JSON	Parse as structured objects. Detect if array-of-objects or object-of-arrays.	Flatten nested structures where possible.
TSV	Same as CSV with tab delimiter.

If Python is available (recommended for large datasets):

pip install pandas openpyxl  # if missing
python3 -c "
import pandas as pd
df = pd.read_csv('data.csv')
print(df.info())
print(df.describe())
print(df.head())
"

If Python is not available, parse manually:

1. Read the file line by line
2. Identify headers (first row)
3. Identify column types (numeric vs text vs date)
4. Store as an array of rows or objects

Initial Inspection

After loading, always answer these questions:

• Shape: How many rows and columns?
• Column names: What are they and what data types?
• Missing values: Which columns have gaps, and how many?
• Date/time columns: Are they parsed as datetime objects?
• Unique values: For categorical columns, how many unique categories?

Step 2 — Descriptive Statistics

Numeric Columns

Compute and report:

Statistic	What It Tells You
Count	Number of non-null values
Mean	Average value
Median	Midpoint (50th percentile) — more robust than mean for skewed data
Std Dev	Spread around the mean
Min / Max	Full range
25th / 75th Percentile	Interquartile range bounds
Skewness	Symmetry of the distribution. Positive = right tail, negative = left tail.

Formula reference (manual calculation):

Mean       = sum(x) / n
Median     = middle value when sorted
Std Dev    = sqrt(sum((x - mean)^2) / (n-1))
Percentile = sort values, take value at position (p/100 * n)

Categorical Columns

Statistic	What It Tells You
Count	Total non-null values
Unique	Number of distinct categories
Top	Most frequent category
Frequency	How often the top category appears
Distribution	Share of each category (as percentages)

Step 3 — Analysis

3a. Anomaly Detection

Method: IQR (Interquartile Range)

Q1 = 25th percentile
Q3 = 75th percentile
IQR = Q3 - Q1
Lower fence = Q1 - 1.5 * IQR
Upper fence = Q3 + 1.5 * IQR
Anomaly = any value outside [Lower fence, Upper fence]

Method: Z-Score (for approximately normal distributions)

z = (x - mean) / std_dev
Anomaly = |z| > 3 (values more than 3 std devs from mean)

Output anomalies: For each detected anomaly, report:

• Row index
• Column name
• Anomalous value
• Distance from expected (how many IQRs or std devs)

3b. Trend Analysis

For time-series data (data with a date/time column):

1. Identify the time column — Sort by date
2. Aggregate by period — Group by day/week/month/quarter/year
3. Direction — Is the metric increasing, decreasing, or flat?
4. Rate of change — Period-over-period percentage change
5. Seasonality — Recurring patterns (monthly, quarterly, yearly)
6. Breakout — Sudden jumps or drops (potential regime changes)

Output format:

📈 Trend: [Metric Name]
Period: [Date Range]
Direction: [Up/Down/Flat] (slope: ±X%)
Key Points:
- [Date]: Value = X (↗/↘/→)
- Highest point: [Date] = X
- Lowest point: [Date] = X

For non-time-series data, analyze rank order and distribution shape:

Top 5 by [metric]:
1. [Category] = X
2. [Category] = Y
...
Bottom 5 by [metric]:

3c. Correlation Analysis

Pearson correlation coefficient (for linear relationships between two numeric variables):

r = sum((x - mean_x) * (y - mean_y)) / (n * std_x * std_y)

Interpretation:

r value	Strength	Direction
0.7 to 1.0	Strong	Positive (both rise together)
0.3 to 0.7	Moderate	Positive
0 to 0.3	Weak	Positive
-0.3 to 0	Weak	Negative (one rises, other falls)
-0.7 to -0.3	Moderate	Negative
-1.0 to -0.7	Strong	Negative

Caveats:

• Correlation ≠ causation. Always note this.
• Pearson only captures linear relationships.
• Outliers can distort correlation heavily — check after removing anomalies.

Step 4 — Report Generation

Visualization Recommendations

For each finding, recommend the best chart type:

Analysis Type	Recommended Chart	Why
Distribution of one variable	Histogram	Shows shape, skew, peaks
Comparison across categories	Bar chart	Easy to compare magnitudes
Trend over time	Line chart	Emphasizes direction and continuity
Relationship between 2 variables	Scatter plot	Shows correlation, clusters, outliers
Part of a whole	Pie / Donut chart	Use only for 2-5 categories
Composition over time	Stacked area chart	Shows both total and parts
Rank order	Horizontal bar chart	Easy to read sorted values
Comparing multiple distributions	Box plot	Shows median, IQR, outliers
Heatmap (correlation matrix)	Heatmap	Quick visual of many correlations

Full Report Template

# Data Analysis Report: [Dataset Name]
Date: [YYYY-MM-DD]

## 1. Overview
- Rows: X | Columns: Y
- Missing data: X cells (X%)
- Key columns: [list with types]

## 2. Descriptive Statistics
### Numeric Columns
[Table: col_name, count, mean, median, std, min, 25%, 75%, max]

### Categorical Columns
[Table: col_name, unique_count, top_value, frequency%]

## 3. Key Findings

### Finding 1: [Title]
[Description of finding]
📊 Recommended chart: [Chart type]
Supporting data: [stats/view]

### Finding 2: [Title]
...

## 4. Anomalies Detected
[Table: row, column, value, severity]

## 5. Correlations
[Notable correlations >|0.3| or < -|0.3|]

## 6. Recommendations
[Data-driven suggestions based on analysis]

One-Page Summary (Quick)

For quick results, use this compact format:

📊 [Dataset]: [N] rows × [M] cols

📈 Key metrics:
- [metric1]: mean=X, median=Y, range=[min, max]
- [metric2]: ...

🔍 Top findings:
1. [Finding] — [chart recommendation]
2. [Finding] — [chart recommendation]

⚠️ Anomalies: X detected

Python Script (Optional)

For complex analysis, create and run a Python script:

import csv, json, statistics
from collections import Counter

# Load data
with open('data.csv') as f:
    reader = csv.DictReader(f)
    rows = list(reader)

# Get numeric columns
# (column name → list of float values, filtering out blanks)
# Compute mean, median, stdev, percentiles
# Detect outliers via IQR
# Compute correlations between pairs
# Print formatted results

Run with:

python3 analysis.py