Descriptive Statistics

Endpoint: POST /descriptive-statistics

Request Parameters

File Upload

• file (required): The file to be processed. Supported formats include CSV, Excel, etc.

Columns Selection

• columns (optional): Comma-separated list of columns to include in the analysis. If omitted, all columns are included.

Exclude Columns

• exclude_columns (optional): Comma-separated list of columns to exclude from the analysis.

Numerical Statistics

• num_stats (optional): Comma-separated list of numerical statistics to compute. Default values are ["mean", "median", "std", "min", "max", "percentiles", "skew", "kurtosis"]. Available options: mean, median, std, min, max, percentiles, skew, kurtosis.

Categorical Statistics

• cat_stats (optional): Comma-separated list of categorical statistics to compute. Default values are ["count", "unique", "top", "frequency", "mode"]. Available options: count, unique, top, frequency, mode.

Graphs

• include_graphs (optional): Boolean to specify if graphs should be included in the response. Default is false.
• graph_types (optional): Comma-separated list of graph types to include in the analysis. Default is ["Histogram"]. Available options: Histogram, Boxplot, Bar.

Example Request

curl -X POST "http://localhost:8000/descriptive-statistics" \
-F "file=@data.csv" \
-F "columns=Age,Income" \
-F "exclude_columns=Gender" \
-F "num_stats=mean,median,skew" \
-F "cat_stats=count,unique" \
-F "include_graphs=true" \
-F "graph_types=Histogram,Boxplot"

Analysis Components

1. Numerical Statistics: Computes statistical values for numerical columns. This includes mean, median, standard deviation, minimum, maximum, percentiles, skewness, and kurtosis.
2. Categorical Statistics: Provides statistics for categorical columns, such as count, unique values, mode, and frequency distribution.
3. Graphs: Generates optional visualizations (e.g., histograms, boxplots, or bar charts) for numerical or categorical data.

Example Response

{
  "numerical_stats": {
    "mean": {"Age": 35.5, "Income": 55000},
    "median": {"Age": 35, "Income": 50000},
    "std": {"Age": 10.2, "Income": 12000},
    "min": {"Age": 18, "Income": 20000},
    "max": {"Age": 60, "Income": 100000},
    "percentiles": {"Age": {"25th": 25, "50th": 35, "75th": 45}},
    "skew": {"Age": 0.5, "Income": 0.1},
    "kurtosis": {"Age": 2.1, "Income": 1.8}
  },
  "categorical_stats": {
    "count": {"Gender": 100, "Education": 100},
    "unique": {"Gender": 2, "Education": 4},
    "top": {"Gender": "Male", "Education": "Bachelor"},
    "frequency": {"Gender": {"Male": 60, "Female": 40}},
    "mode": {"Gender": "Male", "Education": "Bachelor"}
  },
  "graphs": {
    "histogram": {
      "Age": {"bin_edges": [18, 26, 34, 42, 50, 60], "frequencies": [10, 20, 30, 25, 15]},
      "Income": {"bin_edges": [20000, 40000, 60000, 80000, 100000], "frequencies": [10, 30, 40, 15, 5]}
    },
    "boxplot": {
      "Age": {"min": 18, "q1": 25, "median": 35, "q3": 45, "max": 60},
      "Income": {"min": 20000, "q1": 40000, "median": 50000, "q3": 70000, "max": 100000}
    }
  }
}