inspecty 0.1.0

Universal data inspector — one function to understand any dataset

inspecty

Universal data inspector — one function to understand any dataset.

pip install inspecty

Instead of print(), type(), .info(), .describe() — just run ins.inspect(data) and get a high-density statistical report for any data type.

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Supported data types

DataFrame, Series, NumPy array (1D/2D/3D), list, tuple, set, dictionary, JSON string, CSV file path, Excel file path — any Python object.

Quick example

import inspecty as ins
import pandas as pd

df = pd.DataFrame({
    "price":  [100.5, 101.2, 100.8, 102.1, 101.5],
    "volume": [1000, 1500, 1200, 1800, 2000],
})

ins.inspect(df)

Output:

Data type: <class 'pandas.DataFrame'>
Total rows: 5. Total columns: 2. Summary:

column:      price      volume
 dtype:    float64       int64
     -      -----       -----
 Row 0:      100.5        1000
 Row 1:      101.2        1500
 Row 2:      100.8        1200
----------  -----       -----
 count:          5           5
   max:      102.1      2000.0
   min:      100.5      1000.0
  mean:     101.22      1500.0
   std:   0.622093  412.310563
median:      101.2      1500.0
  mode:      100.5      1000.0
   nan:          0           0

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More examples

Pandas Series

series = pd.Series([10, 20, 30, 40, 50], name="RSI")
ins.inspect(series)

Dictionary pivot

data = {
    "Ticker": ["BTC", "ETH", "SOL"],
    "Price":  [62000.5, 3400.2, 145.1],
    "Signal": ["Buy", "Hold", "Buy"],
}
ins.inspect(data)

3D NumPy tensor

tensor = np.random.randn(2, 100, 5)  # (Tickers, Days, Features)
ins.inspect(tensor)

Auto-load files

ins.inspect("historical_prices.csv")
ins.inspect("data.xlsx")

JSON string

json_resp = '{"Symbol": ["AAPL", "TSLA"], "Price": [150.2, 700.5]}'
ins.inspect(json_resp)

System info

ins.inspect("all_info")

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Parameters

Param	Type	Default	Description
df	Any	required	Data object, file path, or JSON string
count	int	3	Rows shown from top & bottom
silent	bool	False	Hide headers, show only table

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Why not df.describe()?

df.describe() works only on numeric columns, hides NaN counts, and needs multiple calls for basic stats. inspect() handles mixed types, shows NaN explicitly, and works on any data structure — not just DataFrames.

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License

MIT