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pyskim\n\n[![PyPI](https://img.shields.io/pypi/v/pyskim.svg?style=flat)](https://pypi.python.org/pypi/pyskim)\n[![Tests](https://github.com/kpj/pyskim/actions/workflows/main.yaml/badge.svg)](https://github.com/kpj/pyskim/actions/workflows/main.yaml)\n\nQuickly create summary statistics for a given dataframe.\n\nThis package aspires to be as awesome as [skimr](https://github.com/ropensci/skimr).\n\n\n## Installation\n\n```bash\n$ pip install pyskim\n```\n\n## Usage\n\n### Commandline tool\n\n\n`pyskim` can be used from the commandline:\n\n```bash\n$ pyskim iris.csv\n── Data Summary ────────────────────────────────────────────────────────────────────────────────────\ntype                 value\n-----------------  -------\nNumber of rows         150\nNumber of columns        5\n──────────────────────────────────────────────────\nColumn type frequency:\n           Count\n-------  -------\nFloat64        4\nstring         1\n\n── Variable type: number ───────────────────────────────────────────────────────────────────────────\n    name            na_count    mean     sd    p0    p25    p50    p75    p100  hist\n--  ------------  ----------  ------  -----  ----  -----  -----  -----  ------  ----------\n 0  sepal_length           0    5.84  0.828   4.3    5.1   5.8     6.4     7.9  ▂▆▃▇▄▇▅▁▁▁\n 1  sepal_width            0    3.06  0.436   2      2.8   3       3.3     4.4  ▁▁▄▅▇▆▂▂▁▁\n 2  petal_length           0    3.76  1.77    1      1.6   4.35    5.1     6.9  ▇▃▁▁▂▅▆▄▃▁\n 3  petal_width            0    1.2   0.762   0.1    0.3   1.3     1.8     2.5  ▇▂▁▂▂▆▁▄▂▃\n\n── Variable type: string ───────────────────────────────────────────────────────────────────────────\n    name       na_count    n_unique  top_counts\n--  -------  ----------  ----------  -----------------------------------------\n 0  species           0           3  setosa: 50, versicolor: 50, virginica: 50\n```\n\nFull overview:\n\n```bash\n$ pyskim --help\nUsage: pyskim [OPTIONS] \u003cfile\u003e\n\n  Quickly create summary statistics for a given dataframe.\n\nOptions:\n  -d, --delimiter TEXT   Delimiter of file.\n  -i, --interactive      Open prompt with dataframe as `df` after displaying\n                         summary.\n  --no-dtype-conversion  Skip automatic dtype conversion.\n  --groupby TEXT         Group dataframe by this/these variable(s).\n  --help                 Show this message and exit.\n```\n\n### Python API\n\nAlternatively, it is possible to use it in code:\n\n```python\n\u003e\u003e\u003e from pyskim import skim\n\u003e\u003e\u003e from seaborn import load_dataset\n\n\u003e\u003e\u003e iris = load_dataset('iris')\n\u003e\u003e\u003e skim(iris)\n# ── Data Summary ────────────────────────────────────────────────────────────────────────────────────\n# type                 value\n# -----------------  -------\n# Number of rows         150\n# Number of columns        5\n# ──────────────────────────────────────────────────\n# Column type frequency:\n#            Count\n# -------  -------\n# float64        4\n# string         1\n#\n# ── Variable type: number ───────────────────────────────────────────────────────────────────────────\n#     name            na_count    mean     sd    p0    p25    p50    p75    p100  hist\n# --  ------------  ----------  ------  -----  ----  -----  -----  -----  ------  ----------\n#  0  sepal_length           0    5.84  0.828   4.3    5.1   5.8     6.4     7.9  ▂▆▃▇▄▇▅▁▁▁\n#  1  sepal_width            0    3.06  0.436   2      2.8   3       3.3     4.4  ▁▁▄▅▇▆▂▂▁▁\n#  2  petal_length           0    3.76  1.77    1      1.6   4.35    5.1     6.9  ▇▃▁▁▂▅▆▄▃▁\n#  3  petal_width            0    1.2   0.762   0.1    0.3   1.3     1.8     2.5  ▇▂▁▂▂▆▁▄▂▃\n#\n# ── Variable type: string ───────────────────────────────────────────────────────────────────────────\n#     name               na_count    n_unique  top_counts\n# --  ---------------  ----------  ----------  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