Metadata-Version: 2.1
Name: pandas-shortcuts
Version: 0.0.1
Summary: Why even wait for autocompletion when you can use `pandas_shortcuts`?
Home-page: https://github.com/baogianghoangvu/pandas-shortcuts
Author: BaoGiang HoangVu
Author-email: hoangvubaogiang@gmail.com
License: UNKNOWN
Project-URL: Bug Reports, https://github.com/baogianghoangvu/pandas-shortcuts/issues
Project-URL: Source, https://github.com/baogianghoangvu/pandas-shortcuts
Description: # pandas-shortcuts
        
        [![.](https://img.shields.io/static/v1?logo=github&label=maintainer&message=baogianghoangvu&color=violet)](https://github.com/baogianghoangvu)
        
        [![.](https://img.shields.io/badge/version-0.0.1-informational)](https://github.com/baogianghoangvu/pandas-shortcuts/blob/main/pandas_shortcuts/__init__.py)
        [![.](https://img.shields.io/badge/python-3.8-important)](https://github.com/python/cpython)
        [![.](https://img.shields.io/badge/code%20style-black-black)](https://github.com/psf/black)
        
        Why even wait for autocompletion when you can use `pandas_shortcuts`?
        
        ## How to use
        
        - Simply import `pandas_shortcuts` together with `pandas`.
        
          ```Python
          import pandas as pd
          import pandas_shortcuts
          ```
        
        - Every `pd.DataFrame` and `pd.Series` objects will have:
        
          - shortcuts (full list [below](#available-shortcuts-and-methods))
        
          ```Python
          # shortcut for `df.head()`
          df.h()
        
          # shortcut for df.columns
          df.c
        
          # shortcut for df["col"].unique()
          df["col"].u()
          ```
        
          - new methods (full list [below](#available-shortcuts-and-methods))
        
          ```Python
          # view up to `r` rows and `c` columns of a dataframe, overiding pandas' default limit
          df.v()  # default r=50, c=50
        
          # view up to `r` rows of a series, overiding pandas' default limit
          df["col"].v(100)
        
          # stylize a dataframe's numeric columns as heatmap or bars
          # view up to `r` rows and `c` of a dataframe, overiding pandas' default limit
          df.sh()  # style=heatmap
          df.sb()  # style=bar
        
          # call `dtale.show`, refer to dtale docs for details
          df.dt()
        
          # call `pandas_profiling.ProfileReport`, refer to pandas_profiling docs for details
          df.pp()
          ```
        
        ## Available Shortcuts and Methods
        
        ```Python
        
        # Heads or tails
        df.h(...)  # df.head(...)
        df.t(...)  # df.tail(...)
        df["col"].h(...)  # df["col"].head(...)
        df["col"].t(...)  # df["col"].tail(...)
        
        # Sort
        df.si(...)  # df.sort_index(...)
        df["col"].si(...)  # df["col"].sort_index(...)
        df.sv(...)  # df.sort_values(...)
        df["col"].sv(...)  # df["col"].sort_values(...)
        
        # Index
        df.sx(...)  # df.set_index(...)
        df.rx(...)  # df.reset_index(...)
        df["col"].rx(...)  # df["col"].reset_index(...)
        
        # Groupby
        df.gb(...)  # df.groupby(...)
        df["col"].gb(...)  # df["col"].groupby(...)
        
        # Duplicates
        df.dd(...)  # df.drop_duplicates(...)
        df["col"].dd(...)  # df["col"].drop_duplicates(...)
        df.dup(...)  # df.duplicated(...)
        df["col"].dup(...)  # df["col"].duplicated(...)
        df["col"].u(...)  # df["col"].unique(...)
        
        # Properties
        df.c  # df.columns
        df.i  # df.index
        df["col"].i  # df["col"].index
        
        
        # Methods
        df.v(...)
        df["col"].v(...)
        df.sh(...)
        df.sb(...)
        df.dt(...)
        df.pp(...)
        ```
        
        ## Note
        
        - Some dependencies (`pandas_profiling`'s `numba` and `llvmlite`) do not support Python 3.9 as of `pandas_shortcuts` v0.0.1
        - `df.v()` directly generates `IPython.core.display.HTML` object under the hood, thus completely bypassing any `pd.set_option("display.max_rows", ...)` and `pd.set_option("display.max_columns", ...)` that the user may have specified.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: <3.9
Description-Content-Type: text/markdown
