Metadata-Version: 2.1
Name: pandas_selectable
Version: 1.0.1
Summary: Add a select accessor to pandas
Home-page: https://github.com/jseabold/pandas-selectable
Author: Skipper Seabold
License: UNKNOWN
Description: # pandas-selectable
        
        ![test status](https://github.com/jseabold/pandas-selectable/workflows/tests/badge.svg)
        
        ## What Is It?
        
        `pandas-selectable` adds a `select` accessor to pandas DataFrames and Series. It's like `query` but with the niceties of tab-completion.
        
        ## Quickstart
        
        ```python
        In [1]: import numpy as np
        
        In [2]: import pandas as pd
        
        In [3]: import pandas_selectable  # magic
        
        In [4]: dta = pd.DataFrame.from_dict({
           ...:     'A': ['A', 'B', 'C'] * 5,
           ...:     'B': np.arange(1, 16),
           ...:     'C': pd.date_range('2020-01-01', periods=15)
           ...: })
        
        In [5]: dta.head()
        Out[5]:
           A  B          C
        0  A  1 2020-01-01
        1  B  2 2020-01-02
        2  C  3 2020-01-03
        3  A  4 2020-01-04
        4  B  5 2020-01-05
        
        In [6]: dta.select.A == 'B'
        Out[6]:
            A   B          C
        1   B   2 2020-01-02
        4   B   5 2020-01-05
        7   B   8 2020-01-08
        10  B  11 2020-01-11
        13  B  14 2020-01-14
        
        In [7]: dta.select.C >= '2020-01-03'
        Out[7]:
            A   B          C
        2   C   3 2020-01-03
        3   A   4 2020-01-04
        4   B   5 2020-01-05
        5   C   6 2020-01-06
        6   A   7 2020-01-07
        7   B   8 2020-01-08
        8   C   9 2020-01-09
        9   A  10 2020-01-10
        10  B  11 2020-01-11
        11  C  12 2020-01-12
        12  A  13 2020-01-13
        13  B  14 2020-01-14
        14  C  15 2020-01-15
        
        In [8]: dta.select.A.str.contains('A')
        Out[8]:
            A   B          C
        0   A   1 2020-01-01
        3   A   4 2020-01-04
        6   A   7 2020-01-07
        9   A  10 2020-01-10
        12  A  13 2020-01-13
        
        In [9]: dta.select.C.dt.is_month_start
        Out[9]:
           A  B          C
        0  A  1 2020-01-01
        ```
        
        It also works for Series.
        
        ```python
        In [10]: dta.A.select == 'A'
        Out[10]:
        0     A
        3     A
        6     A
        9     A
        12    A
        Name: A, dtype: object
        ```
        
        Though the string and datetime accessor APIs are slightly inconsistent. They're available via the select accessor now.
        
        ```python
        In [11]: dta.A.select.str.contains('B')
        Out[11]:
        1     B
        4     B
        7     B
        10    B
        13    B
        Name: A, dtype: object
        ```
        
        ## Requirements
        
        [pandas](https://pandas.pydata.org/) >= 1.1
        
        ## Installation
        
        ```bash
        pip install pandas-selectable
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
