Metadata-Version: 2.1
Name: df4loop
Version: 0.1.0
Summary: df4loop supports general purpose processe that requires a combination of both pandas.DataFrame and loop.
Home-page: https://github.com/daikikatsuragawa/df4loop
Author: Daiki Katsuragawa
Author-email: daikikatsuragawa@gmail.com
License: Apache-2.0
Description: # df4loop
        
        df4loop supports general purpose processes that requires a combination of both pandas.DataFrame and loop. Specifically, the mission of df4loop is to "speed up processing" and "make complex code intuitive" at low installation costs.
        
        ## Installation
        
        ```sh
        pip install df4loop
        ```
        
        ## Usage
        
        The following DataFrame is defined to assist users envision the use of df4loop.
        
        ```py
        import pandas as pd
        
        sample_dict = {
            "column_1": [100, 200, 300, 400, 500],
            "column_2": ["A", "B", "C", "D", "E"],
            "column_3": ["a", "b", "c", "d", "e"],
        }
        df = pd.DataFrame.from_dict(sample_dict)
        df
        ```
        
        |     |column_1|column_2|column_3|
        |----:|-------:|--------|--------|
        |    0|     100|A       |a       |
        |    1|     200|B       |b       |
        |    2|     300|C       |c       |
        |    3|     400|D       |d       |
        |    4|     500|E       |e       |
        
        ### DFIterator
        
        DFIterator helps developers writing the following code. This is code written using pandas.DataFrame.iterrows for the purpose of referencing a value by row.
        
        ```py
        for index, row in df.iterrows():
            tmp = row["column_1"]
        ```
        
        DFIterator reproduces this process and speeds it up. Actually, DataFrame and its row pandas.Series are converted to lists and dictionaries to speed up. However, the usage is almost the same.
        
        ```py
        from df4loop import DFIterator
        
        df_iterator = DFIterator(df)
        for index, row in df_iterator.iterrows():
            tmp = row["column_1"]
        ```
        
        If you do not need to output the index, set `return_indexes=False`.
        
        ```py
        from df4loop import DFIterator
        
        df_iterator = DFIterator(df)
        for row in df_iterator.iterrows(return_indexes=False):
            tmp = row["column_1"]
        ```
        
        ### DFGenerator
        
        DFGenerator supports the generation of DataFrame with rows set by loops. Adding rows to the DataFrame in a loop will take a long time to process. The secret to speeding up is to organize rows in a list or dictionary and then make them pandas.DataFrame at once. DFGenerator supports this process for intuitive implementation.
        
        The following code is an example of selecting the dict type as the row.
        
        ```py
        from df4loop import DFGenerator
        
        # It is not necessary to specify columns.
        df_generator = DFGenerator(columns=df.columns.values.tolist())
        for _, row in df.iterrows():
            tmp_row = {
                "column_1": row["column_1"],
                "column_2": row["column_2"],
                "column_3": row["column_3"],
            }
            df_generator.append(tmp_row)
        new_df = df_generator.generate_df()
        ```
        
        The following code is an example of selecting the list type as the row. columns must be specified during initialization.
        
        ```py
        from df4loop import DFGenerator
        
        df_generator = DFGenerator(columns=df.columns.values.tolist())
        for _, row in df.iterrows():
            tmp_row = [
                row["column_1"],
                row["column_2"],
                row["column_3"],
            ]
            df_generator.append(tmp_row)
        new_df = df_generator.generate_df()
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
