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        "description": "\n**nicexcel** is a lightweight Python package \nthat provides the possibility of exporting pandas \nnative DataFrame objects into nicely formatted MS Excel files.\n\nThe package leverages **pandas** and **openpyxl** open-source \nPython libraries to add some simple aesthetic details to output Excel files.\nThis improves the overall aspect and usability of the files, hence, it \ncan help in the automation of reporting activities.\n\n1. Column widths auto-adapted to fit characters contained inside them\n2. Header row containing filterable columns within Excel (by default)\n3. Header row freezed (by default)\n4. No indexing by default (vs **pandas** default index set on)\n5. Columns in the output Excel file have a formatting that resembles \n**pandas** default data types. However, it is still possible to specify some \nnumber format via specific arguments\n\n#### Getting started with *to_excel()*\n\nThe **nicexcel** package main functionality consists in the exporting of \npandas.DataFrame object. The *to_excel()* method is conceived as\na wrapper of the popular *pd.DataFrame.to_excel()* that takes two \nkey inputs:\n   * **df**: pd.DataFrame instance\n   * **filename**: string containing file path where the output \n   *.xlsx* file will be saved\n\nSee below a simple example of application\n\n```python\nimport nicexcel as nl\nimport pandas as pd\n\n# instantiate mock data\nmock_df = pd.DataFrame(data={\n    'Product Line': [\"T-Shirts\", \"Jeans\", \"Jackets\"],\n    'Units sold': [11213, 9908, 2345],\n    'Yearly Revenues': [30000.0411, 45513.1228, 10220.103],\n    'Gross Margin (%)': [0.223, 0.1151, 0.458]})\n\n# column format\ncols_format = {'Integer': ['Yearly Income'], '%': ['Gross Margin (%)']}\n\n# generate nicely formatted excel\nnl.to_excel(\n    df=mock_df,\n    filename='output.xlsx',\n    cols_format=cols_format)\n``` \n\n![Output Results](https://raw.githubusercontent.com/scouseralex/nicexcel/master/docs-images/method1_snippet.png)\n\n\n#### A second method: *to_excel_ms()*\n\nIn a similar fashion, the *nicexcel.to_excel_ms()* allows the \npossibility of exporting a group of different *pd.DataFrame* \nobjects to the same output .xlsx files on different worksheets.\n\nThe method takes two main arguments\n   * **dfs**: dict of pd.DataFrame instances\n   * **filename**: string containing file path where the output \n   *.xlsx* file will be saved\n\nSee an example of application below.\n\n```python\nimport nicexcel as nl\nimport pandas as pd\n\n# get sample data\ntx_df = pd.DataFrame(data={\n    'date': ['11/08/2018', '09/04/2018', '30/03/2018'], \n    'customer': ['XYZ Corporation', 'ABC Electric', 'XYZ Corporation'],\n    'price': [113.202, 220.99, 90.1011]})\ncust_df = pd.DataFrame(data={\n    'customer': ['ABC Electric', 'XYZ Corporation'],\n    'city': ['London', 'Tokyo'],\n    'revenue (M)': [100.2334, 76.23216]})\n\n# initiate dict of dataframes\ndfs = {'transactional_db': tx_df, 'customer_db': cust_df}\n\n# generate nicely formatted excel file\nnl.to_excel_ms(\n    dfs=dfs,\n    filename='output_ms')\n``` \n)\n\n### Documentation\nThe documentation is available in docstrings provided within \nthe code.\n\n\n",
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