| Name | data-profiler-kit JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | A library to provide quick and insightful data profiling for pandas DataFrames |
| upload_time | 2024-11-22 15:00:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2024 Najm Eddine Charaf Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
data
profiling
pandas
analytics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# DataProfilerKit
A Python library that provides quick and insightful data profiling for pandas DataFrames. It generates detailed reports including missing values analysis, data type information, correlations, outliers, and column statistics in a clear, organized format.
## Installation
```bash
pip install data-profiler-kit
```
## Usage
```python
from dataprofilerkit import DataProfiler
import pandas as pd
# Create or load your DataFrame
df = pd.read_csv('your_data.csv')
# Create a DataProfiler instance
profiler = DataProfiler(df)
# Generate the profile
profile = profiler.generate_profile()
# Access different aspects of the profile
print("Basic Information:")
print(profile['basic_info'])
print("\nMissing Values Analysis:")
print(profile['missing_values'])
print("\nColumn Statistics:")
print(profile['column_stats'])
print("\nDuplicates Analysis:")
print(profile['duplicates'])
print("\nOutliers Analysis:")
print(profile['outliers'])
```
## Core Functionality
- ### Basic DataFrame Information:
- Number of rows, columns, and total cells.
- Memory usage of the DataFrame.
- Data types and their counts.
- ### Missing Value Analysis:
- Total missing values across the DataFrame.
- Missing values by column.
- Percentage of missing values for each column.
- ### Column-wise Analysis:
- #### Numeric Columns:
- Descriptive statistics (mean, median, standard deviation, etc.).
- Skewness and kurtosis.
- #### Categorical Columns:
- Count of unique values.
- Top 5 most frequent values with their percentages.
- #### Datetime Columns:
- Minimum and maximum values.
- Range in days.
- ### Duplicate Detection:
- Duplicate rows (count and percentage).
- Duplicate columns (count and list of column names).
- ### Outlier Detection:
- For numeric columns, detects outliers using:
- Z-score method (with indices and percentages).
- Interquartile Range (IQR) method (with indices and percentages).
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License - see the LICENSE file for details.
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"description": "# DataProfilerKit\r\n\r\nA Python library that provides quick and insightful data profiling for pandas DataFrames. It generates detailed reports including missing values analysis, data type information, correlations, outliers, and column statistics in a clear, organized format.\r\n\r\n## Installation\r\n\r\n```bash\r\npip install data-profiler-kit\r\n```\r\n\r\n## Usage\r\n\r\n```python\r\nfrom dataprofilerkit import DataProfiler\r\nimport pandas as pd\r\n\r\n# Create or load your DataFrame\r\ndf = pd.read_csv('your_data.csv')\r\n\r\n# Create a DataProfiler instance\r\nprofiler = DataProfiler(df)\r\n\r\n# Generate the profile\r\nprofile = profiler.generate_profile()\r\n\r\n# Access different aspects of the profile\r\nprint(\"Basic Information:\")\r\nprint(profile['basic_info'])\r\n\r\nprint(\"\\nMissing Values Analysis:\")\r\nprint(profile['missing_values'])\r\n\r\nprint(\"\\nColumn Statistics:\")\r\nprint(profile['column_stats'])\r\n\r\nprint(\"\\nDuplicates Analysis:\")\r\nprint(profile['duplicates'])\r\n\r\nprint(\"\\nOutliers Analysis:\")\r\nprint(profile['outliers'])\r\n```\r\n\r\n## Core Functionality\r\n\r\n- ### Basic DataFrame Information:\r\n - Number of rows, columns, and total cells.\r\n - Memory usage of the DataFrame.\r\n - Data types and their counts.\r\n\r\n- ### Missing Value Analysis:\r\n - Total missing values across the DataFrame.\r\n - Missing values by column.\r\n - Percentage of missing values for each column.\r\n\r\n- ### Column-wise Analysis:\r\n\r\n - #### Numeric Columns:\r\n - Descriptive statistics (mean, median, standard deviation, etc.).\r\n - Skewness and kurtosis.\r\n\r\n - #### Categorical Columns:\r\n - Count of unique values.\r\n - Top 5 most frequent values with their percentages.\r\n\r\n - #### Datetime Columns:\r\n - Minimum and maximum values.\r\n - Range in days.\r\n\r\n- ### Duplicate Detection:\r\n - Duplicate rows (count and percentage).\r\n - Duplicate columns (count and list of column names).\r\n\r\n- ### Outlier Detection:\r\n - For numeric columns, detects outliers using:\r\n - Z-score method (with indices and percentages).\r\n - Interquartile Range (IQR) method (with indices and percentages).\r\n\r\n## Contributing\r\n\r\nContributions are welcome! Please feel free to submit a Pull Request.\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License - see the LICENSE file for details.\r\n",
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