# DataNerd
This package provides various functions for data analysis, statistical calculations, database operations, and sending notifications.
## Installation
To use these functions, you need to have Python installed on your system. You also need to install the required libraries. You can install them using pip:
```
pip install pandas numpy sqlalchemy requests
```
## Functions
### 1. stats()
This function provides statistical summary of a given dataframe.
#### Parameters:
- `df` (pandas.DataFrame): The input dataframe
#### Returns:
- A dataframe containing various statistics for each column
#### Statistics provided:
- count
- mean
- std
- min
- 10th, 20th, 25th, 30th, 40th, 50th (median), 60th, 70th, 75th, 80th, 90th, 95th, 99th percentiles
- max
- % of missing values
- number of unique values
#### Usage:
```python
import pandas as pd
import datanerd as dn
df = pd.read_csv('titanic.csv')
summary_stats = dn.stats(df)
```
### 2. iv_woe()
This function calculates the Weight of Evidence (WoE) and Information Value (IV) for a given dataframe.
#### Parameters:
- `data` (pandas.DataFrame): The input dataframe
- `target` (str): The name of the target variable
- `bins` (int): The number of bins to use for discretizing continuous variables
- `optimize` (bool): Whether to optimize the binning of continuous variables
- `threshold` (float): The minimum percentage of non-events in each bin for optimization
#### Returns:
- A tuple containing two dataframes: (iv, woe)
#### Usage:
```python
import pandas as pd
import datanerd as dn
df = pd.read_csv('cancer.csv')
iv, woe = dn.iv_woe(data=df, target='Diagnosis', bins=20, optimize=True, threshold=0.05)
```
### 3. pushdb()
This function pushes a Pandas dataframe to a Microsoft SQL Server database.
#### Parameters:
- `data` (pandas.DataFrame): The dataframe to be pushed
- `tablename` (str): The name of the table in the database
- `server` (str): The name of the SQL Server
- `database` (str): The name of the database
- `schema` (str): The name of the schema
#### Usage:
```python
import pandas as pd
import datanerd as dn
df = pd.read_csv('day.csv')
dn.pushdb(df, tablename='day', server='SQL', database='schedule', schema='analysis')
```
### 4. teams_webhook()
This function sends a formatted message to a Microsoft Teams channel using a webhook URL.
#### Parameters:
- `webhook_url` (str): The webhook URL for the Teams channel
- `title` (str): The title of the message
- `message` (str): The body of the message
#### Usage:
```python
import datanerd as dn
webhook_url = "https://outlook.office.com/webhook/..."
title = "Important Notification"
message = "This is a test message sent from Python!"
dn.teams_webhook(webhook_url, title, message)
```
### 5. ntfy()
This function sends a notification message to an ntfy.sh server.
#### Parameters:
- `server` (str): The name of the ntfy.sh server/topic to send the message to
- `message` (str): The message to be sent
#### Usage:
```python
import datanerd as dn
server = "your_server_name"
message = "This is a test notification from Python!"
dn.ntfy(server, message)
```
Raw data
{
"_id": null,
"home_page": null,
"name": "datanerd",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "python, describe, stats, unique values, information value, woe, iv",
"author": "Sunil Aleti",
"author_email": "iam@sunilaleti.dev",
"download_url": "https://files.pythonhosted.org/packages/4d/f1/e693c5893724d9cedd5b2a94f172d5fdea4bf3b4e60d0e404efb1debcdb3/datanerd-1.1.tar.gz",
"platform": null,
"description": "\n# DataNerd\n\nThis package provides various functions for data analysis, statistical calculations, database operations, and sending notifications.\n\n## Installation\n\nTo use these functions, you need to have Python installed on your system. You also need to install the required libraries. You can install them using pip:\n\n```\npip install pandas numpy sqlalchemy requests\n```\n\n## Functions\n\n### 1. stats()\n\nThis function provides statistical summary of a given dataframe.\n\n#### Parameters:\n- `df` (pandas.DataFrame): The input dataframe\n\n#### Returns:\n- A dataframe containing various statistics for each column\n\n#### Statistics provided:\n- count\n- mean\n- std\n- min\n- 10th, 20th, 25th, 30th, 40th, 50th (median), 60th, 70th, 75th, 80th, 90th, 95th, 99th percentiles\n- max\n- % of missing values\n- number of unique values\n\n#### Usage:\n\n```python\nimport pandas as pd\nimport datanerd as dn\n\ndf = pd.read_csv('titanic.csv')\nsummary_stats = dn.stats(df)\n```\n\n### 2. iv_woe()\n\nThis function calculates the Weight of Evidence (WoE) and Information Value (IV) for a given dataframe.\n\n#### Parameters:\n- `data` (pandas.DataFrame): The input dataframe\n- `target` (str): The name of the target variable\n- `bins` (int): The number of bins to use for discretizing continuous variables\n- `optimize` (bool): Whether to optimize the binning of continuous variables\n- `threshold` (float): The minimum percentage of non-events in each bin for optimization\n\n#### Returns:\n- A tuple containing two dataframes: (iv, woe)\n\n#### Usage:\n\n```python\nimport pandas as pd\nimport datanerd as dn\n\ndf = pd.read_csv('cancer.csv')\niv, woe = dn.iv_woe(data=df, target='Diagnosis', bins=20, optimize=True, threshold=0.05)\n```\n\n### 3. pushdb()\n\nThis function pushes a Pandas dataframe to a Microsoft SQL Server database.\n\n#### Parameters:\n- `data` (pandas.DataFrame): The dataframe to be pushed\n- `tablename` (str): The name of the table in the database\n- `server` (str): The name of the SQL Server\n- `database` (str): The name of the database\n- `schema` (str): The name of the schema\n\n#### Usage:\n\n```python\nimport pandas as pd\nimport datanerd as dn\n\ndf = pd.read_csv('day.csv')\ndn.pushdb(df, tablename='day', server='SQL', database='schedule', schema='analysis')\n```\n\n### 4. teams_webhook()\n\nThis function sends a formatted message to a Microsoft Teams channel using a webhook URL.\n\n#### Parameters:\n- `webhook_url` (str): The webhook URL for the Teams channel\n- `title` (str): The title of the message\n- `message` (str): The body of the message\n\n#### Usage:\n\n```python\nimport datanerd as dn\n\nwebhook_url = \"https://outlook.office.com/webhook/...\"\ntitle = \"Important Notification\"\nmessage = \"This is a test message sent from Python!\"\n\ndn.teams_webhook(webhook_url, title, message)\n```\n\n### 5. ntfy()\n\nThis function sends a notification message to an ntfy.sh server.\n\n#### Parameters:\n- `server` (str): The name of the ntfy.sh server/topic to send the message to\n- `message` (str): The message to be sent\n\n\n#### Usage:\n\n```python\nimport datanerd as dn\n\nserver = \"your_server_name\"\nmessage = \"This is a test notification from Python!\"\n\ndn.ntfy(server, message)\n```\n\n\n\n\n\n",
"bugtrack_url": null,
"license": null,
"summary": "Contains multiple functions stats(), iv_woe(), pushdb(), teams_webhook(), and ntfy()",
"version": "1.1",
"project_urls": null,
"split_keywords": [
"python",
" describe",
" stats",
" unique values",
" information value",
" woe",
" iv"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "b8e415f1f541b5e970cfce3c6dc6f388b4835eb6fa4a188ce10a1b52cb25281c",
"md5": "7328ce9561cef61ebcdc8ac0349f6a3f",
"sha256": "17bb79b0af4cf79d829631a73b9ccd92ce4490f40fbee0e100b4e01cbd30df73"
},
"downloads": -1,
"filename": "datanerd-1.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "7328ce9561cef61ebcdc8ac0349f6a3f",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 6329,
"upload_time": "2024-07-13T23:05:24",
"upload_time_iso_8601": "2024-07-13T23:05:24.851602Z",
"url": "https://files.pythonhosted.org/packages/b8/e4/15f1f541b5e970cfce3c6dc6f388b4835eb6fa4a188ce10a1b52cb25281c/datanerd-1.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4df1e693c5893724d9cedd5b2a94f172d5fdea4bf3b4e60d0e404efb1debcdb3",
"md5": "6bc7e10f5d71319f4ad5edcf3af00f1a",
"sha256": "74ddaca4ec6136001615c081c653762afadcd6f6e9cd0beb877be2ad88c9d873"
},
"downloads": -1,
"filename": "datanerd-1.1.tar.gz",
"has_sig": false,
"md5_digest": "6bc7e10f5d71319f4ad5edcf3af00f1a",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 5743,
"upload_time": "2024-07-13T23:05:26",
"upload_time_iso_8601": "2024-07-13T23:05:26.229214Z",
"url": "https://files.pythonhosted.org/packages/4d/f1/e693c5893724d9cedd5b2a94f172d5fdea4bf3b4e60d0e404efb1debcdb3/datanerd-1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-07-13 23:05:26",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "datanerd"
}
JSON