# 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)
```
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"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",
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