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:

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:

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:

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:

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:

import datanerd as dn

server = "your_server_name"
message = "This is a test notification from Python!"

dn.ntfy(server, message)