Title: Building a Fun and Smart Home with a DIY Smart Doorbell Introduction: In the exciting world of electronics, there's nothing quite as thrilling as creating your very own smart home gadgets. Today, we'll embark on a journey to build a Smart Doorbell using simple and easily accessible equipment like the Arduino Uno, an IR proximity sensor, and a buzzer. This DIY project is not only a fantastic introduction to electronics but also a step towards transforming your home into a tech-savvy haven. So, gear up, young inventors, as we venture into the world of smart homes! Materials You'll Need: 1. Arduino Uno: The brain of our smart doorbell, capable of processing information and controlling the connected devices. 2. IR Proximity Sensor: A nifty gadget that detects the presence of objects in its vicinity. 3. Buzzer: This component will be the voice of our smart doorbell, alerting you when someone approaches. Step 1: Setting Up the Arduino Uno: Begin by connecting your Arduin...
Seaborn is a Python data visualization library built on the matplotlib library. (i.e., it runs matplotlib at the backend and so accepts some keyword argument used in matplotlib).
seaborn is a dataset-oriented visualization library. Its plotting functions operate on data frames and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.
The Seaborn library aims at creating informative plots with fewer lines of code.
It also has the following features.
* Aesthetic styling and colour palette
* It is dataset oriented, i.e., works well for matrices, data frames, etc.
* It functions well with pandas and NumPy data structures
* Create statistical plots with fewer lines of code.
Getting Started
Install the Seaborn library into your Python environment. make sure you have matplotlib installed also since the seaborn library runs off the matplotlib library.
`pip install seaborn
#This installs the latest version of the Seaborn library
Seaborn Plots
The seaborn library is used to visualize data, it is also used to create a variety of plots and visualize data in different formats (data frame, matrices, NumPy array, etc).
The focus of this article is to discuss the different kinds of plots you can visualize with Seaborn and why and when to use a particular plot type.
Seaborn can be used to create the following plots.
* Scatter plots
*Distribution plots
*Categorical plots
*Comparison plots
*Seaborn Grids
*Matrix plots
Scatter plots with Seaborn
Scatter plots are plots that show the relationship between two continuous features. (i.e., say Two features Age and Salary in a dataset).
Scatter Plots
A Scatter plot is a plot that shows the relationship between two continuous features (X and Y). The scatter plot of two…
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#import the library with an alias
import seaborn as sns
import pandas as pd
data = {"acceleration":[23,34,45,23,56,34,54,34,56],
"mpg": [12,22,33,11,37,23,39,21,38] }
#create a data frame from the dictionary
df = pd.DataFrame(data)
#plot a scatterplot using seaborn scatterplot()
#x- the values/column to be plotted on the x-axis
#y- the values/column to be plotted on the y-axis
#data - the data frame of reference
sns.scatterplot(x="acceleration", y="mpg", data=df )
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