Plotly library Tips exploratory data analysis

Name: Plotly library Tips exploratory data analysis
Rating: 4.5 (19 reviews)

Get a kick start on exploratory data analysis using plotly library

Created byShambhavi Gupta

Last updated 5/2022

English

English [Auto],

What you'll learn

Exploratory data analysis using plotly library
Making different plots like Barplot, Scatterplot etc.
Visualizing data through graphs
Using different functions to make the graph look more accurate
Applying conditions to the dataset

Course content

1 section • 11 lectures • 2h 11m total length

IDE's online and offline10:33
Explore online and offline Python IDEs, including Google Cloud notebooks, PyCharm, and Visual Studio Code, with guidance on Python installation and setting up tools for data analysis and machine learning.
Bar plot12:23
Learn to create bar plots with the Plotly library for exploratory data analysis, loading a dataset, inspecting columns, and mapping x, y, and color to reveal patterns.
Bar plot using query function14:15
Create a bar plot in Plotly by filtering data with a query function and applying conditions on total bill and date, including Sunday.
Scatter and Bubble plot11:31
Discover scatter and bubble plots in Plotly for exploratory data analysis, using bubble size for family size and color to show days of the week.
Pie chart11:48
Learn how to build a pie chart with plotly, loading data, selecting category labels and values, customizing colors, and interpreting percentage shares by day and category.
line plot14:54
Explore line plots with the Plotly library for exploratory data analysis, using gapminder data, filtering by continent, and applying conditional queries to visualize life expectancy and GDP per capita.
Scatter plot11:49
Learn to convert a data set into horizontal bar charts using Plotly, compare vertical vs horizontal orientation, customize colors, and interpret charted columns for exploratory data analysis.
Horizontal bar plot11:49
Learn how to create horizontal bar plots in Plotly, compare horizontal vs vertical orientation, and customize data, colors, and labels for exploratory data analysis.
Box plot10:50
Explore box plots revealing the five values: min, Q1, median, Q3, and max, and identifying outliers, with notes on notches and Plotly-based visualization.
Histogram11:45
Master histogram and violin plots in Plotly for exploratory data analysis, using margins, color coding by days, and selecting data columns to reveal key distribution insights.
Scatter_Matrix10:08
Learn to create a Plotly scatter matrix for exploratory data analysis by selecting three or four numeric columns and coloring points by a categorical variable.

Requirements

Basic knowledge of python programming language

Description

Welcome to the Plotly library Tips exploratory data analysis course! An excellent choice for beginners and professionals looking to expand their knowledge on one of the most popular Python libraries in the world i.e plotly library. This course includes case study for drawing meaningful insights out of given data.

Plotly library Tips exploratory data analysis course offers video tutorials on the most powerful data analysis toolkit available today.

Why learn Data Analysis and Insights Visualization using Python?

If you've spent time in a spreadsheet software like Microsoft Excel, Google Sheets or any form of tabular data such as database tables, delimited files or csv files and are eager to take your data analysis skills to the next level using python, this course is for you!

Plotly Python Open Source Graphing Library

Plotly's Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.

The Plotly Python library is an interactive open-source library. This can be a very helpful tool for data visualization and understanding the data simply and easily. plotly graph objects are a high-level interface to plotly which are easy to use. It can plot various types of graphs and charts like scatter plots, line charts, bar charts, box plots, histograms, pie charts, etc.

So you all must be wondering why plotly over other visualization tools or libraries? Here’s the answer –

Plotly has hover tool capabilities that allow us to detect any outliers or anomalies in a large number of data points.
It is visually attractive that can be accepted by a wide range of audiences.
It allows us for the endless customization of our graphs that makes our plot more meaningful and understandable for others.

So in this course you will get to learn visualization using the plotly library

Who this course is for:

Data Science enthusiasts interested in learning exploratory data analysis using library