Data science is an interdisciplinary field that applies information from data across a wide range of application fields by using scientific methods, procedures, algorithms, and systems to infer knowledge and insights from noisy, structured, and unstructured data.
Data visualization libraries like matplotlib, Bokeh, bqplot, Plotnine, cufflinks, Altair, hvplot, Holoviews, seaborn and more.
Different Interactive charts Sunburst Charts, Sankey Diagrams (Alluvial), Candlestick Charts, Network Charts, Chord Diagram, Parallel Coordinates Plots, Radar Charts, Connection Map, Treemap, Choropleth Maps, Scatter & Bubble Maps.
Apart from this, you will find tutorials about time series data and its applications, creating dashboards, and other concepts.
For an in-depth understanding of the above concepts, check out the sections below.
A simple guide on how to create a Venn diagram using Python data visualization library Matplotlib. We explain how to create a data science Venn diagram using Matplotlib.
A simple guide on how to watermark matplotlib charts. The tutorial explains how to add image and text watermarks to the matplotlib chart with simple examples.
Discover how to create Hexbin Charts using Matplotlib, a popular Python library for data visualization. This tutorial will guide you through the process of creating stunning visualizations that allow you to explore and analyze your data in a new and exciting way.
Learn how to create visually appealing Gauge Charts using the powerful Python data visualization library, Matplotlib. Follow this step-by-step tutorial and add an extra layer of insight to your data.
Need to create a project roadmap or timeline? This article will show you how to use Matplotlib to create Gantt charts in Python. Whether you're a project manager or a data scientist, you'll learn how to visualize project schedules and track progress using this powerful tool. Follow our step-by-step guide and create professional-looking Gantt charts for your next project. Don't miss out on this comprehensive tutorial!
Looking to give your Matplotlib charts a unique look? In this article, we'll show you how to style your charts using Matplotlib's built-in themes or by creating your own custom themes. Whether you're a beginner or an experienced data scientist, you'll learn how to create visually appealing charts that stand out. Don't miss out on this guide to changing the theme of your Matplotlib charts!
In this tutorial, we have focused on how to create event plots using "Matplotlib". We have covered the "eventplot()" function available for creating event plots, and how to customize their properties, such as markers, colors, and labels. We have also demonstrated how to create stacked event plots, which can show multiple events happening at the same time.
As a part of this tutorial, we will explore how to create bullet charts using Python data viz library "Matplotlib" and see some examples of how they can be used to represent different types of data. First, we have created a simple bullet chart and then added a theme to improve its look further.
In this tutorial, we have explained how to create secondary axes (Secondary X axis and Secondary Y axis) in "Matplotlib" and demonstrated how they can be used to enhance your visualizations. Additionally, we have discussed some common use cases where secondary axes can be useful and demonstrate how to implement them in Python.
A simple guide on how to create a waterfall chart using Python Data Viz library matplotlib.Tutorial covers a guide on how to improve look & feel of waterfall chart as well. It even covers different ways to lay out bars of waterfall chart.
Data Visualization is a field of graphical representation of information / data. It is one of the most efficient ways of communicating information with users as humans are quite good at capturing patterns in data.
Python has a bunch of libraries that can help us create data visualizations. Some of these libraries (matplotlib, seaborn, plotnine, etc) generate static charts whereas others (bokeh, plotly, bqplot, altair, holoviews, cufflinks, hvplot, etc) generate interactive charts. Majority of basic visualizations like bar charts, line charts, scatter plots, histograms, box plots, pie charts, etc are supported by all libraries. Many libraries also support advanced visualization, widgets, and dashboards.
Basic Data Visualizations like bar charts, line charts, scatter plots, histograms, box plots, pie charts, etc are quite good at representing information and exploring relationships between data variables.
But sometimes these visualizations are not enough and we need to analyze data from different perspectives. For this purpose, many advanced visualizations are developed over time like Sankey diagrams, candlestick charts, network charts, chord diagrams, sunburst charts, radar charts, parallel coordinates charts, etc. Python has many data visualization libraries that let us create such advanced data visualizations.
Dashboards are literally everywhere and everyone is using them. Dashboards are GUI with various visualizations and metrics that can be used to monitor key performance indicators. Dashboards have a very wide range of applications in all fields.
Python has a bunch of libraries (dash, panel, streamlit, bokeh, etc) that let us create dashboards using them. They let us include widgets and interactive data visualizations in dashboards.
Time series is a type of data where data points are recorded in time order or at specified time intervals. Many real-world datasets like stock prices, weather indicators, heights of ocean tides, retail sales, etc.
Time series analysis involves various tasks like resampling time series, trying moving window functions, forecasting, classification, etc.
Python has various libraries (pandas, statsmodels, etc.) that let us load and work with time series data efficiently. They even provide useful functionalities to work with time series data.
Maps are one of the best ways to display and analyze geospatial data. It helps us better see patterns and trends geographically. This can help us with better decision-making.
Many different types of maps have been developed over time to analyze data from different perspectives. Some common map visualization types are choropleth maps, scatter maps, bubble maps, connection maps, etc. Apart from these, we can also include pins on maps to identify locations.
Python has many different libraries (geopandas, folium, ipyleaflet, cartopy, geoviews, geoplot, bokeh, altair, plotly, hvplot, etc) that let us create static as well as interactive maps.
Exploratory data analysis (commonly referred to as EDA) is an initial analysis of data to look for various relationships, anomalies, missing values, distributions, basic statistics, etc. It helps us understand data better to make further decisions. Various stats are calculated and statistical visualizations are created during EDA to understand data.
Python provides many different tools / libraries (Sweetviz, missingno, seaborn, pandas, etc) for performing EDA. It's quite common to use more than one of these tools to perform EDA.