CoderzColumn : Data Science Tutorials (Page: 2)

Data Science Tutorials

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.

Recent Data Science Tutorials

Tags gantt-charts, matplotlib
Gantt Charts using Matplotlib | Project Roadmap
Data Science

Gantt Charts using Matplotlib | Project Roadmap

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!

Sunny Solanki  Sunny Solanki
Mar 25, 2023 ~15 mins
Tags style-charts, modify-theme, add-theme
Style Matplotlib Charts | Change Theme of Matplotlib Charts
Data Science

Style Matplotlib Charts | Change Theme of Matplotlib Charts

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!

Sunny Solanki  Sunny Solanki
Mar 22, 2023 ~15 mins
Tags evet-plots, matplotlib
Event Plot using Matplotlib
Data Science

Event Plot using Matplotlib

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.

Sunny Solanki  Sunny Solanki
Mar 08, 2023 ~15 mins
Tags bullet-chart, matplotlib
Bullet Charts using Matplotlib
Data Science

Bullet Charts using Matplotlib

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.

Sunny Solanki  Sunny Solanki
Mar 05, 2023 ~15 mins
Tags matplotlib, secondary-y-axis, secondary-x-axis
Matplotlib - Secondary X and Y Axis
Data Science

Matplotlib - Secondary X and Y Axis

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.

Sunny Solanki  Sunny Solanki
Mar 03, 2023 ~20 mins
Tags waterfall-charts, matplotlib
Waterfall Chart using Matplotlib | Python
Data Science

Waterfall Chart using Matplotlib | 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.

Sunny Solanki  Sunny Solanki
Jan 09, 2023 ~20 mins
Tags hvplot, pandas
hvplot - Create Interactive Charts from Pandas DataFrame with One Line Of Code
Data Science

hvplot - Create Interactive Charts from Pandas DataFrame with One Line Of Code

A detailed guide on how to create interactive charts from pandas dataframe using Python library hvplot. Tutorial explains various chart types like scatter charts, bar charts, line charts, histograms, etc. Tutorial also covers how to modify chart properties (labels, title, etc) to improve look and feel of it.

Sunny Solanki  Sunny Solanki
Dec 25, 2022 ~30 mins
Tags matplotlib, animations
Animations using Matplotlib | Python
Data Science

Animations using Matplotlib | Python

A simple guide on how to create animation using Python library matplotlib. Tutorial creates simple examples demonstrating animations like line chart animation, bar chart animation, bubbe chart animations, etc. Tutorial is a good starting point for someone who is new to creating animation using matplotlib.

Sunny Solanki  Sunny Solanki
Dec 10, 2022 ~20 mins
Tags plotnine, grammer-of-graphics, charts
Plotnine: Simple Guide to Create Charts using Grammar of Graphics [Python]
Data Science

Plotnine: Simple Guide to Create Charts using Grammar of Graphics [Python]

A detailed guide to create charts using Python library Plotnine. Plotnine has same API as that of R programming data viz package ggplot2. It is commonly referred to as ggplot of Python. Plotnine is based on concept of grammar of graphics. Tutorial covers many different chart types like scatter charts, bar charts, line charts, heatmaps, etc.

Sunny Solanki  Sunny Solanki
Dec 05, 2022 ~30 mins
Tags datascience, datavisulisation, matplotlib
Matplotlib.Pyplot - Complete Guide to Create Charts in Python
Data Science

Matplotlib.Pyplot - Complete Guide to Create Charts in Python

A detailed guide on how to create data visualizations using Python library matplotlib. Tutorial covers chart creation using pyplot API of matplotlib. Tutorial explains many different chart types like scatter charts, bar charts, line charts, heatmaps, box plots, etc. Tutorial also covers how to change themes to improve look and feel of matplotlib charts.

Sunny Solanki  Sunny Solanki
Dec 01, 2022 ~10 mins

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Most Popular Data Science Tutorials

1. Candlestick Chart in Python (mplfinance, plotly, bokeh, bqplot & cufflinks)
2. How to Plot Sankey Diagram in Python Jupyter Notebook [holoviews & plotly]?
3. Time Series - How to Remove Trend & Seasonality from Time-Series Data
4. Basic Dashboard using Streamlit and Matplotlib
5. cufflinks - How to create plotly charts from pandas dataframe with one line of code?

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Data Science (84)
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Machine Learning (38)
Python (130)

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Python Data Visualization Libraries

Python Data Visualization Libraries

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.

matplotlib (Static Charts)

matplotlib (Static Charts)
bokeh (Interactive Charts)

bokeh (Interactive Charts)
bqplot (Interactive Charts)

bqplot (Interactive Charts)
plotnine (Static Charts)

plotnine (Static Charts)
altair (Interactive Charts)

altair (Interactive Charts)
seaborn (Static Charts)

seaborn (Static Charts)
holoviews (Static & Interactive Charts)

holoviews (Static & Interactive Charts)
cufflinks (Interactive Charts)

cufflinks (Interactive Charts)
hvplot (Interactive Charts)

hvplot (Interactive Charts)
Pandas-Bokeh (Interactive Charts)

Pandas-Bokeh (Interactive Charts)
Advanced Data Visualizations using Python

Advanced Data Visualizations using Python

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.

Candlestick Charts

Candlestick Charts
Sankey Diagrams

Sankey Diagrams
Sunburst Charts

Sunburst Charts
Chord Diagrams

Chord Diagrams
Network Charts

Network Charts
Parallel Coordinates Charts

Parallel Coordinates Charts
Radar Charts

Radar Charts
Treemap Charts

Treemap Charts
Connection Maps

Connection Maps
Choropleth Maps

Choropleth Maps
Scatter & Bubble Maps

Scatter & Bubble Maps
Dashboards using Python

Dashboards using Python

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.

Dashboard using Dash & Plotly

Dashboard using Dash & Plotly
Dashboard using Panel & Matplotlib

Dashboard using Panel & Matplotlib
Dashboard using Bokeh

Dashboard using Bokeh
Dashboard using Streamlit & Cufflinks

Dashboard using Streamlit & Cufflinks
Dashboard with Widgets using Dash & Plotly

Dashboard with Widgets using Dash & Plotly
Dashboard using Streamlit & Matplotlib

Dashboard using Streamlit & Matplotlib
Work with Time Series Data in Python

Work with Time Series Data in Python

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.

Dates, Timestamps, Timedeltas, Periods & Time Zones using Pandas

Dates, Timestamps, Timedeltas, Periods & Time Zones using Pandas
Resampling & Moving Window Functions using Pandas

Resampling & Moving Window Functions using Pandas
Remove Trend & Seasonality to Make Time-Series Stationary

Remove Trend & Seasonality to Make Time-Series Stationary
Visualize Maps using Python

Visualize Maps using Python

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.

Geopandas

Geopandas
Folium

Folium
ipyleaflet

ipyleaflet
Cartopy

Cartopy
Geoviews

Geoviews
Geoplot

Geoplot
Exploratory Data Analysis using Python

Exploratory Data Analysis using Python

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.

Sweetviz: Automate EDA

Sweetviz: Automate EDA
missingno: Analyze Missing Data

missingno: Analyze Missing Data
Seaborn: Statistical Visualization to Analyze Relationships

Seaborn: Statistical Visualization to Analyze Relationships
Pivot Tables using Pandas

Pivot Tables using Pandas