CoderzColumn : Data Science Tutorials (Page: 3)

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

Add Annotations to Matplotlib Charts

Add Annotations to Matplotlib Charts

Sunny Solanki  Sunny Solanki
Dec 01, 2022 ~25 mins
bqplot - Interactive Charts in Python Jupyter Notebook

bqplot - Interactive Charts in Python Jupyter Notebook

Sunny Solanki  Sunny Solanki
Nov 22, 2022 ~30 mins
Bokeh - Basic Interactive Plotting in Python [Jupyter Notebook]

Bokeh - Basic Interactive Plotting in Python [Jupyter Notebook]

Sunny Solanki  Sunny Solanki
Nov 15, 2022 ~30 mins
Getting Started with Holoviews - Basic Interactive Plotting [Python]

Getting Started with Holoviews - Basic Interactive Plotting [Python]

Sunny Solanki  Sunny Solanki
Nov 12, 2022 ~45 mins
Simple Guide to Create Animation using Bokeh

Simple Guide to Create Animation using Bokeh

Sunny Solanki  Sunny Solanki
Nov 04, 2022 ~30 mins
Simple Guide to use Bokeh Widgets (Interactive GUI / Apps)

Simple Guide to use Bokeh Widgets (Interactive GUI / Apps)

Sunny Solanki  Sunny Solanki
Nov 02, 2022 ~25 mins
Add Annotations to Plotnine Charts

Add Annotations to Plotnine Charts

Sunny Solanki  Sunny Solanki
Oct 14, 2022 ~25 mins
Pandas-Bokeh: Create Bokeh Charts from Pandas DataFrame with One Line of Code

Pandas-Bokeh: Create Bokeh Charts from Pandas DataFrame with One Line of Code

Sunny Solanki  Sunny Solanki
Oct 10, 2022 ~30 mins
How to Add Annotations to Bokeh Charts?

How to Add Annotations to Bokeh Charts?

Sunny Solanki  Sunny Solanki
Oct 07, 2022 ~25 mins
How to Link Bokeh Charts with IPywidgets widgets to Dynamically Update Charts?

How to Link Bokeh Charts with IPywidgets widgets to Dynamically Update Charts?

Sunny Solanki  Sunny Solanki
Oct 03, 2022 ~30 mins

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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