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

Gantt Charts using Matplotlib | Project Roadmap

Gantt Charts using Matplotlib | Project Roadmap

Mar 25, 2023 ~15 mins

Style Matplotlib Charts | Change Theme of Matplotlib Charts

Style Matplotlib Charts | Change Theme of Matplotlib Charts

Mar 22, 2023 ~15 mins

Event Plot using Matplotlib

Event Plot using Matplotlib

Mar 08, 2023 ~15 mins

Bullet Charts using Matplotlib

Bullet Charts using Matplotlib

Mar 05, 2023 ~15 mins

Matplotlib - Secondary X and Y Axis

Matplotlib - Secondary X and Y Axis

Mar 03, 2023 ~20 mins

Waterfall Chart using Matplotlib | Python

Waterfall Chart using Matplotlib | Python

Jan 09, 2023 ~20 mins

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

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

Dec 25, 2022 ~30 mins

Animations using Matplotlib | Python

Animations using Matplotlib | Python

Dec 10, 2022 ~20 mins

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

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

Dec 05, 2022 ~30 mins

Matplotlib.Pyplot - Complete Guide to Create Charts in Python

Matplotlib.Pyplot - Complete Guide to Create Charts in Python

Dec 01, 2022 ~10 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

Network Charts

Parallel Coordinates Charts

Parallel Coordinates Charts

Radar 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

Folium

ipyleaflet

Cartopy

Geoviews

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

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