A Beginner's Guide to Data Visualization with Python

Introduction:

                          Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Data Visualization in Python:

                             Data visualization refers to representing information in the form of visuals. Data Visualization can make your data speak! There is no doubt that when information is represented in the form of a picture like a graph or a chart, it can provide a much better understanding of the Data meaningful and effective.

·        The plots and graphs can provide a clear description of the data.

·        The Visuals can help support any claims you make based on the Data at hand.

Data Visualization offers:

·       Efficiency

·       Clarity

·       Accuracy

Data Visualization Tools:

·       Pandas Visualization

·       Matplotlib

·       Seaborn

·       Plotly

Pandas visualization:

                       Pandas library in python is mainly used for data analysis. It is not a data visualization library but, we can create basic plots using Pandas. Pandas is highly useful and practical if we want to create exploratory data analysis plots.

Matplotlib:

                      Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Create publication quality plots. Make interactive figures that can zoom, pan, update.

Seaborn:

                     Seaborn is a library for making statistical graphics in Python. It builds on top of matplotlib and integrates closely with pandas data structures. Seaborn helps you explore and understand your data. Seaborn is a library that uses Matplotlib underneath to plot graphs. It will be used to visualize random distributions.

Plotly:

                    The plotly Python library is an interactive, open-source plotting library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases.

Some common plots for visualizations:

·       Scatter plot

·       Line Chart

·       Histogram

·       Bar Chart

·       Box plot

·       Heatmap

·       Pair plot

Scatter Plot:

                      A scatter plot (or ‘scatterplot’) is generally used to summarize the relationship between two paired data samples. Paired data samples mean that two measures were recorded for a given observation, such as the weight and height of a person.
                      

Line Chart:

                      A line plot is generally used to present observations collected at regular intervals.

Histogram:

                      A histogram plot is generally used to summarize the distribution of a data sample.

                      The x-axis represents discrete bins or intervals for the observations.The y-axis represents the frequency or count of the number of observations in the dataset that belong to each bin

Bar Chart:

                      A bar chart is generally used to present relative quantities for multiple categories.
                      The x-axis represents the categories and are spaced evenly. The y-axis represents the quantity for each category and is drawn as a bar from the baseline to the appropriate level on the y-axis.

Box Plot:

                   A box and whisker plot, or boxplot for short, is generally used to summarize the distribution of a data sample.

Heatmap:

                    A Heatmap is a graphical representation of data where the individual values contained in a matrix are represented as colors. Heatmaps are perfect for exploring the correlation of features in a dataset.

Pair Plot:

                               It enables us to plot a grid of pairwise relationships in a dataset.