{"id":436,"date":"2023-06-08T02:30:56","date_gmt":"2023-06-07T21:30:56","guid":{"rendered":"https:\/\/immadshahid.com\/?p=436"},"modified":"2023-06-08T02:40:51","modified_gmt":"2023-06-07T21:40:51","slug":"data-science-lecture-2-data-visualization","status":"publish","type":"post","link":"https:\/\/immadshahid.com\/blog\/data-science-lecture-2-data-visualization\/","title":{"rendered":"Data Science Lecture 2: Data Visualization"},"content":{"rendered":"\n

The study of how to visually portray data is known as data visualisation. It effectively communicates findings from data by visually displaying the data.<\/p>\n\n\n\n

We may obtain a visual overview of our data via data visualisation. The human mind processes and comprehends any given data more easily when it is presented with images, maps, and graphs. Both small and big data sets benefit from data visualisation, but enormous data sets are where it really shines because it is difficult to manually view, let alone analyse, and comprehend, all of our data.<\/p>\n\n\n\n

Python has a number of charting libraries, including Matplotlib, Seaborn, and many additional data visualisation tools with a variety of capabilities for building educational, unique, and visually appealing charts to show data in the simplest and most powerful manner.<\/p>\n\n\n\n

The first step in this is to import the modules, and then after that start working on it.<\/p>\n\n\n\n

Importing the modules:<\/p>\n\n\n\n

import matplotlib.pyplot as plt<\/code> #For Matplotlib<\/p>\n\n\n\n

import seaborn as sns<\/code> #For Seaborn<\/p>\n\n\n\n

Matplotlib vs Seaborn<\/h2>\n\n\n\n
Matplotlib<\/strong><\/td>Seaborn<\/strong><\/td><\/tr>
used for basic graph like line graphs, bar charts, etc<\/td>used to visualise statistics and is capable of completing more sophisticated visualisations with fewer instructions.<\/td><\/tr>
mainly used by datasets and arrays<\/td>works in entire datasets<\/td><\/tr>
acts productively with datasets and arrays, and considers aces and figures as objects<\/td>more organized and functional, treats the entire dataset as a solitary unit<\/td><\/tr>
For exploratory data analysis, Matplotlib is more flexible and works well with Pandas and Numpy.<\/td>more inbuilt themes and mainly used for statistical analysis<\/td><\/tr><\/tbody><\/table>
Table: Matplotlib vs Seaborn<\/em><\/figcaption><\/figure>\n\n\n\n

Line Chart in Matplotlib<\/h2>\n\n\n\n

An informational graph called a line chart shows data as a collection of dots connected by straight lines. Each marker or data point in a line chart is drawn and linked by a line or curve.<\/p>\n\n\n\n

The first step to start is by importing the modules, as told above. <\/p>\n\n\n

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For better understanding, we can add an x-axis too, in this case for years.<\/p>\n\n\n

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Now we’ll move forward to name the axis.<\/p>\n\n\n

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We can also plot multiple datasets on a single axis.<\/p>\n\n\n

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With the help of the marker parameter, we can use markers to display each data point on our graph. Matplotlib offers a wide variety of marker shapes, including a circle, cross, square, diamond, etc. We can also show the legend, to make the graph more clear. <\/p>\n\n\n

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We can also change the size of the figure, or more precisely the size of the graph, by defining the range of the figure size.<\/p>\n\n\n\n

Plotting Barplot with Seaborn<\/h2>\n\n\n\n

We’ll use the dataset of tips for working out with the Seaborn library.<\/p>\n\n\n\n

The dataset consists of:<\/p>\n\n\n\n