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Aug 26, 2024 · In this article, we will provide a comprehensive guide to using Matplotlib for creating various types of plots and customizing them to fit specific needs and how to visualize data with the help of the Matplotlib library of Python.
Overview of many common plotting commands provided by Matplotlib. See the gallery for more examples and the tutorials page for longer examples. Pairwise data # Plots of pairwise (x, y), tabular (v a r _ 0, ⋯, v a r _ n), and functional f (x) = y data. plot (x, y) scatter (x, y) bar (x, height) stem (x, y) fill_between (x, y1, y2) stackplot (x, y)
- Selva Prabhakaran
- Scatter plot. Scatteplot is a classic and fundamental plot used to study the relationship between two variables. If you have multiple groups in your data you may want to visualise each group in a different color.
- Bubble plot with Encircling. Sometimes you want to show a group of points within a boundary to emphasize their importance. In this example, you get the records from the dataframe that should be encircled and pass it to the encircle() described in the code below.
- Scatter plot with linear regression line of best fit. If you want to understand how two variables change with respect to each other, the line of best fit is the way to go.
- Jittering with stripplot. Often multiple datapoints have exactly the same X and Y values. As a result, multiple points get plotted over each other and hide.
- Python Libraries
- Nature of Visualization
- Box Plot
- Scatter Plot
- Histogram
- Countplot
- Correlation Plot
- Heat Maps
- Pie Chart
- Error Bars
There are a lot of python libraries which could be used to build visualization like matplotlib, vispy, bokeh, seaborn, pygal, folium, plotly, cufflinks, and networkx. Of the many, matplotlib and seabornseems to be very widely used for basic to intermediate level of visualizations.
Depending on the number of variables used for plotting the visualization and the type of variables, there could be different types of charts which we could use to understand the relationship. Based on the count of variables, we could have 1. Univariateplot(involves only one variable) 2. Bivariateplot(more than one variable in required) A Univariate...
A boxplot, also known as a box and whisker plot, the box and the whisker are clearly displayed in the below image. It is a very good visual representation when it comes to measuring the data distribution. Clearly plots the median values, outliers and the quartiles. Understanding data distribution is another important factor which leads to better mo...
Scatter plots or scatter graphs is a bivariateplot having greater resemblance to line graphs in the way they are built. A line graph uses a line on an X-Y axis to plot a continuous function, while a scatter plot relies on dots to represent individual pieces of data. These plots are very useful to see if two variables are correlated. Scatter plot co...
Histograms display counts of data and are hence similar to a bar chart. A histogram plot can also tell us how close a data distribution is to a normal curve. While working out statistical method, it is very important that we have a data which is normally or close to a normal distribution. However, histograms are univariate in nature and bar charts ...
A countplot is a plot between a categorical and a continuous variable. The continuous variable in this case being the number of times the categorical is present or simply the frequency. In a sense, count plot can be said to be closely linked to a histogram or a bar graph. It simply shows the number of occurrences of an item based on a certain type ...
Correlation plot is a multi-variate analysis which comes very handy to have a look at relationship with data points. Scatter plots helps to understand the affect of one variable over the other. Correlation could be defined as the affect which one variable has over the other. Correlation could be calculated between two variables or it could be one v...
Heat map is a multi-variate data representation. The color intensity in a heat map displays becomes an important factor to understand the affect of data points. Heat maps are easier to understand and easier to explain as well. When it comes to data analysis using visualization, its very important that the desired message gets conveyed with the help...
Pie chart is a univariate analysis and are typically used to show percentage or proportional data. The percentage distribution of each class in a variable is provided next to the corresponding slice of the pie. The python libraries which could be used to build a pie chart is matplotlib and seaborn. Below are the advantages of a pie chart 1. Easier ...
Error bars could be defined as a line through a point on a graph, parallel to one of the axes, which represents the uncertainty or error of the corresponding coordinate of the point. These types of plots are very handy to understand and analyze the deviations from the target. Once errors are identified, it could easily lead to deeper analysis of th...
Apr 24, 2023 · In this article, I'll show you how to create a bar chart, a pie chart, and a line plot to explain how you can do data visualization using Matplotlib. The first thing you need is to import the Matplotlib and other relevant libraries like Pandas, Numpy and their sub modules.
Oct 6, 2023 · This tutorial demonstrates how to use Matplotlib, a powerful data visualization library in Python, to create line, bar, and scatter plots with stock market data.
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Matplotlib cheatsheets — Visualization with Python. Plot types. User guide. Tutorials. Examples.
