Funnel charts are mostly used for representing a sequential process, allowing the viewers to compare and see how the numbers change through the stages.
In this article, we’ll explore how to build a funnel chart from scratch using Matplotlib, and then we’ll have a look at an easier implementation with Plotly.
There is no method for instantly creating funnel charts in Matplotlib, so let’s start with a simple horizontal bar chart and build from there.
import matplotlib.pyplot as plty = [5,4,3,2,1]
x = [80,73,58,42,23]plt.barh(y, x)
In this article, we’ll explore Kepler.gl, an open-source solution for geospatial data visualization and exploration. Kepler was developed by Uber to make it easier for users of all levels to design meaningful maps that also look good. The tool can handle large amounts of data and has a friendly, intuitive interface that allows users to build effective maps in an instant.
Available for all to use since 2018, it’s about time we get a closer look at how the tool fits into the data visualization landscape. …
Commonly referred to as a Mosaic plot, Spine plot, or just Mekko. This chart is often used, among other applications, as market maps to visualize industries segmented by types of customers, regions, and many other variables.
In essence, they are just a stacked bar chart where the bar widths are used to display another variable, usually the whole group’s proportion to the total.
With a straightforward format that can effortlessly illustrate changes and rank variables, Slope charts are more than just an over-glorified line chart.
They have earned their place in data visualization and can be a great addition to your reports and dashboards.
In this article, we’ll explore how to plot them with Matplotlib, get a look at different ways of designing them and how they compare to other data visualizations.
With a history going back to the 18th century, Open-High-Low-Close (OHLC) charts are among the most popular financial analysis tools, typically used to illustrate stock prices over time.
In this article, we’ll see how to build an OHLC chart with Matplotlib from scratch, explore the advantages and limitations of this visualization, and get a look at a more straightforward approach with mplfinance.
The chart is composed of a series of vertical lines that pack four critical variables about the price; The minimum, maximum, initial, and ending values over time, commonly measured in hours, days, or weeks.
Bars, lines, and some colors may be more than enough to visualize most data. But too many lines or bar charts always bothered me, they make my reports and dashboards feel redundant even if the information in those charts is unique.
Lollipop charts are a great variation of bar charts and represent our data just as effectively, with a pleasant design that can break the boringness of our reports.
This will be a short tutorial since you don’t need much to turn your bar chart into a lollipop chart.
Whether you’re working with a sensor, continually pulling data from an API, or have a file that’s often updated, you may want to analyze your data in real-time.
Data visualization is all about reducing complexity; we use graphical representations to make difficult concepts and insights more comfortable to understand.
Titles, subtitles, notes, annotations, and labels serve an essential function in this process. They guide our audience through the story we’re trying to tell, much like a narrator.
In this article, we’ll explore the functions of titles, subtitles and labels, get a look at how to add annotations to our charts and check how to use custom fonts in Matplotlib.
Let’s start with a simple line chart.
import matplotlib.pyplot as plt# data spam = [263.12, 302.99, 291.23, 320.68…
Clustering sure isn’t something new. MacQueen developed the k-means algorithm in 1967, and since then, many other implementations and algorithms have been developed to perform the task of grouping data.
In this article, we’ll explore how to improve our cluster’s visualization with scatter plots.
Let’s start by loading and preparing our data. I’ll use a dataset of Pokemon stats.
import pandas as pddf = pd.read_csv('data/Pokemon.csv')# prepare data
types = df['Type 1'].isin(['Grass', 'Fire', 'Water'])
drop_cols = ['Type 1', 'Type 2', 'Generation', 'Legendary', '#']
df = df[types].drop(columns = drop_cols)
Data by itself can be quite interesting, but even if you’re dealing with a small dataset, the chances are that you’ll have to summarize or aggregate it in some way. That’s where we’ll need groups.
Sure, it’s nice to know the total amount of sales. But it’s often more interesting to know the total amount of sales by salesperson, or by month.
Grouping data is undeniably essential for data analysis, and in this article, I’ll investigate some of the methods for doing so with R, Tidyverse and dplyr.
Data visualization enthusiast