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Из курса от партнера IBM

Data Visualization with Python

102 оценки

IBM

Data Visualization with Python

102 оценки

Курс 2 из 4 — Specialization Applied Data Science

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.

Из урока

Introduction to Data Visualization Tools

In this module, you will learn about data visualization and some of the best practices to keep in mind when creating plots and visuals. You will also learn about the history and the architecture of Matplotlib and learn about basic plotting with Matplotlib. In addition, you will learn about the dataset on immigration to Canada, which will be used extensively throughout the course. Finally, you will briefly learn how to read csv files into a pandas dataframe and process and manipulate the data in the dataframe, and how to generate line plots using Matplotlib.

Introduction to Data Visualization4:36

Introduction to Matplotlib6:26

Basic Plotting with Matplotlib4:39

Dataset on Immigration to Canada2:43

Познакомьтесь с преподавателями

Alex Aklson
Ph.D., Data Scientist
IBM Developer Skills Network

In this video, things will start getting more exciting. We will generate our first

visualization tool: the line plot. So what is a line plot? As its name suggests, it

is a plot in the form of a series of data points connected by straight line

segments. It is one of the most basic type of chart and is common in many

fields not just data science. The more important question is when to use line

plots. The best use case for a line plot is when you have a continuous dataset

and you're interested in visualizing the data over a period of time. As an

example, say we're interested in the trend of immigrants from Haiti to Canada.

We can generate a line plot and the resulting figure will depict the trend

of Haitian immigrants to Canada from 1980 to 2013. Based on this line plot, we

can then research for justifications of obvious anomalies or changes. So in this

example, we see that there is a spike of immigration from Haiti to Canada in 2010.

A quick Google search for major events in Haiti in 2010 would return the tragic

earthquake that took place in 2010, and therefore this influx of immigration to

Canada was mainly due to that tragic earthquake. Okay now how can we generate

this line plot? Before we go over the code to do that, let's do a quick recap

of our dataset. Each row represents a country and contains metadata about the

country such as where it is located geographically, and whether it is

developing or developed. Each row also contains numerical figures of annual

immigration from that country to Canada from 1980 to 2013.

Now let's process the dataframe so that the country name becomes the index of

each row. This should make querying specific countries easier. Also let's add

an extra column which represents the cumulative sum of annual immigration from

each country from 1980 to 2013. So for Afghanistan, it is 58,639,

total, and for Albania it is 15,699,

and so on. And let's name our dataframe df_canada. So now

that we know how our data is stored in the dataframe, df_canada,

let's generate the line plot corresponding to immigration from Haiti.

First, we import Matplotlib as mpl and its scripting interface as plt. Then,

we call the plot function on the row corresponding to Haiti and we set kind

equals line to generate a line plot. Note that we used years which is a list

containing string format of years from 1980 to 2013 in order to exclude the

column of total immigration that we added. Then to complete the figure, we

give it a title and we label its axes. Finally we call the show function to

display the figure. Note that this is the code to generate the line plot using the

magic function % matplotlib with the inline backend. And there you have it: a

line plot that depicts immigration from Haiti to Canada from 1980 to 2013.

In the lab session, we explore line plots in more details so make sure to

complete this module's lab session. This concludes our video on line plots. I'll

see you in the next video.

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