Dataset on Immigration to Canada

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Do curso por IBM

Data Visualization with Python

769 classificações

IBM

Data Visualization with Python

769 classificações

Curso 7 de 9 em Specialization IBM Data Science Professional Certificate

"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. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

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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

Conheça os instrutores

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

In this video, we will learn more about the dataset that we will be using

throughout the course. The population division of the United Nations compiled

immigration data pertaining to 45 countries. The data consist of the total

number of immigrants from all over the world to each of the 45 countries as

well as other metadata pertaining to the immigrants countries of origin. In this

course, we will focus on immigration to Canada and we will work primarily with

the data set involving immigration to the great white north Here is a snapshot

of the UN data on immigration to Canada in the form of an excel file. As you can

see, the first twenty rows contain textual data about the UN Department and other

irrelevant information. Row 21 contains the labels of the columns. Following that

each row represents a country and contains metadata about the country such

as what continent it resides in, what region it belongs to, and whether the

region is developing or developed. Each row also contains the total number of

immigrants from that country for the years 1980 all the way to 2013.

Throughout this course, we will be using pandas for any analysis of the data

before creating any visualizations. So in order to start creating different types

of plots of the data whether for exploratory analysis or for presentation,

we will need to import the data into a pandas dataframe. To do that, we will

need to import the pandas library as well as the xlrd library, which is

required to extract data from Excel spreadsheet files. Then we call the

pandas function read_excel to read the data into a pandas dataframe.

And let's name this dataframe df_can. Notice how we're skipping

the first twenty rows to read only the data corresponding to each country. If you

want to confirm that you have imported your data correctly, in pandas, you can

always use the head function to display the first five rows of the dataframe. So,

if we call this function on our dataframe, df_can, here is the

output. As you can see, the output of the head function looks correct with the

columns having the correct labels and each row representing a country and

containing the total number of immigrants from that country. This

concludes our video on the immigration to Canada dataset. I will see you in the

next video.

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