Course: Analyzing Data Using Python

$109.00
$131.89 incl. vat
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duration: 10 hours |

Language: English (US) |

access duration: 90 days |

In Onbeperkt Leren

Details

Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. In this training, you will develop the skills to work with this library. For data analysis to be valuable and accurate, the analyzed data needs to be cleaned and curated. There are copious methods to achieve this in pandas. In this course, you'll learn how to identify and eliminate duplicates in pandas. You will start the training by installing pandas from the Jupyter notebook. You will learn how to filter data using loc and iloc, classic pandas lookup syntax, and the pandas filter and query methods.

You'll practice using common file types such as CSV, Excel, JSON, and HTML through pandas. With these files, you can do many different things, including statistics base calculation, editing, and sorting data in rows. To analyze data, the data must be accurate. In this training, you will learn to identify and eliminate duplicates. Finally, you will get started with aggregating data using groupby, pivot and pivot_table.

Result

After completing this course successfully, you have the skills to manage and analyze data by using pandas.

Prerequisites

Basic Python knowledge is recommended.

Target audience

Database Administrators, Data analist

Content

Analyzing Data Using Python

10 hours

Analyzing Data Using Python: Data Analytics Using Pandas

Built on the Python programming language, pandas provides a flexible and open source tool for data manipulation. In this course, you'll develop the skills you need to get started with this library. You'll begin by installing pandas from a Jupyter notebook using pip.

Next, you'll instantiate a pandas object, including a Series and DataFrame, and practice several ways of instantiating Dataframes - for instance, from lists, dictionaries of lists, and tuples created from lists using the zip method.

You round out this course by performing filter operations on DataFrames using the loc and iloc operations - fundamental techniques used to access specific rows and columns. You'll use loc to identify rows based on labels and iloc to access rows based on the index offset position starting from 0.

Analyzing Data Using Python: Importing, Exporting, & Analyzing Data With Pandas

You can analyze a myriad of data formats through pandas - all you need to know is how. In this course, you'll bring various data types into pandas and perform several operations on the data.

You'll practice using common file types such as CSV, Excel, JSON, and HTML through pandas. You'll not only learn how to open and read files of different types, but you'll also serialize objects and copy them to the in-memory clipboard.

You'll move on to perform various fundamental operations on DataFrame objects. Lastly, you'll learn to compute basic statistics, access metadata, and modify and sort data in rows.

Analyzing Data Using Python: Analyzing Data Using Python: Filtering Data in Pandas

Not all data is useful. Luckily, there are some powerful filtering operations available in pandas. The course begins with a detailed look at how loc and iloc can be used to access specific data from a DataFrame. You'll move on to filter data using the classic pandas lookup syntax and the pandas filter and query methods. You'll illustrate how the filter function accepts wildcards as well as regular expressions and use various methods such as the .isin method to filter data.

Furthermore, you'll filter data using either two pairs of square brackets - in which case the resulting subset is itself a DataFrame - or a single pair of square brackets, in which case the returned data takes the form of a Series. You'll drop rows and columns from a pandas DataFrame and see how rows can be filtered out of a DataFrame. Lastly, you'll identify a possible gotcha that arises when you drop rows in-place but neglect to reset the index labels in your object.

Analyzing Data Using Python: Cleaning & Analyzing Data in Pandas

For data analysis to be useful and accurate, the analyzed data needs to be cleaned and curated. There are copious methods to achieve this in pandas. In this course, you'll learn how to identify and eliminate duplicates in pandas.

You'll start by using the pandas cut method to discretize data into bins, using bins to plot histograms and identify outliers using box-and-whisker plots. You'll parse and work with datetime objects read in from strings and convert string columns to datetime using the dateutils python library.

Moving on, you'll master different pandas methods for aggregating data - including the groupby, pivot, and pivot_table methods. Lastly, you'll perform various joins - inner, left outer, right outer, and full outer - using both the merge and join methods.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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