Data Analysis with Python and Pandas

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Karlijn Dinnissen
Data Quality Analyst, Textkernel
10
Data Analysis with Python and Pandas

"Attending the bespoke course Data Munging with Pandas at Textkernel has proven to be an excellent choice. Jeroen’s personal approach and highly interactive way of teaching made this course valuable to a diverse group of developers and analysts, as did the possibility to apply theory on our own data and API during the courses. I’ve since been able to code cleaner and more efficient, and applied the pandas package in several monitoring and analytics scripts." - 10.11.2020 09:37

"Attending the bespoke course Data Munging with Pandas at Textkernel has proven to be an excellent choice. Jeroen’s personal approach and hig… Gesamte Bewertung lesen - 10.11.2020 09:37

Startdaten und Startorte

computer Online: Video conferencing
26. Mär 2021
check_circle Garantierte Durchführung
Details ansehen
event 26. März 2021, 10:00-17:00, Video conferencing, Day 1

Beschreibung

Introduction

Learn how to accelerate your data analyses using Pandas, a Python library specifically designed for working with medium-sized data sets. Together with JupyterLab it enables a convenient environment for interactive data analysis.

Pandas is part of the so-called PyData ecosystem, and in this workshop we’ll start by providing an overview of PyData and explain where Pandas stands and how it interacts with other libraries such as NumPy and Seaborn. Pandas introduces a few new data structures, most importantly the DataFrame, which are essential to understand how to work with tabular data efficiently.

Pandas offers many features, and in one day, through a good balance of presentation a…

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Frequently asked questions

Es wurden noch keine Besucherfragen gestellt. Wenn Sie weitere Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice.

Noch nicht den perfekten Kurs gefunden? Verwandte Themen: Data Science Python, Python, Big Data, Data Science und Data Analytics.

Introduction

Learn how to accelerate your data analyses using Pandas, a Python library specifically designed for working with medium-sized data sets. Together with JupyterLab it enables a convenient environment for interactive data analysis.

Pandas is part of the so-called PyData ecosystem, and in this workshop we’ll start by providing an overview of PyData and explain where Pandas stands and how it interacts with other libraries such as NumPy and Seaborn. Pandas introduces a few new data structures, most importantly the DataFrame, which are essential to understand how to work with tabular data efficiently.

Pandas offers many features, and in one day, through a good balance of presentation and interactive exercises, we’re going to cover the most important ones, including: importing, filtering, grouping, joining, exploring, and visualising data. By the end of this workshop, you’ll understand the fundamentals of Pandas, be aware of common pitfalls, and be ready to perform your own analyses.

What you'll learn

  • Load data from text files, spreadsheets, databases, and APIs
  • Use the Split-Apply-Combine paradigm to summarise data
  • Performing advanced joins and merges
  • Generating insightful pivot tables
  • Transforming data between wide and long formats
  • Working with time series data
  • Explore data using a variety of visualisation types
  • Avoid common pitfalls in NumPy and Pandas by understanding general concepts and principles

This workshop is for you because

  • You have experience in Excel or R and want to learn about pandas and the PyData ecosystem
  • You have programming experience in Python and want to start analysing data using pandas
  • You want to improve your understanding of Pandas by learning general concepts and principles common to most data manipulation frameworks

Schedule

  • Overview of the PyData ecosystem
    • NumPy, SciPy, Pandas
    • Matplotlib, Seaborn, Bokeh
    • SciKit-Learn
  • Essential data structures
    • Numpy data types
    • Numpy arrays
    • Pandas Series
    • Pandas DataFrames
    • Pandas Index, MultiIndex
  • Importing data
    • From CSV
    • From Excel
    • From Databases
    • From APIs
  • Manipulating data
    • Selecting rows and columns
    • Filtering rows
    • Joining and concatenating
    • Missing values, duplicates
    • Converting data types
    • Dates and times
    • Working with categorical data
    • String manipulation
  • Exploring data
    • Computing aggregate statistics
    • Pivot tables
    • Correlations
  • Visualising data
    • Histogram
    • Densityplot
    • Boxplot
    • Bar chart

Prerequisites

You’re expected to have some experience with programming in Python. Our workshop Introduction to Programming in Python is one option that can help you with that. Roughly speaking, if you’re familiar with the following Python syntax and concepts, then you’ll be fine:

  • assignment, arithmetic, boolean expression, tuple unpacking
  • bool, int, float, list, tuple, dict, str, type casting
  • in operator, indexing, slicing
  • if, elif, else, for, while
  • range(), len(), zip()
  • def, (keyword) arguments, default values
  • import, import as, from import ...
  • lambda functions, list comprehension
  • JupyterLab or Jupyter Notebook

Recommended preparation

We're going to use Python together with JupyterLab and the following packages:

  • numpy
  • pandas
  • seaborn

The recommended way to get everything set up is to download and install the Anaconda Distribution.

Alternatively, if you don’t want to use Anaconda, then you can install everything using pip. In any case, if running import pandas, seaborn doesn’t produce any errors then you know you’ve set up everything correctly.

Clients

We’ve previously delivered this workshop at:

  • Brabant Water
  • Jheronimus Academy of Data Science
  • Textkernel
  • Transavia
  • Vocalink

Testimonials

"At Brabant Water, most of us were still using spreadsheets to clean, analyse, and model our data. Thanks to Data Science Workshops, who delivered an engaging, hands-on workshop at our office, many of us have switched to Python and Jupyter Notebook, which allows our analyses to be much more advanced and reliable."

--Stijn de Jong, Advisor Water Supply, Brabant Water

"Attending the bespoke course Data Munging with Pandas at Textkernel has proven to be an excellent choice. Jeroen's personal approach and highly interactive way of teaching made this course valuable to a diverse group of developers and analysts, as did the possibility to apply theory on our own data and API during the courses. I've since been able to code cleaner and more efficient, and applied the pandas package in several monitoring and analytics scripts."

--Karlijn Dinnissen, Data Quality Analyst, Textkernel

9
Durchschnittliche Bewertung für Data Analysis with Python and Pandas
Berechnet aus 5 Bewertungen.
starstarstarstarstar
Karlijn Dinnissen
Data Quality Analyst, Textkernel
10
Data Analysis with Python and Pandas

"Attending the bespoke course Data Munging with Pandas at Textkernel has proven to be an excellent choice. Jeroen’s personal approach and highly interactive way of teaching made this course valuable to a diverse group of developers and analysts, as did the possibility to apply theory on our own data and API during the courses. I’ve since been able to code cleaner and more efficient, and applied the pandas package in several monitoring and analytics scripts." - 10.11.2020 09:37

"Attending the bespoke course Data Munging with Pandas at Textkernel has proven to be an excellent choice. Jeroen’s personal approach and hig… Gesamte Bewertung lesen - 10.11.2020 09:37

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Robertus van Gaal
Technical Consultant
8
Data Analysis with Python and Pandas

"A very eager teacher who wants to be challenged in his knowledge. You will learn the material by doing and he managed to make it interesting for advanced, rusty and beginner students. " - 09.11.2020 09:33

"A very eager teacher who wants to be challenged in his knowledge. You will learn the material by doing and he managed to make it interesting… Gesamte Bewertung lesen - 09.11.2020 09:33

Muyiwa Felixson-Yusuf
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Muyiwa Felixson-Yusuf
Design/UX Manager
10
Data Analysis with Python and Pandas

"One of the best training sessions I've had so far. Jeroen is extremely knowledgable, he was able to balance a class of development and non-development members and still got practical results from everyone.
We covered a wide range of topics from Basic Python, Pandas, Data analysis fundamentals, to introductory Machine Learning.
It was amazing how much impact his short stay in our HQ, Kano Nigeria had on my general outlook to Python, Data Analysis and ML.

I highly recommend Data Science workshops.com to anyone at any level
" - 04.11.2020 21:35

"One of the best training sessions I've had so far. Jeroen is extremely knowledgable, he was able to balance a class of development and non-d… Gesamte Bewertung lesen - 04.11.2020 21:35

Oluwatosin Eke Williams
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Oluwatosin Eke Williams
8
Data Analysis with Python and Pandas

"Jeroen had a total understanding of the subject matter and the training was really tailored to my need in understanding data sciences. It kept pushing me and engineering me to understand more about Pandas and Python, putting me in front of where I used to be. Thanks for your mentorship, Jeroen." - 04.11.2020 08:55

"Jeroen had a total understanding of the subject matter and the training was really tailored to my need in understanding data sciences. It ke… Gesamte Bewertung lesen - 04.11.2020 08:55

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Henning Rode
Senior Research Engineer
9
Data Analysis with Python and Pandas

"He makes sure everything works as interactive as possible. So you really get "hands on" experience.

It was possible any moment to ask questions, though that never slowed down the course program too much." - 04.11.2020 07:26

"He makes sure everything works as interactive as possible. So you really get "hands on" experience. It was possible any moment to ask que… Gesamte Bewertung lesen - 04.11.2020 07:26

Es wurden noch keine Besucherfragen gestellt. Wenn Sie weitere Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice.

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