- Writing and running Python in iPython
- Using Python lists and dictionaries
- Creating NumPy arrays
- Indexing and slicing in NumPy
- Downloading and parsing data files into NumPy and Pandas
- Using multilevel series in Pandas
- Aggregating data in Pandas
Skill Level Intermediate
- Hi, I'm Michele Vallisneri and I'd like to welcome you to Introduction to Data Analysis with Python. Data science has been described as intersection of programming, statistics and topical expertise. Python is an excellent programming tool for data analysis because it's friendly, pragmatic, mature and because it's complemented by excellent third party packages that were designed to deal with large amounts of data. We will start this course by reviewing Python data containers which are useful on their own and which set the model for the more powerful data objects of NumPy and Pandas.
We will then put our knowledge of containers to work in a practical project. Then, we will talk about NumPy, the package that extends Python with a fast and efficient numerical array object. And we'll take NumPy out for a spin for a real data analysis project. Last, we will look at Pandas which is suitable for any kind of data and implements many ideas from the world of relational databases. We will use Pandas for its own practical project. So, let's get started with Introduction to Data Analysis with Python.
Q: The course shows how to download files from FTP and web servers using Python 3.X. How do I do the same thing with Python 2.7?
A: First import urllib, then use urllib.urlretrieve(URL,filename). For instance, to download the stations.txt files used in the chapter 5 video “Downloading and parsing data files,” you’d do urllib.urlretrieve(‘ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt','stations.txt').
1. Installation and Setup
2. Refresher: Data Containers in Python
3. Word Anagrams in Python
4. Introduction to NumPy
5. Weather Data with NumPy
6. Introduction to Pandas
7. Baby Names with Pandas
Next steps1m 36s
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