Time Series Data Visualization Using Matplotlib and Seaborn in Python - Tutorial 10

In this Python for Data Science Tutorial you will learn about Time series Visualization in python using matplotlib and seaborn in jupyter notebook (Anaconda).

This is the 10th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets.

Download Link for Cars Data Set:

Download Link for Enrollment Forecast:

Download Link for Iris Data Set:

Download Link for Snow Inventory:

Download Link for Super Store Sales:

Download Link for States:

Download Link for Spam-base Data Base:

Download Link for Parsed Data:

Download Link for HTML File:

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Please watch: "How to Calculate Age from Date of Birth in Excel in Years Months and Days (Simple Formula)"
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[arkapravadutta]

Quick story:
Since my superstore file was not working i ended up tinkering with the mtcars.csv file cause i got used to it over your last 2 tuitorials and ended up plotting hp vs mpg which actually makes a lot of sense.
As the hp went up the mpg went down.
This is my first real data sciency stuff that actually makes some sense.
Thank you

[mateofriedman]

For future viewers: If you keep getting an error when reading the .csv, the solutions is to add something inside the read_csv():
df = pd.read_csv(address, index_col='Order Date', parse_dates=True, encoding = "ISO-8859-1")

[triptabhattacharjee]

Hey I got this error for "from pandas import Series, Dataframe"

ImportError: cannot import name 'Dataframe' from 'pandas'


Can you help?

[arkapravadutta]

I tried doing this with the mtcars.csv and it works just fine. Is it possible that the file got corrupted while downloading

[arkapravadutta]

I keep getting this error:

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xae in position 16: invalid start byte

As per online searches i understood that there is some character that can not be decoded but i cant seem to fix it or even understand the entire issue
Can someone please help me out

[riteshtripathi]

while loading data from the GitHub link (Superstore-Sales.csv), it throws error: UnicodeDecodeError: 'utf-8' codec can't decode byte 0xae in position 1: invalid start byte
Please Advise, thank you