I explained in my previous post about arrays and how to manipulate them using NumPy in Python. In this post, I am going to tell you on top differences of Series data Vs DataFrame in Python.
1). How the Series Data Looks Like in Python ‘Pandas’
The data is in the form of Key, Value pair. You can ask a question about what is Key. So, I am giving here example of Series data.
Sample Series Data
Key is equal to index in Key value data.
2). How the Data in DataFrame Displayed in Python ‘Pandas’
The dataFrame is an ordered collection of data. You can create data in multiple dimensions.
The Data in the tabular form. It consist of Data, rows, Columns. Example DataFrame from geeksforgeeks.com
In Python, with ‘pandas’ you can create DataFrame.
List of Data Sources
In support of the growing demand for data, a huge number of data sources are now available on the Internet. These data sources freely provide information to anyone in need, and they are called open data.
- DataHub (http://datahub.io/dataset)
- World Health Organization (http://www.who.int/research/en/)
- Data.gov (http://data.gov)
- European Union Open Data Portal (http://open-data.europa.eu/en/data/)
- Amazon Web Service public datasets (http://aws.amazon.com/datasets)
- Facebook Graph (http://developers.facebook.com/docs/graph-api)
- Healthdata.gov (http://www.healthdata.gov)
- Google Trends (http://www.google.com/trends/explore)
- Google Finance (https://www.google.com/finance)
- Google Books Ngrams (http://storage.googleapis.com/books/ngrams/books/datasetsv2.html)