4.3 Doing the data analysis

Key points

1. NVIDIA's exploratory data analysis (EDA) tutorial starts by reviewing the dataset and understanding the variables. This tells you the dimensions and the kinds of data in the DataFrame.

   What NVIDIA says (2)

   “Review the dataset and understand the variables that you are working with.”

   — Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF

   “This helps you understand the dimensions of and the kinds of data in the DataFrame.”

   — Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF

2. NVIDIA's exploratory data analysis (EDA) walkthrough notes that some missing data is acceptable, but frequent outages could make the data misrepresent true conditions.

   What NVIDIA says (1)

   “Some missing data is acceptable, but if the stations went down too often, the data could be misrepresentative of true conditions throughout the year.”

   — Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF

3. NVIDIA's pandas glossary says pandas imports and exports comma-separated values (CSV), Structured Query Language (SQL) and spreadsheet files. Combined with its manipulation features, it can clean, shape and analyze tabular data.

   What NVIDIA says (2)

   “pandas facilitates importing and exporting datasets from various file formats, such as CSV, SQL, and spreadsheets.”

   — What Is Pandas and Why Does it Matter?

   “These operations, combined with its data manipulation capabilities, enable pandas to clean, shape, and analyze tabular and statistical data.”

   — What Is Pandas and Why Does it Matter?

Key terms

• pandas / DataFrame: Python library for tabular data; a DataFrame is a table of rows and columns.
• Exploratory data analysis: Open-ended exploration of a dataset to understand it before modeling.

Sample question

You receive a large new dataset. Which first step does NVIDIA's cuDF exploratory data analysis (EDA) walkthrough recommend?