From the course: Complete Guide to Generative AI for Data Analysis and Data Science
Unlock the full course today
Join today to access over 24,800 courses taught by industry experts.
Dataframes in Python
From the course: Complete Guide to Generative AI for Data Analysis and Data Science
Dataframes in Python
- [Instructor] Pandas is a Python library that's widely used in data analytics and data science. And DataFrames are a data structure in Pandas that form the foundation of a lot of the work we do. Now, we can think of DataFrame as a basic two-dimensional structure. So here for a simple example where we have three rows of data, we have the name of a person, their age, and the city they live in. And you'll notice that this data structure looks a lot like a spreadsheet in that we have columns and rows and that we can name our columns. So we have, like name and age and city. And then we also have along the side, we have a numeric index: zero, one, and two. So we can reference each cell. So for example, the name value at row zero, the age value at row one, and so on. Now, this is a really simple DataFrame. Typically, we're more likely to see something like this where we have multiple columns or many more columns, many more rows, and different types of data. So we might have, for example…
Contents
-
-
-
-
-
-
-
-
-
-
-
-
(Locked)
Data files4m 9s
-
Using spreadsheets with CSV files2m 43s
-
(Locked)
Reviewing an example JSON file4m 29s
-
(Locked)
Using jq with JSON files6m 23s
-
(Locked)
Generating jq commands using AI6m 1s
-
(Locked)
Dataframes in Python8m 20s
-
(Locked)
Loading CSV data into dataframes3m 44s
-
(Locked)
Loading JSON into dataframes6m 17s
-
(Locked)
Inspecting dataframes4m 12s
-
(Locked)
Data quality and data cleansing6m 28s
-
(Locked)
Using AI for data quality and data cleansing5m 6s
-
(Locked)
Challenge: Missing data35s
-
(Locked)
Solution: Missing data4m
-
(Locked)
-
-
-
-
-
-
-
-
-