Came across this article on medium that covered 20 tips when performing EDA on a pandas dataframe

https://towardsdatascience.com/20-pandas-functions-that-will-boost-your-data-analysis-process-f5dfdb2f9e05

Some of the more useful ones in there I thought were:

• df.infer_objects().dtypes —> by far most useful one I found. function will infer better datatypes from columns whose datatype is object. Ive run into errors before because one series will contain multiple datatypes
• df.sample(n) —> returns a random subset of the dataframe. can either pass an int in for n to get a certain number of rows back, or pass in frac=0.5 to return a fraction of the dataframe
• df[some_column].pct_change —> shows the percentage change of the values in a series. useful for time series data
• df.explode(column) —> if a column contains values in a list, it will expand the dataframe so each value in the list is now its own row.

https://github.com/prompt-toolkit/ptpython

• custom python shell to make writing python easier/prettier from the terminal. I almost never am writing python strictly in my terminal, but for those that do this could be helpful