Learn how to use Pandas to calculate a sum, including adding Pandas Dataframe columns and rows, and how to add columns conditionally.
Learn how to use the Pandas diff method to calculate the difference between dataframe rows and columns, including at defined intervals.
Learn how to normalize and standardize a Pandas Dataframe with sklearn, including max absolute scaling, min-max scaling and z-scoare scaling.
Learn how to use Python to count the number of occurrences of an item in a list, using count, Counter, pandas, operator, and comprehensions.
Learn how to use the Pandas quantile method to calculate percentiles in Pandas including how to modify the interpolation of values.
Learn how to use the Pandas rank method to rank you data, including how to rank a grouped dataframe using the groupby method.
Learn how to use the Pandas describe method to generate summary statistics on your Pandas Dataframe, including changing percentiles.
Learn how to use the Python Pandas shift function to move a dataframe’s rows up or down, including working with time series and missing data.
Learn how to sample data in Pandas using Python, including how to use the sample function, reproduce results, and weighted samples of data.
Learn to use Python to lowercase text, using the lower and caseload functions, checking if strings are lower and converting lists to lower.
Use Python and Pandas to export a dataframe to a CSV file, using .to_csv, including changing separators, encoding, and missing values.
Learn how to use Python and Pandas to iterate over rows of a dataframe, why vectorization is better, and how to use iterrows and itertuples.