Category Pandas

Turning category labels into indicator columns gives a pandas table one column for each category. pandas.get_dummies() creates those columns, using Boolean values by default or integers when you set dtype=int. I’ll show how to encode selected columns and align training…

A table can keep one row per record while storing each measurement in a separate column. Pandas melt turns selected measurement columns into labeled rows, and pivot reshapes those rows into columns again. This article walks through choosing columns for…

Splitting a set of numbers, such as test scores, into groups like quartiles lets you compare values by where they rank. pandas qcut does this by rank, putting a similar number of values in each group. This guide shows how…

Data saved as a CSV (comma-separated values) file is plain text until you load it into something Python can work with. pandas read_csv() reads the file into a DataFrame, a table with labeled rows and columns. In this piece, I’ll…

`header=3` makes the fourth worksheet row the column labels. In the sample, `i20` becomes a heading, which makes me notice how directly that offset picks the labels. Compare a call for one worksheet with a call that uses `sheet_name=None`. Inspect…

A pickle file records the Python path of every class inside the object that wrote it. That recorded path is why a file loads on one pandas release and raises a missing-module error on another. I loaded a frame from…

I stacked two DataFrames with concat and watched every row line up wrong because the keys never matched. I ran pd.concat([employees, departments], axis=1) on my machine and got NaN-filled rows where Alice should have met Engineering, and that was when…

Pandas reads the first CSV row as column names by default. Use pd.read_csv with header=0 when you want to state that choice explicitly, or header=None when the first row is data. The default header behavior keeps named-column selection straightforward. Read…

Pandas makes moving data around straightforward, and ORC has become a reliable choice for analytical workloads. It sits in the same space as Parquet, but ORC offers better compression and solid type preservation. Writing a Pandas DataFrame to ORC takes…