Pivot tables in pandas

Notes from datacamp lesson…I have found taking notes in a markdown file and creating as a post at the same time as the lesson has really helped with learning and recall.

Subsetting and calculations with pivot tables

how to call pivot tables:

df.pivot_table("column name containing numerical values to aggregate", index="columns to group by and display in rows", columns="columns to display as columns")

Pivot tables are dataframes with sorted indexes…

therefore can use loc and iloc on saved sorted dataframes…

e.g.

df_sorted = df.pivot_table("value", index=, column=)
df_sorted.loc["rows", "columns"]
#slicing is particularly good for subsetting pivot tables
df_sorted.loc["row : row"]

methods for summary statistics…

e.g. .mean()

have an axis function df_sorted.mean(axis=) default value is “index” which calculates across rows (if axis isn’t specified it will default to index).

Time to play with pivot tables apparently,

Eddie(WTR)