Lesson 16
Boolean Selection¶
This lesson covers:
- Boolean selection
where
Begin by loading the data in momentum.csv.
# Setup: Load the momentum data
import numpy as np
import pandas as pd
momentum = pd.read_csv("data/momentum.csv", index_col="date", parse_dates=True)
print(momentum.head())
Problem: Selecting rows with boolean conditions¶
Select the rows in momentum where all returns on a day are negative.
Problem: Selecting rows¶
Select the rows in momentum where 50% or more of the returns on a day are negative.
Problem: Selecting columns¶
Select the columns in momentum what have the smallest and second smallest average returns.
Problem: Selecting rows and columns¶
Select the returns for the column with the single most negative return on days where all of the returns are negative.
Problem: Selecting Elements using Logical Statements¶
For portfolio 1 and portfolio 10 compute the correlation when both returns are negative and when both are positive.
# Setup: Reproducible random numbers
rs = np.random.RandomState(19991231)
x = rs.randint(1, 11, size=(10,3))
x
Problem: Select the columns of x that means >= $E[x]$¶
Problem: Select the rows of x that means >= $E[x]$¶
Problem: Select the rows and column of x where both have means < $E[x]$¶
Problem: Using where¶
Use where to select the index of the elements in portfolio 5 that are negative. Next, use the where command in its two output form to determine which elements of the portfolio return matrix are less than -2%.
Exercise: Select the High Kurtosis Portfolios¶
Select the columns that have kurtoses above the median kurtosis.
Exercise: Select¶
Select the rows where all of the returns in the row are less than the 25% quantile for their portfolio.
Note: Comparisons between DataFrames and Series works like mathematical operations (+, -, etc.).