This video how to use the where() function in numpy and pandas to extract indices based on logical conditions and populate new columns of data based on elementwise logic. The np.where() function can perform a similar operation to the ifelse() function in R.
Code used in this Python Code Clip:
import numpy as np
import pandas as pd
import statsmodels.api as sm #(To access mtcars dataset)
mtcars = sm.datasets.get_rdataset(“mtcars”, “datasets”, cache=True).data
mtcars.head()
inds = np.where(mtcars.mpg > 22)
inds
np.where(mtcars.mpg > 22, # Condition
“High MPG”, # Value to set if condition is True
“Low MPG”) # Value to set if condition is False
np.where(mtcars.mpg > 22, # Condition
mtcars.mpg, # Value to set if condition is True
mtcars.cyl) # Value to set if condition is False
► Subscribe: https://www.youtube.com/c/DataDaft?sub_confirmation=1
#python