Jul-20-2019, 08:48 AM
Ability to use NaN values with columns of integer type is latest pandas feature (and still experimental).
As of Pandas 0.24.x you can do this, e.g.
As of Pandas 0.24.x you can do this, e.g.
import pandas as pd import numpy as np diSales= { 2016:{'qtr1':34500,'qtr2':56000,'qtr3':47000,'qtr4':49000}, 2017:{'qtr1':44900,'qtr2':46100,'qtr3':57000,'qtr4':59000}, 2018:{'qtr1':54500,'qtr2':51000,'qtr3':57000,'qtr4':58500}, 2019:{'qtr1':61000} } df = pd.DataFrame(diSales, dtype=pd.Int32Dtype()) df.mad(axis=0)
df.info()
returns integer dtype, but df.mad
still returns float.Output:2016 6062.5
2017 6250.0
2018 2500.0
2019 0.0
dtype: float64