Categorise precipitation type for values of temperature and precipitation value (python dataframe)-CodePudding

I have dataframe for temperature, precipitation:

dataframe

I want to categorize the precipitation for the following types;

* 0: No precipitation
* 1: Snow
* 2: Mixed snow and rain
* 3: Rain
* 4: Drizzle
* 5: Freezing rain
* 6: Freezing drizzle

I tried the following function:

def func(x):
    if smhi['Temperature'] < -8 and smhi['Precipitation'] > 1 : smhi['PreciCateg'] = '1'
    elif smhi['Temperature'] < -2 and smhi['Precipitation'] > 1 : smhi['Temperature'] = '2'
    elif smhi['Temperature'] < 30 and smhi['Precipitation'] >= 1 : smhi['PreciCateg'] = '3'
    elif smhi['Temperature'] < 20 and smhi['Precipitation'] < 1 : smhi['Temperature'] = '4'
    elif smhi['Temperature'] < 5 and smhi['Precipitation'] > 0.5 : smhi['PreciCateg'] = '5'
    elif smhi['Temperature'] < 5 and smhi['Precipitation'] > 0.2 : smhi['Temperature'] = '6'
    else: smhi['PreciCateg'] = '0'
smhi['PreciCateg'] = smhi.apply(func)

I get:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

I think I messed up the logic for categorisation!?

CodePudding user response：

Use numpy.select:

import numpy as np

conditions = [smhi["Temperature"].lt(-8) & smhi["Precipitation"].gt(1),
              smhi["Temperature"].lt(-2) & smhi["Precipitation"].gt(1),
              smhi["Temperature"].lt(30) & smhi["Precipitation"].ge(1),
              smhi["Temperature"].lt(20) & smhi["Precipitation"].lt(1),
              smhi["Temperature"].lt(5) & smhi["Precipitation"].gt(0.5),
              smhi["Temperature"].lt(5) & smhi["Precipitation"].gt(0.2)]

smhi["PreciCateg"] = np.select(conditions, [1,2,3,4,5,6], 0)

>>> smhi
   Temperature  Precipitation  Wind Speed         timestamp  PreciCateg
0        -1.33           0.17        2.61  2017-1-1 0:00:00           4
1        -1.93           0.07        2.06  2017-1-1 1:00:00           4
2        -2.39           0.02        1.98  2017-1-1 2:00:00           4
3        -2.57           0.01        2.24  2017-1-1 3:00:00           4
4        -3.23           0.00        2.18  2017-1-1 4:00:00           4