如何删除具有 NaN 值的行，以便我可以压缩和范围

Question

I have a code that range the values between two columns, the code works normally when there is no empty cell, I tried the df.isnull, dropna, always the same problem

import pandas as pd
import numpy as np


path  = [('SC200', 100, 102),
           ('Unified', 210, 210),
           ('Clé',np.nan,np.nan),
           ('samsung', 155, 158),
           ]

df_l = pd.DataFrame(path, columns=['Désignation', 'First', 'Last'])

zipped_l = zip(df_l['Désignation'], df_l['First'], df_l['Last'])

df_l = pd.DataFrame([(k, y) for k, s, e in zipped_l for y in range(s, e+1) ], columns=['Désignation', 'KITCODE'])

print(df_l)

Answer 1

Is this what you are trying to do?

import pandas as pd
import numpy as np

path  = [('SC200', 100, 102),
           ('Unified', 210, 210),
           ('Clé',np.nan,np.nan),
           ('samsung', 155, 158),
           ]

df_l = pd.DataFrame(path, columns=['Désignation', 'First', 'Last'])
print (df_l)
def kitcd(d):
    first = int(d.First)
    last  = int(d.Last) + 1
    return [i for i in range(first, last)]

df_l['KITCODE'] = df_l.apply(lambda x: kitcd(x) if pd.notnull(x.First) else x.First, axis = 1)
df_l = df_l.explode('KITCODE')

print (df_l)

The output of this will be:

Original dataframe:

  Désignation  First   Last
0       SC200  100.0  102.0
1     Unified  210.0  210.0
2         Clé    NaN    NaN
3     samsung  155.0  158.0

Updated dataframe with KITCODE:

  Désignation  First   Last KITCODE
0       SC200  100.0  102.0     100
0       SC200  100.0  102.0     101
0       SC200  100.0  102.0     102
1     Unified  210.0  210.0     210
2         Clé    NaN    NaN     NaN
3     samsung  155.0  158.0     155
3     samsung  155.0  158.0     156
3     samsung  155.0  158.0     157
3     samsung  155.0  158.0     158

If you want to ignore the rows that have NaN, then you can change the code to the following:

def kitcd(d):
    first = int(d.First)
    last  = int(d.Last) + 1
    return [i for i in range(first, last)]

df_l = df_l.dropna(axis=0, subset=['First', 'Last'])

df_l['KITCODE'] = df_l.apply(lambda x: kitcd(x), axis = 1)
df_l = df_l.explode('KITCODE')

print (df_l)

This will remove the record from df_l and will help you process the data as normal. The output will have same set with one row missing 'Clé'