从 for 循环 [python pandas] 获取 NAN 值

Question

I have a python dataframe with a column CREATIVE_NAME and I want to create a new column CREATIVE_SIZE by searching specific substrings and put them in the new column.

 creative_size = []
    for i in df['CREATIVE_NAME']:
        if search('320x480', i):
            creative_size.append('320x480')
        elif search('728x1024', i):
            creative_size.append('728x1024')
        elif search('320x50', i):
            creative_size.append('320x50')
        elif search('728x90', i):
            creative_size.append('728x90')
        elif search('300x250', i):
            creative_size.append('300x250')
        elif search('80x80', i):
            creative_size.append('80x80')
        elif search('1200x627', i):
            creative_size.append('1200x627')
        elif search('768x1024', i):
            creative_size.append('768x1024')
        elif search('320x420', i):
            creative_size.append('320x420')
        elif search('768x820', i):
            creative_size.append('768x820')
        else:
            creative_size.append('no creative size')

sizes = pd.Series(creative_size)
df.insert(column='creative_size', value=sizes, loc = 0)

df['creative_size'].isna().sum()
output: 1579

I don't understand why I'm getting NAN values from for loop because it should have captured all the conditions and nothing should be left out.

Answer 1

import pandas as pd
####    FOR TESTING ####
test_data_dict = {
'CREATIVE_NAME':['320x480', '728x1024', '1000x1000']
}

df = pd.DataFrame(data=test_data_dict)

#### Define a set of all creative sizes you want to check against

creative_sizes =('320x480','728x1024','320x50','728x90','300x250','80x80','1200x627','768x1024','320x420','768x820') #list of valid creative sizes

###### Define a function which will check if `C_name` is a substring of available creative_sizes

def get_creative_size(c_name):
    #c_name is the value of creative_name in row
    result = [size for size in creative_sizes if c_name in size] 
    if len(result) > 0:
        return result[0]
    else:
        return 'no creative size'

df['CREATIVE_SIZE'] = df['CREATIVE_NAME'].apply(lambda x: get_creative_size(x))
print(df.head())