Filter variable not working properly in Pandas-CodePudding

I have a very large dataset, and I am applying multiple filters on many columns. In order to make the code more readable, I assign the filters to some variables - but I noticed that although the values in the dataframe have changed, the filter seems like doesn't take into account the new values.

This is my dataframe:

data = {'id':[12, 84, 156, 228, 300, 372, 444, 516, 588, 660, 732],
       'age':['18-18', '22-22', '35-35', '33-33', '45-45', '40-40', '55-55', '60-60', '47-47', '25-25', '59-59'],
       'height':['175-177', '165-167', '175-178', '165-168', '175-179', '165-169', '175-180', '165-170', '175-181', '165-171', '175-182'],
       'weight':['65-70', '65-70', '80-85', '75-80', '90-95', '100-105', '80-85', '70-75', '70-75', '85-90', '90-95'],
       'education':['10-12', '11-13', '12-14', '13-15', '14-16', '15-17', '16-18', '17-19', '18-20', '19-21', '20-22'],
       'employment':['1-4', '8-11', '8-11', '4-7', '5-8', '5-8', '9-12', '15-18', '13-16', '12-15', '12-15'],
       'country':['France-EU', 'Austria-EU', 'Netherland-EU', 'Italy-EU', 'Texas-US', 'California-US', 'Washington-US', 'Poland-EU', 'Spain-EU', 'Greece-EU', 'New York-US'],
       'city':['Paris-FR', 'Vienna-AUS', 'Amsterdam-NL', 'Rome-ITA', 'Austin-TX', 'LA-CAL', 'Olympia-WAS', 'Warsaw-PL', 'Madrid-SPA', 'Athens-GR', 'Albany-NY']}

df = pd.DataFrame(data)

And I want to apply this filter:

`df['weight'] = df['weight'].astype(str)
filter1 = (df['weight'].str.slice(stop=2)=='65') & (df['country'].str.slice(stop=2)=='Au')`

Initially, I get what I want using the filter:

df.loc[filter1]

Later, I change the filtered rows as follows:

df.loc[filter1,'weight'] = '100'

And when I use again the filter I expect no result, but instead it returns me the same rows, although the value of the filter should be False

CodePudding user response：

filter1 doesn't magically update to match values that you set after it is created... make it again after your changes and you'll see that it works as expected:

def get_filter1(df):
    return df['weight'].str[:2].eq('65') & df['country'].str[:2].eq('Au')


print(df.loc[get_filter1(df)])

df.loc[get_filter1(df), 'weight'] = '100'

print(df.loc[get_filter1(df)])

Output:

   id    age   height weight education employment     country        city
1  84  22-22  165-167  65-70     11-13       8-11  Austria-EU  Vienna-AUS

Empty DataFrame
Columns: [id, age, height, weight, education, employment, country, city]
Index: []