Pandas - Filtering None Values

Question

I'm using Pandas to explore some datasets. I have this dataframe:

I want to exclude any row that has a city value. So I've tried:

new_df = all_df[(all_df["City"] == "None") ]
new_df

But then I got an empty dataframe:

It works whenever I use any value other than None. Any idea how to filter this dataframe?

Solution 1

Consider using isnull() to locate missing values

all_df[all_df['City'].isnull()]

Solution 2

Try this to select only the None values for city column:

new_df = all_df['City'][all_df['City'] == "None"]

Try this to see all other columns which has the same rows of 'City'==None

new_df = all_df[all_df['City'] == "None"]
print(new_df.head()) # with function head() you can see the first 5 rows

Comments

[Shadin]

Thanks! but when i print the new df i get: Series([], Name: City, dtype: object)

[Shadin]

How can i print it properly?

[imanzabet]

Ok, I think I got your question. You need to print all other columns as well for city==None. I have edited the codes. Hope it will be helpful

[fstang]

To exclude those rows: all_df[~all_df['City'].isnull()]

[tarashypka]

@fstang You can also use notnull: all_df[all_df['City'].notnull()]

[Nabin]

How about getting all that is not None?

[BND]

@Nabin all_df[~all_df['City'].isnull()]

[Olsgaard]

isnull() returns True for both None and NaN values.