pandas filter non nan
filtered_df = df[df['name'].notnull()]
remove rows or columns with NaN value
df.dropna() #drop all rows that have any NaN values
df.dropna(how='all')
clean nas from column pandas
>>> df.dropna()
name toy born
1 Batman Batmobile 1940-04-25
handling missing dvalues denoted by a '?' in pandas
# Making a list of missing value typesmissing_values = ["n/a", "na", "--"]df = pd.read_csv("property data.csv", na_values = missing_values)
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