pandas dropna specific column
df.dropna(subset=['column1','column2'], inplace=True)drop if nan in column pandas
df = df[df['EPS'].notna()]remove rows or columns with NaN value
df.dropna() #drop all rows that have any NaN values
df.dropna(how='all')Source:stackoverflow.com
dropna for specific column pandas
df = df[df['EPS'].notna()] #instead of dropping, take another DF with non-na valuesdropna in specific column pandas
df.dropna(subset = ['column1', 'column2'], inplace = True)Returns a new DataFrame omitting rows with null values
# Returns a new DataFrame omitting rows with null values
df4.na.drop().show()
# +---+------+-----+
# |age|height| name|
# +---+------+-----+
# | 10| 80|Alice|
# +---+------+-----+Source: spark.apache.org