Pandas - Function to remove na

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trying to do a quick function but struggling since new to Pandas/Python. I'm trying to remove nas from two of my columns, but I keep getting this error, my code is the following:

def remove_na():
    df.dropna(subset=['Column 1', 'Column 2'])
    df.reset_index(drop=True)

df = remove_rows()
df.head(3)

AttributeError: 'NoneType' object has no attribute 'dropna'

I want to use this function on different tables, hence why I thought it would make sense to create a method. However, I just don't understand why it's not working for this method when compared to others it seems fine. Thank you.

I believe you can specify if you want to remove NA from columns or rows by the paremeter axis where 0 is index and 1 is columns. This would remove all NAs from all columns

df.dropna(axis =1, inplace=True ) 

How to remove NaN from a Pandas Series in Python, How do you drop a row with missing values in Python? Pandas dropna () is an inbuilt DataFrame function that is used to remove rows and columns with Null/None/NA values from DataFrame. Pandas dropna () method returns the new DataFrame, and the source DataFrame remains unchanged.

I think you can use apply with dropna:

df = df.apply(lambda x: pd.Series(x.dropna().values))
print (df)

OR you can also try this

df=df.dropna(axis=0, how='any')

Python, () method allows the user to analyze and drop Rows/Columns with Null values in different ways. Parameters: axis: axis takes int or string value for rows/columns. Input can be 0 or 1 for Integer and 'index' or 'columns' for String. Require that many non-NA values. subset array-like, optional. Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include. inplace bool, default False. If True, do operation inplace and return None. Returns DataFrame. DataFrame with NA entries dropped from it.

You're getting an error cos the dropna function here yields a dataframe as its output. You can either save it to a dataframe:

df = df.dropna(subset=['Column 1', 'Column 2'])

or call the argument 'inplace=True' :

df.dropna(subset=['Column 1', 'Column 2'], inplace=True)

Remove missing values. See the User Guide for more on which values are considered missing, and how to work with missing data. Parameters. axis{0 or '​index',  Pandas DataFrame dropna() function is used to remove rows and columns with Null/NaN values. By default, this function returns a new DataFrame and the source DataFrame remains unchanged. We can create null values using None, pandas.NaT, and numpy.nan variables.

In order to remove all the missing values from the data set at once using pandas you can use the following:(Remember You have to specify the index in the arguments so that you can efficiently remove the missing values)

# making new data frame with dropped NA values 
 new_data = data.dropna(axis = 0, how ='any') 

I believe you can specify if you want to remove NA from columns or rows by the paremeter axis where 0 is index and 1 is columns. This would  Pandas DataFrame - dropna() function: The dropna() function is used to remove missing values.

In the next section, I'll review the steps to apply the above syntax in practice. Steps to Drop Rows with NaN Values in Pandas DataFrame. Step 1: Create a  Python’s pandas library provides a function to remove rows or columns from a dataframe which contain missing values or NaN i.e.

Pandas DataFrame dropna() function is used to remove rows and columns with Null/NaN values. By default, this function returns a new DataFrame and the source DataFrame remains unchanged. We can create null values using None, pandas. NaT, and numpy. Python pandas has 2 inbuilt functions to deal with missing values in data. Those are fillna or dropna. We can replace the null by using mean or medium functions data. Or we will remove the data

Pandas dropna() method allows the user to analyze and drop Rows/Columns with thresh takes integer value which tells minimum amount of na values to drop. pandas.DataFrame.isna. ¶. DataFrame.isna(self) → 'DataFrame' [source] ¶. Detect missing values. Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values.