(df, solutions=True)
return df_
Function: count_solutions
    Summary: Count the number of solutions of each problem and language
    Examples: Iam tired...
    Attributes:
        @param (df): pd.DataFrame
    Returns: pd.DataFrame
Function: count_solutions
    Summary: Count the number of solutions of each problem and language
    Examples: Iam tired...
    Attributes:
Function : count_solutions Summary : Count the number of solutions of each problem and language Examples : Iam tired ... Attributes :
def count_solutions(df, solutions=True):
    """
    Function: count_solutions
    Summary: Count the number of solutions of each problem and language
    Examples: Iam tired...
    Attributes:
        @param (df): pd.DataFrame
    Returns: pd.DataFrame
    """
    df = df.dropna(axis=1, how='all')  # columns all nan
    df = df.dropna(how='all')  # rows all nan
    df_ = pd.DataFrame()
    df_ = df.applymap(lambda x: len(x) if x is not np.NAN else 0)

    if len(df.columns) > 1 and solutions:
        df_["Solutions"] = df_[df_.columns].apply(tuple, axis=1).map(sum)
        df_ = df_[df_.Solutions > 0]

    return df_
def count_solutions ( df , solutions = True ) : df = df . dropna ( axis = 1 , how = 'all' ) # columns all nan df = df . dropna ( how = 'all' ) # rows all nan df_ = pd . DataFrame ( ) df_ = df . applymap ( lambda x : len ( x ) if x is not np . NAN else 0 ) if len ( df . columns ) > 1 and solutions : df_ [ "Solutions" ] = df_ [ df_ . columns ] . apply ( tuple , axis = 1 ) . map ( sum ) df_ = df_ [ df_ . Solutions > 0 ] return df_