import csv
from statistics import mean, stdev, median, mode

class SimpleFrame():
    def __init__(self, filename):
        self.filename = filename
    
    def read_data(self):
        '''
        Reads and opens the data
        '''
        f = open(self.filename,"r")
        self.data = list(csv.reader(f))
        self.columns = self.data[0]
    
    def head(self):
        '''
        Displays the first five rows
        '''
        return self.data[:5]
        
    
    def shape(self):
        num_rows = 0
        for row in self.data:
            num_rows += 1
        
        num_cols = len(self.data[0])
        return [num_rows, num_cols]
    
    def new_column(self, column_name):
        for pos, d in enumerate(self.data):
            if pos == 0:
                d.append(column_name)
            else:
                d.append('NA')
    
    def apply(self, column_name, new_value):
        for pos, col in enumerate(self.data[0]):
            if col == column_name:
                column_index = pos
        
        for data in self.data[1:]:
            data[column_index] = new_value
    
    def subset(self, column_name, row_value):
        for pos, col in enumerate(self.data[0]):
            if col == column_name:
                column_index = pos
        
        print(column_index)
        subset_data = []
        for data in self.data[1:]:
            if row_value in data:
                subset_data.append(data[column_index])
        return subset_data

    
    def summary_stats(self, column_name):
        for pos, col in enumerate(self.data[0]):
            if col == column_name:
                column_index = pos

        num_data = [data[column_index] for data in self.data[1:]]
        m = statistics.mean(num_data)
        std = stdev(num_data)
        median = statistics.median(num_data)
        
        print("Mean is {mean}".format(mean= m))
        print("Standard Deviation is {std}".format(std= std))
        print("Median is {median}".format(median= median))
        
            
    def minimum(self, column):
        for pos, col in enumerate(self.data[0]):
            if col == column:
                column_index = pos

        ## Find min value
        col_data = []
        for row in self.data[1:]:
            col_data.append([row[1],row[2],row[column_index]])
        
        return min(col_data, key= lambda x: x[2])
    
    def maximum(self, column):
        for pos, col in enumerate(self.data[0]):
            if col == column:
                column_index = pos
        ## Find min value
        col_data = []
        for row in self.data[1:]:
            col_data.append([row[1],row[2],row[column_index]])
        return max(col_data, key= lambda x: x[2])
    
s = SimpleFrame("music_data.csv")
s.read_data()

s.shape()
s.columns
s.new_column('hello')
s.subset("Artist","Shakira")
print(s.maximum("Streams"))
print(s.minimum("Streams"))