import numpy as np

### Read the hdb resale price index csv file with the loadtxt() function
hdbrpi = "CA1data/housing-and-development-board-resale-price-index-1q2009-100-quarterly.csv"
data = np.genfromtxt(hdbrpi, delimiter=",", skip_header=1, dtype=[('quarter', 'U50'), ('index', 'U50')])

### Print out total rows and columns of data in the file
print("***HDB Resale Price Index***")
print()
print(f"There are {len(data)} rows and {len(data[0])} columns of data in this dataset {hdbrpi}")
print()

### Print out the names of the columns in the file
print("The names of the columns are:")

with open(hdbrpi) as data:
    data = np.genfromtxt(hdbrpi, delimiter=",", skip_header=1, dtype=[('quarter', 'U50'), ('index', 'U50')])
    line_count = 0
    for line_count in data:
        if line_count >= 0:
            print(row[line_count], type(row[line_count]) , "isnumeric:", row[line_count].isnumeric())
            #unique_elements, counts_elements = np.unique(data, return_counts=True)
            #print(unique_elements, counts_elements)
            #print(np.unique(row[line_count], return_counts = true))
        line_count += 1
        print(line_count)
 

import pandas as pd
data = pd.read_csv('path_to_your_csv_file.csv')
uniques_by_column = {col: len(data.loc[:, col].unique()) for col in data.columns}
print(uniques_by_column) # this dictionary contains the number of unique values in each column

import numpy as np
data = np.loadtxt('path_to_your_csv_file.csv', delimiter=',') # check delimiter
# data assumed to be 2D array
uniques_by_column = {j: len(np.unique(data[:, j])) for j in range(data.shape[-1])}