import h5py
import pandas as pd
import numpy as np
import plotly.graph_objects as go

f = h5py.File('product1.h5','r')
list(f.keys())
for key in f.keys():
    print(f[key])
    df = pd.read_hdf('product1.h5', key='df')
    df = df._append(pd.read_hdf('product1.h5', key))
    # print(f[key].shape)
    # print(f[key].value)
# df = pd.read_hdf('product1.h5', key='df7')
print(df)
'''
# Open the HDF5 file
with h5py.File('product1.h5', 'r') as file:
    dataframes=pd.DataFrame(columns=['Product ID', 'Zip Code', 'Shown Price', 'Minimum Quantity', 'Total Price', 'Unit Price','Sales Count','Sold Status'])  
    for key in file.keys():
        # Read the dataframe from the HDF5 file
        dataframe = pd.read_hdf('product1.h5', key)

        # Append the dataframe to the list
        dataframes._append(dataframe)

# Print the number of dataframes read from the HDF5 file
print(f"Read {len(dataframes)} dataframes from the HDF5 file")

item_std_devs = {}
for dataframe in dataframes:
    item_name = dataframe['Product ID'].iloc[0]
    std_dev = np.std(dataframe['Unit Price'])
    item_std_devs[item_name] = std_dev

# Print the item names and their corresponding standard deviations
for item_name, std_dev in item_std_devs.items():
    print(f"Item: {item_name}, Std Dev: {std_dev}")

# Prepare the data for the graph
x = list(item_std_devs.keys())
y = list(item_std_devs.values())

# Create a bar graph
fig = go.Figure(data=[go.Bar(x=x, y=y)])

# Customize the graph
fig.update_layout(
    title="Standard Deviation of Pricing for Each Item",
    xaxis_title="Item",
    yaxis_title="Standard Deviation",
)

# Save the graph as an HTML file
report_file = 'test2.html'
fig.write_html(report_file)



'''
# Calculate the standard deviation using pandas' function
std_dev_unit = df.groupby('Product ID')['Unit Price'].std()
std_dev_displayed = df.groupby('Zip Code')['Shown Price'].std()

# Generate the graph using Plotly
fig = go.Figure()
fig.add_trace(go.Bar(x=std_dev_unit.index, y=std_dev_unit.values))
fig.update_layout(title='Standard Deviation of the Unit Pricing by Item', xaxis_title='Product ID', yaxis_title='Standard Deviation')

report_file = 'test2.html'
fig.write_html(report_file)