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import io
import time
import pandas as pd
import streamlit as st
@st.cache_data
def convert_dfs(dfs: list[pd.DataFrame], sheet_names: list[str]) -> bytes:
# IMPORTANT: Cache the conversion to prevent computation on every rerun
# Create a BytesIO object
bytes_io = io.BytesIO()
# Write the dataframes to the BytesIO object
with pd.ExcelWriter(bytes_io, engine="xlsxwriter") as writer:
for df, sheet_name in zip(dfs, sheet_names):
df.to_excel(writer, sheet_name=sheet_name, index=False)
# Get the bytes data
bytes_data = bytes_io.getvalue()
# Close the BytesIO object
bytes_io.close()
return bytes_data
# def save_dataframes(dfs: list[pd.DataFrame], sheet_names: list[str], folder_path: str):
# """
# Save the dataframes to an excel file. The excel file will be saved in the
# folder_path directory.
# Args:
# dfs (list[pd.DataFrame]): The list of dataframes to save.
# sheet_names (list[str]): The list of names for each sheet.
# folder_path (str): The path to the folder where the excel file will be saved.
# """
# bytes_data = convert_dfs(dfs, sheet_names)
# timestamp = int(time.time())
# file_name = f"{folder_path}/data_{timestamp}.xlsx"
# with open(file_name, "wb") as f:
# f.write(bytes_data)
def save_dataframe(df: pd.DataFrame, sheet_name: str):
"""
Save the dataframe to a csv file.
Args:
df (pd.DataFrame): The dataframe to save.
sheet_name (str): The name of the sheet.
"""
df.to_csv(f"data2/{sheet_name}_{time.time()}.csv", index=False)
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