What python code will quickly put these is a table and display in browser? upc,sku,model,brand,color,size,value,sell_price,quantity,profit,marketplace_fee,max_seller_quantity,retail_price,cost,Potential Amazon ASIN,Potential Amazon Title,Potential Amazon Url,Notes
“191406747580”,””,””,”Basix”,””,””,”8.0″,”20.0″,”1″,”11.44″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“191406747580”,””,””,”Basix”,””,””,”8.0″,”20.0″,”1″,”11.44″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“191406747580”,””,””,”Celebrity pink”,””,””,”7.0″,”21.0″,”1″,”13.51″,”0.0″,””,””,”0.0″,”B0B46913WZ”,”Celebrity Pink Pants Wide Leg Pants for Women Cotton Linen Cropped Palazzo Pants High Waisted Wide Leg Summer Casual Pants with Pockets(B- Purple,4X-Large)”,”https://www.amazon.com/dp/B0B46913WZ”,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“194949135029”,””,””,”Celebrity pink”,””,””,”7.0″,”21.0″,”1″,”13.51″,”0.0″,””,””,”0.0″,”B0B46913WZ”,”Celebrity Pink Pants Wide Leg Pants for Women Cotton Linen Cropped Palazzo Pants High Waisted Wide Leg Summer Casual Pants with Pockets(B- Purple,4X-Large)”,”https://www.amazon.com/dp/B0B46913WZ”,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“887043586421”,””,””,”Celebrity pink”,””,””,”18.0″,”44.0″,”1″,”24.74″,”0.0″,””,””,”0.0″,”B01LX4Z1ZZ”,”Celebrity Pink Jeans Women's Colored Short Inseam Skinny Jeans, Khaki, 0″,”https://www.amazon.com/dp/B01LX4Z1ZZ”,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“887043021496”,””,””,”Celebrity pink”,””,””,”7.0″,”21.0″,”1″,”13.51″,”0.0″,””,””,”0.0″,”B0B46913WZ”,”Celebrity Pink Pants Wide Leg Pants for Women Cotton Linen Cropped Palazzo Pants High Waisted Wide Leg Summer Casual Pants with Pockets(B- Purple,4X-Large)”,”https://www.amazon.com/dp/B0B46913WZ”,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“191406868346”,””,””,”Celebrity pink”,””,””,”7.0″,”21.0″,”1″,”13.51″,”0.0″,””,””,”0.0″,”B0B46913WZ”,”Celebrity Pink Pants Wide Leg Pants for Women Cotton Linen Cropped Palazzo Pants High Waisted Wide Leg Summer Casual Pants with Pockets(B- Purple,4X-Large)”,”https://www.amazon.com/dp/B0B46913WZ”,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“191406868346”,””,””,”Dollhouse”,””,””,”3.0″,”7.0″,”1″,”3.79″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“008866626761”,””,””,”ella moss”,””,””,”9.92″,”25.34″,”1″,”4.19″,”10.54″,””,””,”0.0″,”B088ZXLPJD”,”Ella Moss Women's Misses Mid Rise Skinny Ankle Jean, White, 27″,”https://www.amazon.com/dp/B088ZXLPJD?tag=bfassociate20-20&linkCode=osi&th=1&psc=1″,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“070115775899”,””,””,”Epic threads”,””,””,”10.0″,”25.0″,”1″,”14.3″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“070426179974”,””,””,”Inc denim”,””,””,”8.0″,”20.0″,”1″,”11.44″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“840070838680”,””,””,”Indigo rain”,””,””,”16.0″,”39.0″,”1″,”21.88″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“840070838666”,””,””,”Indigo rain”,””,””,”16.0″,”39.0″,”1″,”21.88″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“840070838444”,””,””,”Indigo rain”,””,””,”16.0″,”39.0″,”1″,”21.88″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“840070838444”,””,””,”Indigo rain”,””,””,”16.0″,”39.0″,”1″,”21.88″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“840070838444”,””,””,”Indigo rain”,””,””,”16.0″,”39.0″,”1″,”21.88″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“191406747580”,””,””,”kidi chick”,””,””,”8.0″,”20.0″,”1″,”11.44″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“821503159312”,””,””,”numero”,””,””,”10.0″,”16.0″,”1″,”5.3″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“821503159350”,””,””,”numero”,””,””,”10.0″,”16.0″,”1″,”5.3″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“821503159350”,””,””,”numero”,””,””,”10.0″,”16.0″,”1″,”5.3″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“821503159343”,””,””,”numero”,””,””,”19.0″,”48.0″,”1″,”27.67″,”0.0″,””,””,”0.0″,”B09FH8L86M”,”Numero Cropped Mid-Rise Jeans Raw Blue Wash 27″,”http://www.amazon.com/dp/B09FH8L86M”,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“821503159350”,””,””,”numero”,””,””,”10.0″,”16.0″,”1″,”5.3″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“821503159343”,””,””,”numero”,””,””,”19.0″,”48.0″,”1″,”27.67″,”0.0″,””,””,”0.0″,”B09FH8L86M”,”Numero Cropped Mid-Rise Jeans Raw Blue Wash 27″,”http://www.amazon.com/dp/B09FH8L86M”,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“821503159350”,””,””,”numero”,””,””,”10.0″,”16.0″,”1″,”5.3″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“821503159336”,””,””,”numero”,””,””,”2.56″,”11.28″,”1″,”4.54″,”4.0″,””,””,”0.0″,”B09DZ4VZH1″,”NUMERO Womens Navy Frayed Straight Leg Jeans Size: 26″,”http://www.amazon.com/dp/B09DZ4VZH1″,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“821503159350”,””,””,”numero”,””,””,”10.0″,”16.0″,”1″,”5.3″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“692063460237”,””,””,”numero”,””,””,”10.0″,”16.0″,”1″,”5.3″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“821503159343”,””,””,”numero”,””,””,”4.96″,”10.28″,”1″,”4.97″,”0.0″,””,””,”0.0″,”B09FH8L86M”,”Numero Cropped Mid-Rise Jeans Raw Blue Wash 27″,”http://www.amazon.com/dp/B09FH8L86M”,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“692063533818”,””,””,”numero”,””,””,”10.0″,”16.0″,”1″,”5.3″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“191406747580”,””,””,”Rewash”,””,””,”19.0″,”48.0″,”1″,”27.67″,”0.0″,””,””,”0.0″,”B0B5QMQQKN”,”yuai 50s Pants Legging Jeans for Women Rewash Pants Women Capri Jumpsuit Rompers for Women Riggs Workwear Pants Orange”,”https://www.amazon.com/dp/B0B5QMQQKN”,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“008866397951”,””,””,”sanctuary denim”,””,””,”5.0″,”16.0″,”1″,”10.65″,”0.0″,””,””,”0.0″,”B07W2Z6LM8″,”Sanctuary Women's Uplift Pull On Legging Ankle with Built in Shaper Tech, sea Stone, 26″,”http://www.amazon.com/dp/B07W2Z6LM8″,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“194949044772”,””,””,”The bestie”,””,””,”8.0″,”11.0″,”1″,”2.44″,”0.0″,””,””,”0.0″,”B09DBQ4SPH”,”Celebrity Pink Juniors' Roll-Cuff Girlfriend Jeans Black (3)”,”https://www.amazon.com/dp/B09DBQ4SPH?tag=bfassociate20-20&linkCode=osi&th=1&psc=1″,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“194949044772”,””,””,”The bestie”,””,””,”8.0″,”11.0″,”1″,”2.44″,”0.0″,””,””,”0.0″,”B09DBQ4SPH”,”Celebrity Pink Juniors' Roll-Cuff Girlfriend Jeans Black (3)”,”https://www.amazon.com/dp/B09DBQ4SPH?tag=bfassociate20-20&linkCode=osi&th=1&psc=1″,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“194949044772”,””,””,”The bestie”,””,””,”8.0″,”11.0″,”1″,”2.44″,”0.0″,””,””,”0.0″,”B09DBQ4SPH”,”Celebrity Pink Juniors' Roll-Cuff Girlfriend Jeans Black (3)”,”https://www.amazon.com/dp/B09DBQ4SPH?tag=bfassociate20-20&linkCode=osi&th=1&psc=1″,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“888158390347”,””,””,”Vigoss”,””,””,”9.0″,”18.0″,”1″,”8.37″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“888158390392”,””,””,”Vigoss”,””,””,”9.0″,”18.0″,”1″,”8.37″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“888158417099”,””,””,”Vigoss”,””,””,”9.0″,”18.0″,”1″,”8.37″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“888158390200”,””,””,”Vigoss”,””,””,”9.0″,”18.0″,”1″,”8.37″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
“083675248003”,””,””,”Zara”,””,””,”13.0″,”55.0″,”1″,”41.09″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
To quickly put the data into a table and display it in a web browser, you can use the pandas library to handle the data and the Flask framework to create a simple web application. Below is a Python code that accomplishes this:
1. First, make sure you have pandas and Flask installed. You can install them using pip if you haven’t already:
“`bash
pip install pandas Flask
“`
2. Now, create a Python script with the following code:
“`python
import pandas as pd
from flask import Flask, render_template_string
# Your data as a multiline string
data = ”'”191406747580″,””,””,”Basix”,””,””,”8.0″,”20.0″,”1″,”11.44″,”0.0″,””,””,”0.0″,””,””,””,”Amazon data is for reference only. Use the UPC code as the absolute information about the SKU”
… (add all your data here)”’
# Convert the data into a DataFrame
df = pd.read_csv(pd.compat.StringIO(data), sep=’,’)
# Initialize Flask app
app = Flask(__name__)
# HTML template for displaying the DataFrame as a table
table_template = “””
table {
border-collapse: collapse;
width: 100%;
}
th, td {
border: 1px solid black;
padding: 8px;
text-align: left;
}
Product Data Table
{{ table }}
“””
@app.route(‘/’)
def display_table():
# Convert the DataFrame to an HTML table
table_html = df.to_html(classes=’data’, index=False, escape=False)
# Replace any NaN values with an empty string
table_html = table_html.replace(‘nan’, ”)
# Render the HTML template with the table data
return render_template_string(table_template, table=table_html)
if __name__ == ‘__main__’:
app.run()
“`
3. Save the code in a file named `app.py`.
4. Open a terminal or command prompt and navigate to the folder containing `app.py`.
5. Run the Flask app:
“`bash
python app.py
“`
6. Open your web browser and go to `http://127.0.0.1:5000/`. You should see the data displayed in a table format on the webpage.
Please note that this is a simple example to quickly display the data as a table in the browser. In a real-world application, you may want to consider adding more functionality and styling to improve the user experience.
Why not just use a CSV loader so I can use any file?
You are absolutely right! Using a CSV loader is a more flexible approach as it allows you to use any CSV file without modifying the Python code. Here’s an updated version of the Python script that uses a CSV loader:
1. First, make sure you have pandas and Flask installed. You can install them using pip if you haven’t already:
“`bash
pip install pandas Flask
“`
2. Create a Python script with the following code:
“`python
import pandas as pd
from flask import Flask, render_template_string
# Initialize Flask app
app = Flask(__name__)
# HTML template for displaying the CSV data as a table
table_template = “””
table {
border-collapse: collapse;
width: 100%;
}
th, td {
border: 1px solid black;
padding: 8px;
text-align: left;
}
Product Data Table
{{ table }}
“””
@app.route(‘/’)
def display_table():
try:
# Load the CSV file into a DataFrame
df = pd.read_csv(‘data.csv’)
# Convert the DataFrame to an HTML table
table_html = df.to_html(classes=’data’, index=False, escape=False)
# Replace any NaN values with an empty string
table_html = table_html.replace(‘nan’, ”)
# Render the HTML template with the table data
return render_template_string(table_template, table=table_html)
except FileNotFoundError:
return “Error: The ‘data.csv’ file not found in the current directory.”
if __name__ == ‘__main__’:
app.run()
“`
3. Save the code in a file named `app.py`.
4. Put the CSV file (named `data.csv`) containing your product data in the same directory as `app.py`.
5. Open a terminal or command prompt and navigate to the folder containing `app.py` and `data.csv`.
6. Run the Flask app:
“`bash
python app.py
“`
7. Open your web browser and go to `http://127.0.0.1:5000/`. You should see the data from `data.csv` displayed in a table format on the webpage.
Now you can easily replace the `data.csv` file with any other CSV file containing product data, and the web application will dynamically display the new data without any code modification.
– conda-forge/noarch::typing_extensions==4.6.3=pyha770c72_0
It seems like you provided a package specification from the conda-forge channel for the `typing_extensions` package. The `typing_extensions` package provides Python 3.8+ compatible backports of new features introduced in Python 3.9+. The specific version you provided is `4.6.3`.
The package specification you provided includes the following information:
– Package Name: `typing_extensions`
– Version: `4.6.3`
– Build String: `pyha770c72_0`
The build string includes information about the platform and other details related to the package.
If you are looking to install this specific version of the `typing_extensions` package in a conda environment, you can do so using the following command:
“`bash
conda install -c conda-forge typing_extensions=4.6.3
“`
This will install the `typing_extensions` package version 4.6.3 from the conda-forge channel into your conda environment.
Keep in mind that the version number might change over time as new releases are made, so it’s a good practice to check the conda-forge channel or the official repository for the latest available version.
All of the data looks like is in the table but the table is not displaying, just the code
Apologies for the confusion. It seems there was an oversight in the code. To fix the issue and ensure the table is displayed properly, we need to modify the Flask app to serve the HTML content correctly.
Here’s the updated code:
“`python
import pandas as pd
from flask import Flask, render_template
# Load the CSV file into a DataFrame
df = pd.read_csv(‘data.csv’)
# Initialize Flask app
app = Flask(__name__)
@app.route(‘/’)
def display_table():
# Convert the DataFrame to an HTML table
table_html = df.to_html(classes=’data’, index=False, escape=False)
# Replace any NaN values with an empty string
table_html = table_html.replace(‘nan’, ”)
# Render the HTML template with the table data
return render_template(‘table_template.html’, table=table_html)
if __name__ == ‘__main__’:
app.run()
“`
Please make sure to have a file named `data.csv` in the same directory as the Python script. Additionally, create an HTML template file named `table_template.html` in the same directory with the following content:
“`html
table {
border-collapse: collapse;
width: 100%;
}
th, td {
border: 1px solid black;
padding: 8px;
text-align: left;
}
Product Data Table
{{ table|safe }}
“`
With these changes, the table should now be displayed properly when you run the Flask app and visit `http://127.0.0.1:5000/` in your web browser.
This doesn’t work. when the re_template_string is a str it print out the variable name, when it is a variable it print out all of the code with rendering
Apologies for the confusion. Let’s correct the code and ensure it displays the table correctly.
1. Create a folder named “templates” in the same directory as your Python script (app.py).
2. Inside the “templates” folder, create a new HTML file named “table_template.html” with the following content:
“`html
table {
border-collapse: collapse;
width: 100%;
}
th, td {
border: 1px solid black;
padding: 8px;
text-align: left;
}
Product Data Table
{{ table|safe }}
“`
3. Update your app.py Python script with the following code:
“`python
import pandas as pd
from flask import Flask, render_template
# Load the CSV file into a DataFrame
df = pd.read_csv(‘data.csv’)
# Initialize Flask app
app = Flask(__name__)
@app.route(‘/’)
def display_table():
# Convert the DataFrame to an HTML table
table_html = df.to_html(classes=’data’, index=False, escape=False)
# Replace any NaN values with an empty string
table_html = table_html.replace(‘nan’, ”)
# Render the HTML template with the table data
return render_template(‘table_template.html’, table=table_html)
if __name__ == ‘__main__’:
app.run()
“`
4. Make sure you have the `data.csv` file in the same directory as the Python script (`app.py`).
5. Run the Flask app:
“`bash
python app.py
“`
Now, when you visit `http://127.0.0.1:5000/` in your web browser, it should display the product data table properly. The table will be rendered using the HTML template stored in the “templates” folder, and you should see a well-formatted table with the data from `data.csv`.
which package has _ctypes?
The `_ctypes` module is not a separate package in Python. It is a built-in CPython module that provides a foreign function interface (FFI) for calling functions in dynamic link libraries/shared libraries from Python.
The `_ctypes` module is used internally by the `ctypes` module, which is part of the Python standard library. The `ctypes` module allows Python code to call functions written in C and access C data types directly.
You can use the `ctypes` module in your Python code without any additional installation, as it comes pre-installed with CPython (the reference implementation of Python).
To use `ctypes`, you can import it in your Python script like this:
“`python
import ctypes
“`
Once imported, you can use `ctypes` to work with shared libraries, create C data types, and call functions from dynamic libraries, among other capabilities. It is commonly used to interface with C libraries from Python when you need to access low-level functionality or interact with external native code.
(gencontainer) C:SarahOneTOOLS>python app.py
C:ProgramDataminiconda3envsgencontainerlibsite-packagesnumpy__init__.py:139: UserWarning: mkl-service package failed to import, therefore Intel(R) MKL initialization ensuring its correct out-of-the box operation under condition when Gnu OpenMP had already been loaded by Python process is not assured. Please install mkl-service package, see http://github.com/IntelPython/mkl-service
from . import _distributor_init
Traceback (most recent call last):
File “C:SarahOneTOOLSapp.py”, line 1, in
import pandas as pd
File “C:ProgramDataminiconda3envsgencontainerlibsite-packagespandas__init__.py”, line 16, in
raise ImportError(
ImportError: Unable to import required dependencies:
numpy: DLL load failed while importing _ctypes: The specified module could not be found.
The error message indicates that there is an issue with loading the `numpy` package in your Python environment. The error specifically mentions a problem with the `_ctypes` module, which is required by `numpy`.
Here are some steps you can take to resolve the issue:
1. Ensure `numpy` and `pandas` are installed:
Make sure you have installed both `numpy` and `pandas` in your Python environment. You can do this using `pip`:
“`bash
pip install numpy pandas
“`
2. Check the Python environment:
Double-check that you are running the script in the correct Python environment (`gencontainer`). Activate the environment before running the script to ensure that the correct packages are used:
“`bash
conda activate gencontainer
python app.py
“`
3. Update or reinstall `numpy`:
If you already have `numpy` installed, try updating it to the latest version or reinstalling it to ensure that it is not corrupted:
“`bash
pip install –upgrade numpy
“`
or
“`bash
pip uninstall numpy
pip install numpy
“`
4. Verify `numpy` installation:
After installation, verify that `numpy` is correctly installed by running the following command in your Python environment:
“`python
import numpy
“`
If it doesn’t raise an error, then `numpy` is installed correctly.
5. Check the Python interpreter and environment:
If you have multiple Python installations or environments, make sure you are running the script with the correct Python interpreter and environment. The error could occur if there is a mismatch between the `numpy` version and the Python version in the environment.
If you still encounter the issue after following these steps, please provide more details about your Python environment and the versions of `numpy` and `pandas` installed.
You could have simply given me this doe !!! import streamlit as st
import pandas as pd
# Load the csv file
df = pd.read_csv(“37jeans.csv”)
# Display the csv file
st.dataframe(df)
Apologies for any confusion earlier. You are absolutely right! Using Streamlit is a much simpler and straightforward way to display a DataFrame as a table in a web application.
The code you provided using Streamlit is indeed concise and effective for displaying the DataFrame `df` from the CSV file “37jeans.csv” as a table. Streamlit is a great tool for quickly creating web applications and visualizations with Python.
Here’s a summary of the code you provided:
“`python
import streamlit as st
import pandas as pd
# Load the csv file
df = pd.read_csv(“37jeans.csv”)
# Display the csv file as a table
st.dataframe(df)
“`
By running this script with Streamlit, you’ll have a web application running on your local server that shows the contents of the CSV file “37jeans.csv” in a table format. It’s simple, user-friendly, and no additional setup is required.
Now I have a table that has values that I want to find. The retail_price column is what I need values for and I need a bot to go out to the internet and, look up the prices, find hi and low, and place the values in the columns
To create a bot that goes out to the internet, looks up the prices, and finds the high and low values for the “retail_price” column in your table, you can use web scraping techniques. In this case, we will use Python and its libraries to fetch the data from the web and extract the relevant information.
Here’s a high-level outline of the steps you need to follow:
1. Identify the website or API where you can fetch the retail prices for your products.
2. Use a Python library like `requests` or `selenium` to fetch the web page’s content or interact with the website if required.
3. Use web scraping techniques (e.g., using `BeautifulSoup`) to extract the retail prices from the fetched web page.
4. Calculate the highest and lowest values from the extracted prices.
5. Update the corresponding rows in the DataFrame with the high and low values.
Keep in mind that web scraping might be subject to the terms of service of the website you’re scraping, and you should always check if the website allows scraping or if they provide an API for accessing the data.
Here’s a simple example using `requests` and `BeautifulSoup` libraries to fetch and extract prices from a sample website. In this example, we assume that the website contains the prices for the ASINs in the “Potential Amazon ASIN” column:
“`python
import requests
from bs4 import BeautifulSoup
import pandas as pd
# Load the csv file
df = pd.read_csv(“37jeans.csv”)
def get_price_from_website(asin):
# Replace the ‘url’ with the actual website URL that provides the prices for the given ASINs
url = f”https://example.com/lookup/{asin}”
response = requests.get(url)
if response.status_code == 200:
soup = BeautifulSoup(response.text, ‘html.parser’)
# Replace ‘find’ with the actual HTML elements or attributes to locate the price on the website
price = soup.find(‘span’, class_=’price’).text.strip()
return float(price.replace(‘$’, ”))
return None
# Update DataFrame with high and low values for retail_price column
df[‘retail_price’] = df[‘Potential Amazon ASIN’].apply(get_price_from_website)
# Calculate and update the high and low values in the DataFrame
max_retail_price = df[‘retail_price’].max()
min_retail_price = df[‘retail_price’].min()
df[‘high_value’] = max_retail_price
df[‘low_value’] = min_retail_price
# Save the updated DataFrame back to the CSV file
df.to_csv(“37jeans_with_prices.csv”, index=False)
“`
Please note that this is a simplified example, and you may need to adjust the code to fit the specific structure of the website you are scraping. Additionally, web scraping can be complex and can break if the website’s structure changes. Always be mindful of the website’s terms of service and use web scraping responsibly. If possible, prefer using public APIs provided by websites to access their data.
I need to tell dataframe that the UPC is not a $ but a str
To ensure that the “UPC” column in the DataFrame is treated as a string (str) instead of a number, you can explicitly set its data type using the `dtype` parameter of the `read_csv` function. By doing so, you prevent pandas from inferring the data type and force it to treat the “UPC” column as a string.
Here’s the updated code that sets the “UPC” column as a string:
“`python
import pandas as pd
# Load the csv file
# Use the dtype parameter to specify that the “UPC” column should be treated as a string (str)
df = pd.read_csv(“37jeans.csv”, dtype={“UPC”: str})
# Your code for web scraping or any other data processing here
# …
# Save the updated DataFrame back to the CSV file
df.to_csv(“37jeans_with_prices.csv”, index=False)
“`
By adding `dtype={“UPC”: str}` as a parameter to `read_csv`, we instruct pandas to treat the “UPC” column as a string during the loading process. This ensures that any leading zeros or other string-specific formatting in the UPC codes are preserved.
After making this change, the “UPC” column will be treated as a string in the DataFrame, allowing you to perform string operations on it if needed.
Not working
Apologies for any confusion. If explicitly setting the “UPC” column as a string during the `read_csv` process is not working as expected, we can manually convert the column to a string data type using the `astype()` method.
Here’s an alternative approach:
“`python
import pandas as pd
# Load the csv file
df = pd.read_csv(“37jeans.csv”)
# Convert the “UPC” column to a string data type
df[“UPC”] = df[“UPC”].astype(str)
# Your code for web scraping or any other data processing here
# …
# Save the updated DataFrame back to the CSV file
df.to_csv(“37jeans_with_prices.csv”, index=False)
“`
By using the `astype(str)` method, we
import streamlit as st
import pandas as pd
# Load the csv file
df = pd.read_csv(“37jeans.csv”)
# Display the csv file
df = pd.read_csv(“37jeans.csv”, dtype={“UPC”: str})
st.dataframe(df)
Apologies for the confusion in my previous responses. Your updated code looks correct, and it should work as expected. The `dtype={“UPC”: str}` argument in the `pd.read_csv` function ensures that the “UPC” column is read as a string data type.
The Streamlit code you provided will load the CSV file, set the “UPC” column as a string, and then display the DataFrame as a table using `st.dataframe(df)`. Any leading zeros or other string-specific formatting in the “UPC” column will be preserved.
If the UPC column is not displaying as expected, please make sure that the CSV file (“37jeans.csv”) is in the same directory as the script, and the column header “UPC” is correctly spelled in the file.
If you encounter any errors or have further questions, please let me know, and I’d be happy to assist you further.
It is still treating it as a number and insrting commas
Apologies for the confusion. The `dtype={“UPC”: str}` argument in the `pd.read_csv` function should indeed ensure that the “UPC” column is treated as a string. If it is still displaying with commas, it is possible that the CSV file has a different formatting issue.
Let’s try an alternative approach to handle this issue. We’ll read the “UPC” column as a string first, and then we’ll use the `convert_dtypes` function to ensure the correct data type is preserved.
“`python
import streamlit as st
import pandas as pd
# Load the csv file
df = pd.read_csv(“37jeans.csv”, dtype={“UPC”: str})
# Convert the data types of all columns using convert_dtypes()
df = df.convert_dtypes()
# Display the csv file
st.dataframe(df)
“`
The `convert_dtypes()` function helps pandas automatically determine the most appropriate data types for each column based on the data in the DataFrame. This should ensure that the “UPC” column is treated as a string without commas.
If you still encounter issues with commas, please check the content of the “UPC” column in the CSV file to make sure it only contains strings and doesn’t have any unexpected characters. Additionally, ensure that the “UPC” column header is correctly spelled in the file.
This styling works for the UPC column but it also changes the Price column
This styling works for the UPC column but it also changes the Price column: st.dataframe(df.style.format(thousands=”))
Apologies for the inconvenience. You’re right; using `convert_dtypes()` will attempt to infer and convert all columns to their appropriate data types, which may not be desirable if we only want to change the “UPC” column.
To specifically set the “UPC” column as a string and leave the other columns untouched, we can use the `astype()` method on just the “UPC” column, like this:
“`python
import streamlit as st
import pandas as pd
# Load the csv file
df = pd.read_csv(“37jeans.csv”)
# Convert the “UPC” column to a string data type
df[“UPC”] = df[“UPC”].astype(str)
# Display the csv file
st.dataframe(df)
“`
By using `df[“UPC”].astype(str)`, we are specifically converting only the “UPC” column to a string data type while leaving the other columns as they were in the DataFrame.
This way, the “UPC” column should be displayed correctly as a string without commas, and the other columns, including the “Price” column, will remain unchanged.
If you continue to experience issues, please double-check the content of the “Price” column in the CSV file to ensure it contains only numeric values and doesn’t have any formatting issues. If needed, consider using `pd.to_numeric()` to explicitly convert the “Price” column to a numeric data type.
“`python
# Convert the “Price” column to a numeric data type
df[“Price”] = pd.to_numeric(df[“Price”])
“`
This will ensure that the “Price” column is treated as a numeric data type, and any formatting issues, such as commas, are removed.
Apologies for the oversight. You are correct; the `style.format(thousands=”)` method will apply formatting changes to all numeric columns, including the “Price” column, which may result in the commas being removed from the “Price” values as well.
To format only the “UPC” column while keeping the “Price” column as is, you can use the `style.applymap` method to apply formatting to specific cells based on conditions.
Here’s how you can do it:
“`python
import streamlit as st
import pandas as pd
# Load the csv file
df = pd.read_csv(“37jeans.csv”, dtype={“UPC”: str})
# Convert the data types of all columns using convert_dtypes()
df = df.convert_dtypes()
# Create a function to apply formatting to the UPC column cells
def format_upc_cell(cell):
return f'”{cell}”‘
# Apply the formatting function only to the UPC column
formatted_df = df.copy()
formatted_df[‘UPC’] = formatted_df[‘UPC’].apply(format_upc_cell)
# Display the csv file with the formatted UPC column
st.dataframe(formatted_df)
“`
In this code, we define a `format_upc_cell` function that adds double quotes around each cell in the “UPC” column. Then, we create a copy of the DataFrame (`formatted_df`) and apply the `format_upc_cell` function only to the “UPC” column. The rest of the columns, including the “Price” column, will remain unchanged.
Now, when you use `st.dataframe(formatted_df)`, you should see the “UPC” column formatted with double quotes, while the “Price” column and other numeric columns will remain unchanged.
st.dataframe(
column_config={
“sell_price”: st.column_config.NumberColumn(
“Price”,
format=”$%d”,
)
“upc”: st.column_config.NumberColumn(
“UPC”,
format=”$%d”,
)
},
hide_index=True,
)
In the Streamlit code you provided, you are using the `st.column_config` module to customize the display of the “Price” and “UPC” columns in the DataFrame. However, there are a few issues with the code. The `NumberColumn` class is used to format numeric columns, but both “Price” and “UPC” seem to be non-numeric (strings). Additionally, the `format` parameter specifies how to format numeric values, but it should not be used for strings.
To customize the display of the “Price” and “UPC” columns as strings, you can use the `st.dataframe` function directly with the `format` parameter. Here’s how you can do it:
“`python
import streamlit as st
import pandas as pd
# Load the csv file
df = pd.read_csv(“37jeans.csv”, dtype={“UPC”: str})
# Display the csv file with custom formatting for the “Price” and “UPC” columns
st.dataframe(df, format={“sell_price”: “${:.2f}”, “UPC”: ‘”{}”‘})
# Hide the index column
st.dataframe(df.style.hide_index())
“`
In this code, we load the CSV file as before but without using the `convert_dtypes()` method since we don’t want to convert the “Price” and “UPC” columns to numeric types.
We then use the `st.dataframe` function with the `format` parameter to specify custom formatting for the “Price” and “UPC” columns. The format for the “Price” column is set to `”{:.2f}”`, which will format numeric values with two decimal places and add a dollar sign.
For the “UPC” column, we use `'”{}”‘` as the format, which adds double quotes around each cell in the “UPC” column.
Finally, we use `st.dataframe(df.style.hide_index())` to hide the index column.
With this approach, the “Price” and “UPC” columns will be displayed with the specified custom formatting while the rest of the DataFrame remains unchanged.
Script execution error
File “C:SarahOneTOOLSapp.py”, line 15
“sell_price”:st.column_config.NumberColumn(
^
SyntaxError: invalid syntax. Perhaps you forgot a comma?
Apologies for the oversight. The error is due to a missing comma between the two elements in the `column_config` dictionary. Let’s add the missing comma to fix the syntax error. Additionally, since both “Price” and “UPC” columns are non-numeric, we’ll use the `format` parameter directly in the `st.dataframe` function to customize their display.
Here’s the corrected code:
“`python
import streamlit as st
import pandas as pd
# Load the csv file
df = pd.read_csv(“37jeans.csv”, dtype={“UPC”: str})
# Display the csv file with custom formatting for the “Price” and “UPC” columns
st.dataframe(df, format={“sell_price”: “${:.2f}”, “UPC”: ‘”{}”‘})
# Hide the index column
st.dataframe(df.style.hide_index())
“`
With this modification, the code should execute without syntax errors. The “Price” column will be displayed with two decimal places and a dollar sign, and the “UPC” column will have double quotes around each cell. The rest of the DataFrame will remain unchanged, and the index column will be hidden.
I have unistalled and reinstla;l;ed this tloo many times what the fuck is the probblem?
(bambi) (.env) C:SarahOneCoderengineer>python app.py
Traceback (most recent call last):
File “C:SarahOneCoderengineerapp.py”, line 12, in
import torch
ModuleNotFoundError: No module named ‘torch’
(bambi) (.env) C:SarahOneCoderengineer>
The error message indicates that the Python script (`app.py`) is trying to import the `torch` module, but it is not installed in your Python environment. The `torch` module is part of the PyTorch library, which is commonly used for deep learning tasks.
To resolve this issue, you need to install the PyTorch library in your Python environment. You can do this using `pip` or `conda`, depending on how your environment is set up.
If you are using `pip`, run the following command to install PyTorch:
“`bash
pip install torch
“`
If you are using `conda`, run the following command to install PyTorch:
“`bash
conda install pytorch -c pytorch
“`
After installing PyTorch, you should be able to run your script without encountering the `ModuleNotFoundError`.
If you are using a virtual environment (as seen in your command prompt with `(bambi) (.env)`), make sure you have activated the correct virtual environment before installing PyTorch. Activate the virtual environment using:
“`bash
conda activate bambi
“`
or
“`bash
source .env/bin/activate
“`
Replace `bambi` with the name of your virtual environment if it’s different.
Once the correct virtual environment is activated, install PyTorch as mentioned earlier.
If you still encounter issues, ensure that you are installing the packages in the correct Python environment and that the environment is active when you run your script. If you have multiple Python installations or virtual environments, double-check that you are using the correct one where `torch` is installed.
PackagesNotFoundError: The following packages are missing from the target environment:
– conda-forge/noarch::typing_extensions==4.6.3=pyha770c72_0
The `PackagesNotFoundError` you encountered indicates that the package `typing_extensions` with version `4.6.3` and build `pyha770c72_0` is missing in the target environment.
This error typically occurs when you are trying to install or use a package that is not available in the default channels of your conda environment. The package may be available in the `conda-forge` channel, but it’s not present in the default channel.
To resolve this issue, you can try installing the missing package from the `conda-forge` channel using the following command:
“`bash
conda install -c conda-forge typing_extensions=4.6.3
“`
This command explicitly specifies the `conda-forge` channel and the version of the package you need to install.
After installing `typing_extensions`, you can then proceed with installing or running your original script. If there are other missing packages, you may need to install them as well using the same approach.
If you encounter any other issues, make sure your conda environment is correctly activated, and consider updating your conda packages using:
“`bash
conda update –all
“`
This will ensure that your packages are up to date and can help prevent compatibility issues.