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If the forum is losing you money, your time, or your sanity, feel free to hit the “notify” button. Let me know if it does… and for the love of Mike let me know why. I’m at your service.Q:
Error number 324 in Internet Explorer
I have been trying to work out why some clients are getting the error 324 (net::ERR_UNKOWN_ERROR) in Internet Explorer 7 on a web site I am working on. I have made some research and stumbled upon an article about it on the web: “ADO error 324 and details”
However, my site does not have any access to a database and does not connect to a database in my code. The web site is only rendering a page with some graphics and links on it.
The code looks like this:
function onLoad()
{
document.getElementById(“number”).style.display=”none”;
}
However, every time I am on the site, I am getting the error.
I have also found that the error 324 is caused by the web service my web site connects to. However, I do not have access to this web service, I only have the code in my web site.
Is there any way of preforming security checks or something like that on my site, that would make it so that the error 324 is not thrown?
A:
Try this
function onLoad()
{
if (window.XMLHttpRequest)
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Hacksoft. rar Type: Download.Q:
Grouping the data values in a pandas dataframe by a column value
I have a pandas DataFrame and I would like to group it by the values of a column. In this example, I would like to group it by the column “Model” and sum up the “Price” values.
I can only think of a loop solution where I extract the index of the dataframe as list and iterate through the index to find the values. Is there a better way of doing this?
Example data:
import pandas as pd
df = pd.DataFrame({“Brand”: [“Ford”, “Ford”], “Model”: [“Merc”, “Merc”], “Price”: [200, 100], “Year”: [2000, 2000]})
df.set_index([‘Brand’, ‘Model’, ‘Year’], inplace=True)
df.sort_values(by=’Year’, ascending=True, inplace=True)
print df
Output:
Price
Brand Model Year
Merc Merc 2000
Ford Merc 2000
Desired output:
Price
Brand Model Year
Ford Merc 2000 200
Merc Merc 2000 100
A:
Use GroupBy.sum with reset_index:
print (df.groupby([‘Brand’, ‘Model’])[‘Price’]
.sum()
.rename_axis((‘Price’, None), axis=1)
.sort_values(by=’Year’, ascending=True)
.reset_index())
Brand Model Year Price
0 Ford Merc 2000 2000 200
1 Merc Merc 2000 2000 100
If Year is unique:
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