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Percentage display based on a percentage of another column in Pandas
Is there a way to do something like this in Pandas?
df[‘Percentage’] = 0
for i in range(0, df[‘Percentage’]:
df[‘Percentage’] = df[‘Percentage’].astype(‘int’)/100 * df[‘Percentage’] + 1
I tried writing that code myself, but I can’t get it to work.
Try this to find the percentage based on another column
df[‘Percentage’]= df[‘Percentage’] * 100 / df[‘Percentage’].sum()
Another solution (lowers the computational cost):
df[‘Percentage’] = np.where(df[‘Percentage’] == 1, 1, (df[‘Percentage’] / df[‘Percentage’].sum() * 100))
df[‘Percentage’] = df[‘Percentage’] + (np.where(df[‘Percentage’] == 1, 1, (df[‘Percentage’] / df[‘Percentage’].sum() * 100)) * df[‘Percentage’] * 100)
What is “passing by ref” in.NET?
I do realize this question is pretty much a duplicate of What does it mean to “pass by reference” in.Net?, but I couldn’t find the answers in that question satisfying, so I’m asking again.
Here’s the code snippet I’m trying to understand:
string buffer = “Hello,
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We investigated the epidemiology of renal cell carcinoma in 1,245 patients diagnosed at the Hospital das Clínicas of the Federal University of Paraná, Curitiba, Brazil, between January 1989 and December 2000. The cases were obtained from the pathology registry, and the data were analyzed using descriptive statistics and the chi-square test with the SAS 6.12 software. There were 759 male and 486 female patients. The mean age at diagnosis was 61 years, and 43.5% of patients were elderly (60 years of age or older). The greatest incidence was observed among men between ages 30 and 59, particularly in the 56-59 years group. The overall incidence of renal cell carcinoma (RCC) was 1.12 cases per 100,000 people per year, with a significantly increasing trend (p Dave Mora won the WSL 2 Defender Championship without dropping a single heat and will face Siobhan-Marie O’Connor in the semi-finals as defending champion. Photo by Darren McCollester/International Surfing Association
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