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What is the best way to replace all the null "NaN" values in a Python dataframe with the value 0?
Also, is it possible to do this with a for loop?
you can simply use
df.fillna(0)
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I have column with this kind of data:
I want to count how many times valu occur in a row. It is a string, so I want to convert this '63,63,63,63,63,63,63,63,63,63,63' to this ['63','63','63'...].
I there any way to do this quickly?
Thanks
if given string is s
l=s.split(',')
l is the required list
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df['Total']= df.iloc[3:5].sum(axis=1)
returns NaN for some most values, why is this? They are all intergers.
Also is there a better way of doing this?
Check with add with columns slice
df['Total'] = df.iloc[:, 3:5].sum(axis=1)
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Write a python program to calculate sum of all elements in a list excluding the numbers between 5 and 8.
Take the list as l1=[1,5,3,6,8,9,19,17,14]
sum(x for x in l1 if x not in range(5,9))
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I need to find a word "little" in a string but word in the string is not present as a whole.
For eg. input string="aalbeiedteetoolpue" or "lliittttllleee"
May be you can try this:
\b\w*l\w*i\w*t\w*t\w*l\w*e\w*\b
Demo
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the corresponding pattern is ('Handmade Frozen Car', 11.0). I want to get the value as 11.0. Any suggestions are highly appreciated.
x = ('Handmade Frozen Car', 11.0)
secondElement = x[1]
secondElement will be equal to 11.0