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Hey guys I was wondering what exactly this code does, how does it iterate through the dataframe and what exactly does the lambda function do?
df.apply(lambda x: pd.Series(x.dropna().values))
The above block of code traverses the data frame column-wise and drops NA values. Lambda functions are the anonymous functions that are well explained in this text.
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I have dataframe like this
enter image description here
I need to find out the average close days of request Recycling
Please help me.
You can group by request and get the average.This will give average for each group
df.groupby("request")["Days to Close"].mean()
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If two list is having same number, then the final list should not have the number
If your lists contains unique elements, consider using sets instead.
https://docs.python.org/2/library/sets.html
Check this code:
ls=[1,2,3,4,5,5]
ls1=[1,2,3,4,5,7,8,9]
common_elements=set(ls).intersection(set(ls1))
for i in common_elements:
if ls.__contains__(i):
ls.remove(i)
if ls1.__contains__(i):
ls1.remove(i)
final_ls=ls+ls1
print(final_ls)
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For the above example,
I want to have a look at the value of outlet_size when Outlet_identifier = OUT049 or any value for that instance.
I don't want to produce a new dataframe object and then print it, instead I want to know if there is any function or way to directly view it.
For both columns
df.loc[df['Outlet_identifier'].eq('OUT049'), ['Outlet_identifier', 'outlet_size']]
you can do that with pandas like this :
df = pd.DataFrame({'Outlet_identifier': ['OUT049','OUT018','OUT049'], 'outlet_size':
[2.0, 2.0, 2.0]})
df[df["Outlet_identifier"]=="OUT049"]["outlet_size"]
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Im trying to find a way to compare what students performed consistantly in their InternalAssessment_Performance to their FinalExam_Performance. Essentially i need to find what students have the same answer in both those columns.
How is it possible to compare the values in both commons and have them returned if they are the same?
Any help no matter how small would be great.
If the columns are aligned you can do something like this:
df[df['InternalAssessment_Performance'] == df['FinalExam_Performance']]
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I am using Python with Pandas. How can I multiply a column by 1000 given another column has a certain string?
This should do it.
df['columnname'] = np.where(df['othercolumn'] == 'CertainString',
df['columnname'] * 1000,
df['columnname'])