Write a python code to merge two list with the following condition [closed] - python

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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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add index of values in between elements in a string [closed]

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For example:
input: "Orange"
output: "O0r1a2n3g4e5"
I know I will have to convert the string to a list so I can iterate and append the index numbers, but I don't know how to do that in code.

''.join([f'{letter}{index}' for index, letter in enumerate(input)])

What exactly does this code do to a dataframe? [closed]

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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.

Inserting elements into a tuple at the time of running the code [closed]

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An input of n integers is taken at the run time.
n integers are taken as input in a single line, separated by a space.
We have to create a tuple of this n integers and perform hash(tuple) function on it and give the output.

Here it is:
def your_homework():
return hash(tuple([int(i) for i in input("give me n integers").split()]))

Pandas: Multiply a column based on a different columns condition [closed]

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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'])

python: Is it better to have one proper for loop or several oneliner for loops [closed]

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I have several lines of python code that look like this:
myVar1 = np.array([d['key1'] for d in D[0]['Log']])
All keys/values have the same length.
is it better (performance/cpu/memory) to make a single loop and import them, or better several one liners?
What do you think?

You can always use timeit and measure it.

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