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Convert list of dictionaries to a pandas DataFrame
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cs95
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answered Dec 17 '13 at 15:35
jorisjoris
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Python dictionary: Get list of values for list of keys
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213
A list comprehension seems to be a good way to do this:
>>> [mydict[x] for x in mykeys...
Split (explode) pandas dataframe string entry to separate rows
...ws()]).reset_index()
Out[55]:
index 0
0 a 1
1 b 1
2 c 1
3 d 2
4 e 2
5 f 2
Then you just have to rename the columns
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Difference between break and continue statement
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537
break leaves a loop, continue jumps to the next iteration.
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Get difference between two lists
...))
Out[5]: ['Four', 'Three']
Beware that
In [5]: set([1, 2]) - set([2, 3])
Out[5]: set([1])
where you might expect/want it to equal set([1, 3]). If you do want set([1, 3]) as your answer, you'll need to use set([1, 2]).symmetric_difference(set([2, 3])).
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How can I use Homebrew to install both Python 2 and 3 on Mac?
I need to be able to switch back and forth between Python 2 and 3. How do I do that using Homebrew as I don't want to mess with path and get into trouble.
Right now I have 2.7 installed through Homebrew.
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SQL query return data from multiple tables
...what colors we have in the car yard.
mysql> create table colors(id int(3) not null auto_increment primary key,
-> color varchar(15), paint varchar(10));
Query OK, 0 rows affected (0.01 sec)
mysql> show columns from colors;
+-------+-------------+------+-----+---------+---------------...
Are tuples more efficient than lists in Python?
...s much faster than assigning a list.
>>> def a():
... x=[1,2,3,4,5]
... y=x[2]
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>>> def b():
... x=(1,2,3,4,5)
... y=x[2]
...
>>> import dis
>>> dis.dis(a)
2 0 LOAD_CONST 1 (1)
3 LOAD_CONST ...
Image Segmentation using Mean Shift explained
...hood and whose value is within a distance d.
The Mean Shift takes usually 3 inputs:
A distance function for measuring distances between pixels. Usually the Euclidean distance, but any other well defined distance function could be used. The Manhattan
Distance is another useful choice sometimes.
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efficient circular buffer?
...in xrange(20):
... d.append(i)
...
>>> d
deque([10, 11, 12, 13, 14, 15, 16, 17, 18, 19], maxlen=10)
There is a recipe in the docs for deque that is similar to what you want. My assertion that it's the most efficient rests entirely on the fact that it's implemented in C by an incredib...