I want to solve an optimization problem in Julia.
I am trying to define a binary variable x_{r,i}
Thereby, the length of the sets of both indices is not the same.
Let's say there is r_a and r_b, but for r_a there are i_1 and i_2 whereas for r_b there are i_1, i_2 and i_3 so in the end I want to get X_a_1, X_a_2 and X_b_1, X_b_2, X_b_3
The set of indices i varies for different indices r.
Is there any way to define variable x with these indices in Julia?
This is what I tried:
R=["a","b"]
I=Dict("a" => [1,2],"b"=>[1,2,3])
m = Model(CPLEX.Optimizer)
#variables m begin
X[R,[I]], Bin
end
You were on the right track. Create a SparseAxisArray:
julia> R = ["a", "b"]
2-element Vector{String}:
"a"
"b"
julia> I = Dict("a" => [1, 2], "b" => [1, 2, 3])
Dict{String, Vector{Int64}} with 2 entries:
"b" => [1, 2, 3]
"a" => [1, 2]
julia> model = Model();
julia> #variable(model, x[r in R, i in I[r]])
JuMP.Containers.SparseAxisArray{VariableRef, 2, Tuple{String, Int64}} with 5 entries:
[a, 1] = x[a,1]
[a, 2] = x[a,2]
[b, 1] = x[b,1]
[b, 2] = x[b,2]
[b, 3] = x[b,3]
JuMP supports dense arrays with custom indexing.
julia> indices = [Symbol.(:a, 1:2);Symbol.(:b, 1:3)];
julia> #variable(m, x[indices], Bin)
1-dimensional DenseAxisArray{VariableRef,1,...} with index sets:
Dimension 1, [:a1, :a2, :b1, :b2, :b3]
And data, a 5-element Vector{VariableRef}:
x[a1]
x[a2]
x[b1]
x[b2]
x[b3]
Does anyone know how I can get the index position of duplicate items in a python list?
I have tried doing this and it keeps giving me only the index of the 1st occurrence of the of the item in the list.
List = ['A', 'B', 'A', 'C', 'E']
I want it to give me:
index 0: A
index 2: A
You want to pass in the optional second parameter to index, the location where you want index to start looking. After you find each match, reset this parameter to the location just after the match that was found.
def list_duplicates_of(seq,item):
start_at = -1
locs = []
while True:
try:
loc = seq.index(item,start_at+1)
except ValueError:
break
else:
locs.append(loc)
start_at = loc
return locs
source = "ABABDBAAEDSBQEWBAFLSAFB"
print(list_duplicates_of(source, 'B'))
Prints:
[1, 3, 5, 11, 15, 22]
You can find all the duplicates at once in a single pass through source, by using a defaultdict to keep a list of all seen locations for any item, and returning those items that were seen more than once.
from collections import defaultdict
def list_duplicates(seq):
tally = defaultdict(list)
for i,item in enumerate(seq):
tally[item].append(i)
return ((key,locs) for key,locs in tally.items()
if len(locs)>1)
for dup in sorted(list_duplicates(source)):
print(dup)
Prints:
('A', [0, 2, 6, 7, 16, 20])
('B', [1, 3, 5, 11, 15, 22])
('D', [4, 9])
('E', [8, 13])
('F', [17, 21])
('S', [10, 19])
If you want to do repeated testing for various keys against the same source, you can use functools.partial to create a new function variable, using a "partially complete" argument list, that is, specifying the seq, but omitting the item to search for:
from functools import partial
dups_in_source = partial(list_duplicates_of, source)
for c in "ABDEFS":
print(c, dups_in_source(c))
Prints:
A [0, 2, 6, 7, 16, 20]
B [1, 3, 5, 11, 15, 22]
D [4, 9]
E [8, 13]
F [17, 21]
S [10, 19]
>>> def indices(lst, item):
... return [i for i, x in enumerate(lst) if x == item]
...
>>> indices(List, "A")
[0, 2]
To get all duplicates, you can use the below method, but it is not very efficient. If efficiency is important you should consider Ignacio's solution instead.
>>> dict((x, indices(List, x)) for x in set(List) if List.count(x) > 1)
{'A': [0, 2]}
As for solving it using the index method of list instead, that method takes a second optional argument indicating where to start, so you could just repeatedly call it with the previous index plus 1.
>>> List.index("A")
0
>>> List.index("A", 1)
2
I made a benchmark of all solutions suggested here and also added another solution to this problem (described in the end of the answer).
Benchmarks
First, the benchmarks. I initialize a list of n random ints within a range [1, n/2] and then call timeit over all algorithms
The solutions of #Paul McGuire and #Ignacio Vazquez-Abrams works about twice as fast as the rest on the list of 100 ints:
Testing algorithm on the list of 100 items using 10000 loops
Algorithm: dupl_eat
Timing: 1.46247477189
####################
Algorithm: dupl_utdemir
Timing: 2.93324529055
####################
Algorithm: dupl_lthaulow
Timing: 3.89198786645
####################
Algorithm: dupl_pmcguire
Timing: 0.583058259784
####################
Algorithm: dupl_ivazques_abrams
Timing: 0.645062989076
####################
Algorithm: dupl_rbespal
Timing: 1.06523873786
####################
If you change the number of items to 1000, the difference becomes much bigger (BTW, I'll be happy if someone could explain why) :
Testing algorithm on the list of 1000 items using 1000 loops
Algorithm: dupl_eat
Timing: 5.46171654555
####################
Algorithm: dupl_utdemir
Timing: 25.5582547323
####################
Algorithm: dupl_lthaulow
Timing: 39.284285326
####################
Algorithm: dupl_pmcguire
Timing: 0.56558489513
####################
Algorithm: dupl_ivazques_abrams
Timing: 0.615980005148
####################
Algorithm: dupl_rbespal
Timing: 1.21610942322
####################
On the bigger lists, the solution of #Paul McGuire continues to be the most efficient and my algorithm begins having problems.
Testing algorithm on the list of 1000000 items using 1 loops
Algorithm: dupl_pmcguire
Timing: 1.5019953958
####################
Algorithm: dupl_ivazques_abrams
Timing: 1.70856155898
####################
Algorithm: dupl_rbespal
Timing: 3.95820421595
####################
The full code of the benchmark is here
Another algorithm
Here is my solution to the same problem:
def dupl_rbespal(c):
alreadyAdded = False
dupl_c = dict()
sorted_ind_c = sorted(range(len(c)), key=lambda x: c[x]) # sort incoming list but save the indexes of sorted items
for i in xrange(len(c) - 1): # loop over indexes of sorted items
if c[sorted_ind_c[i]] == c[sorted_ind_c[i+1]]: # if two consecutive indexes point to the same value, add it to the duplicates
if not alreadyAdded:
dupl_c[c[sorted_ind_c[i]]] = [sorted_ind_c[i], sorted_ind_c[i+1]]
alreadyAdded = True
else:
dupl_c[c[sorted_ind_c[i]]].append( sorted_ind_c[i+1] )
else:
alreadyAdded = False
return dupl_c
Although it's not the best it allowed me to generate a little bit different structure needed for my problem (i needed something like a linked list of indexes of the same value)
dups = collections.defaultdict(list)
for i, e in enumerate(L):
dups[e].append(i)
for k, v in sorted(dups.iteritems()):
if len(v) >= 2:
print '%s: %r' % (k, v)
And extrapolate from there.
I think I found a simple solution after a lot of irritation :
if elem in string_list:
counter = 0
elem_pos = []
for i in string_list:
if i == elem:
elem_pos.append(counter)
counter = counter + 1
print(elem_pos)
This prints a list giving you the indexes of a specific element ("elem")
Using new "Counter" class in collections module, based on lazyr's answer:
>>> import collections
>>> def duplicates(n): #n="123123123"
... counter=collections.Counter(n) #{'1': 3, '3': 3, '2': 3}
... dups=[i for i in counter if counter[i]!=1] #['1','3','2']
... result={}
... for item in dups:
... result[item]=[i for i,j in enumerate(n) if j==item]
... return result
...
>>> duplicates("123123123")
{'1': [0, 3, 6], '3': [2, 5, 8], '2': [1, 4, 7]}
from collections import Counter, defaultdict
def duplicates(lst):
cnt= Counter(lst)
return [key for key in cnt.keys() if cnt[key]> 1]
def duplicates_indices(lst):
dup, ind= duplicates(lst), defaultdict(list)
for i, v in enumerate(lst):
if v in dup: ind[v].append(i)
return ind
lst= ['a', 'b', 'a', 'c', 'b', 'a', 'e']
print duplicates(lst) # ['a', 'b']
print duplicates_indices(lst) # ..., {'a': [0, 2, 5], 'b': [1, 4]})
A slightly more orthogonal (and thus more useful) implementation would be:
from collections import Counter, defaultdict
def duplicates(lst):
cnt= Counter(lst)
return [key for key in cnt.keys() if cnt[key]> 1]
def indices(lst, items= None):
items, ind= set(lst) if items is None else items, defaultdict(list)
for i, v in enumerate(lst):
if v in items: ind[v].append(i)
return ind
lst= ['a', 'b', 'a', 'c', 'b', 'a', 'e']
print indices(lst, duplicates(lst)) # ..., {'a': [0, 2, 5], 'b': [1, 4]})
Wow, everyone's answer is so long. I simply used a pandas dataframe, masking, and the duplicated function (keep=False markes all duplicates as True, not just first or last):
import pandas as pd
import numpy as np
np.random.seed(42) # make results reproducible
int_df = pd.DataFrame({'int_list': np.random.randint(1, 20, size=10)})
dupes = int_df['int_list'].duplicated(keep=False)
print(int_df['int_list'][dupes].index)
This should return Int64Index([0, 2, 3, 4, 6, 7, 9], dtype='int64').
def index(arr, num):
for i, x in enumerate(arr):
if x == num:
print(x, i)
#index(List, 'A')
In a single line with pandas 1.2.2 and numpy:
import numpy as np
import pandas as pd
idx = np.where(pd.DataFrame(List).duplicated(keep=False))
The argument keep=False will mark every duplicate as True and np.where() will return an array with the indices where the element in the array was True.
string_list = ['A', 'B', 'C', 'B', 'D', 'B']
pos_list = []
for i in range(len(string_list)):
if string_list[i] = ='B':
pos_list.append(i)
print pos_list
def find_duplicate(list_):
duplicate_list=[""]
for k in range(len(list_)):
if duplicate_list.__contains__(list_[k]):
continue
for j in range(len(list_)):
if k == j:
continue
if list_[k] == list_[j]:
duplicate_list.append(list_[j])
print("duplicate "+str(list_.index(list_[j]))+str(list_.index(list_[k])))
Here is one that works for multiple duplicates and you don't need to specify any values:
List = ['A', 'B', 'A', 'C', 'E', 'B'] # duplicate two 'A's two 'B's
ix_list = []
for i in range(len(List)):
try:
dup_ix = List[(i+1):].index(List[i]) + (i + 1) # dup onwards + (i + 1)
ix_list.extend([i, dup_ix]) # if found no error, add i also
except:
pass
ix_list.sort()
print(ix_list)
[0, 1, 2, 5]
def dup_list(my_list, value):
'''
dup_list(list,value)
This function finds the indices of values in a list including duplicated values.
list: the list you are working on
value: the item of the list you want to find the index of
NB: if a value is duplcated, its indices are stored in a list
If only one occurence of the value, the index is stored as an integer.
Therefore use isinstance method to know how to handle the returned value
'''
value_list = []
index_list = []
index_of_duped = []
if my_list.count(value) == 1:
return my_list.index(value)
elif my_list.count(value) < 1:
return 'Your argument is not in the list'
else:
for item in my_list:
value_list.append(item)
length = len(value_list)
index = length - 1
index_list.append(index)
if item == value:
index_of_duped.append(max(index_list))
return index_of_duped
# function call eg dup_list(my_list, 'john')
If you want to get index of all duplicate elements of different types you can try this solution:
# note: below list has more than one kind of duplicates
List = ['A', 'B', 'A', 'C', 'E', 'E', 'A', 'B', 'A', 'A', 'C']
d1 = {item:List.count(item) for item in List} # item and their counts
elems = list(filter(lambda x: d1[x] > 1, d1)) # get duplicate elements
d2 = dict(zip(range(0, len(List)), List)) # each item and their indices
# item and their list of duplicate indices
res = {item: list(filter(lambda x: d2[x] == item, d2)) for item in elems}
Now, if you print(res) you'll get to see this:
{'A': [0, 2, 6, 8, 9], 'B': [1, 7], 'C': [3, 10], 'E': [4, 5]}
def duplicates(list,dup):
a=[list.index(dup)]
for i in list:
try:
a.append(list.index(dup,a[-1]+1))
except:
for i in a:
print(f'index {i}: '+dup)
break
duplicates(['A', 'B', 'A', 'C', 'E'],'A')
Output:
index 0: A
index 2: A
This is a good question and there is a lot of ways to it.
The code below is one of the ways to do it
letters = ["a", "b", "c", "d", "e", "a", "a", "b"]
lettersIndexes = [i for i in range(len(letters))] # i created a list that contains the indexes of my previous list
counter = 0
for item in letters:
if item == "a":
print(item, lettersIndexes[counter])
counter += 1 # for each item it increases the counter which means the index
An other way to get the indexes but this time stored in a list
letters = ["a", "b", "c", "d", "e", "a", "a", "b"]
lettersIndexes = [i for i in range(len(letters)) if letters[i] == "a" ]
print(lettersIndexes) # as you can see we get a list of the indexes that we want.
Good day
Using a dictionary approach based on setdefault instance method.
List = ['A', 'B', 'A', 'C', 'B', 'E', 'B']
# keep track of all indices of every term
duplicates = {}
for i, key in enumerate(List):
duplicates.setdefault(key, []).append(i)
# print only those terms with more than one index
template = 'index {}: {}'
for k, v in duplicates.items():
if len(v) > 1:
print(template.format(k, str(v).strip('][')))
Remark: Counter, defaultdict and other container class from collections are subclasses of dict hence share the setdefault method as well
I'll mention the more obvious way of dealing with duplicates in lists. In terms of complexity, dictionaries are the way to go because each lookup is O(1). You can be more clever if you're only interested in duplicates...
my_list = [1,1,2,3,4,5,5]
my_dict = {}
for (ind,elem) in enumerate(my_list):
if elem in my_dict:
my_dict[elem].append(ind)
else:
my_dict.update({elem:[ind]})
for key,value in my_dict.iteritems():
if len(value) > 1:
print "key(%s) has indices (%s)" %(key,value)
which prints the following:
key(1) has indices ([0, 1])
key(5) has indices ([5, 6])
a= [2,3,4,5,6,2,3,2,4,2]
search=2
pos=0
positions=[]
while (search in a):
pos+=a.index(search)
positions.append(pos)
a=a[a.index(search)+1:]
pos+=1
print "search found at:",positions
I just make it simple:
i = [1,2,1,3]
k = 0
for ii in i:
if ii == 1 :
print ("index of 1 = ", k)
k = k+1
output:
index of 1 = 0
index of 1 = 2
I am new to python and pyspark. I would like to know
how can I write the below spark dataframe function in pyspark:
val df = spark.read.format("jdbc").options(
Map(
"url" -> "jdbc:someDB",
"user" -> "root",
"password" -> "password",
"dbtable" -> "tableName",
"driver" -> "someDriver")).load()
I tried to write as below in pyspark. But, getting syntax error:
df = spark.read.format("jdbc").options(
map(lambda : ("url","jdbc:someDB"), ("user","root"), ("password","password"), ("dbtable","tableName"), ("driver","someDriver"))).load()
Thanks in Advance
In PySpark, pass the options as keyword arguments:
df = spark.read\
.format("jdbc")\
.options(
url="jdbc:someDB",
user="root",
password="password",
dbtable="tableName",
driver="someDriver",
)\
.load()
Sometimes it's handy to keep them in a dict and unpack them later using the splat operator:
options = {
"url": "jdbc:someDB",
"user": "root",
"password": "password",
"dbtable": "tableName",
"driver": "someDriver",
}
df = spark.read\
.format("jdbc")\
.options(**options)\
.load()
Regarding the code snippets from your question: you happened to mix up two different concepts of "map":
Map in Scala is a data structure also known as "associative array" or "dictionary", equivalent to Python's dict
map in Python is a higher-order function you can use for applying a function to an iterable, e.g.:
In [1]: def square(x: int) -> int:
...: return x**2
...:
In [2]: list(map(square, [1, 2, 3, 4, 5]))
Out[2]: [1, 4, 9, 16, 25]
In [3]: # or just use a lambda
In [4]: list(map(lambda x: x**2, [1, 2, 3, 4, 5]))
Out[4]: [1, 4, 9, 16, 25]
Try to use option() instead:
df = spark.read \
.format("jdbc") \
.option("url","jdbc:someDB") \
.option("user","root") \
.option("password","password") \
.option("dbtable","tableName") \
.option("driver","someDriver") \
.load()
To load a CSV file with multiple parameters, pass the arguments to load():
df = spark.read.load("examples/src/main/resources/people.csv",
format="csv", sep=":", inferSchema="true", header="true")
Here's the documentation for that.
I have a dictionary that looks like this:
d = {1:0, 2:0, 3:1, 4:0, 5:2, 6:1, 7:2, 8:0}
And I want to group by .keys() such as I get:
pandas_ordered = { 0:[1,2,4,8], 1:[3,6], 2:[5,7] }
But with this command for
pd.Series(list(d.values())).groupby(list(partition.keys())).to_dict()
Bellow is an example:
# Example:
import pandas as pd
d = {1:0, 2:0, 3:1, 4:0, 5:2, 6:1, 7:2, 8:0}
def pandas_groupby(dictionary):
values = list(dictionary.values())
keys = list(dictionary.keys())
return pd.Series(values).groupby(keys).to_dict()
pandas_groupby(d)
The above code produces the error:
AttributeError: Cannot access callable attribute 'to_dict' of
'SeriesGroupBy' objects, try using the 'apply' method
Any ideas on how to do this?
You dict is already given by the groups in your groupby
d = {1:0, 2:0, 3:1, 4:0, 5:2, 6:1, 7:2, 8:0}
s = pd.Series(d)
s.groupby(s).groups
{0: Int64Index([1, 2, 4, 8], dtype='int64'),
1: Int64Index([3, 6], dtype='int64'),
2: Int64Index([5, 7], dtype='int64')}
But of course, can always agg and customize
s.groupby(s).agg(lambda x: tuple(x.index)).to_dict()
{0: (1, 2, 4, 8), 1: (3, 6), 2: (5, 7)}