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I have a list of key:
list_date = ["MON", "TUE", "WED", "THU","FRI"]
I have many lists of values that created by codes below:
list_value = list()
for i in list(range(5, 70, 14)):
list_value.append(list(range(i, i+10, 3)))
Rules created that:
first number is 5, a list contains 4 items has value equal x = x + 3, and so on [5, 8, 11,1 4]
the first number of the second list equal: x = 5 + 14, and value inside still as above x = x +3
[[5, 8, 11, 14], [19, 22, 25, 28], [33, 36, 39, 42], [47, 50, 53, 56], [61, 64, 67, 70]]
I expect to obtain a dict like this:
collections = {"MON":[5, 8, 11, 14], "TUE" :[19, 22, 25, 28], "WED":[33, 36, 39, 42], "THU":[47, 50, 53, 56], "FRI":[61, 64, 67, 70]}
Then, I used:
zip_iterator = zip(list_date, list_value)
collections = dict(zip_iterator)
To get my expected result.
I tried another way like using lambda function
for i in list(range(5, 70, 14)):
list_value.append(list(range(i,i+10,3)))
couple_start_end[lambda x: x in list_date] = list(range(i, i + 10, 3))
And the output is:
{<function <lambda> at 0x000001BF7F0711F0>: [5, 8, 11, 14], <function <lambda> at 0x000001BF7F071310>: [19, 22, 25, 28], <function <lambda> at 0x000001BF7F071280>: [33, 36, 39, 42], <function <lambda> at 0x000001BF7F0710D0>: [47, 50, 53, 56], <function <lambda> at 0x000001BF7F0890D0>: [61, 64, 67, 70]}
I want to ask there is any better solution to create lists of values with the rules above? and create the dictionary collections without using the zip method?
Thank you so much for your attention and participation.
Sure, you can use enumerate but I wouldn't say it is in anyway better or worse than the zip based solution:
collections = {}
for idx, key in enumerate(list_keys):
collections[key] = list_value[idx]
print(collections)
Output:
{'MON': [5, 8, 11, 14], 'TUE': [19, 22, 25, 28], 'WED': [33, 36, 39, 42], 'THU': [47, 50, 53, 56], 'FRI': [61, 64, 67, 70]}
Further, you don't need to create the value list separately, you can create the dictionary as you go along:
list_keys = ["MON", "TUE", "WED", "THU","FRI"]
collections = {}
for idx, start in enumerate(range(5, 70, 14)):
collections[list_keys[idx]] = [i for i in range(start, start+10, 3)]
print(collections)
I have value X of type ndarray with shape: (40000, 2)
The second column of X contains list of 50 numbers
Example:
[17, [1, 2, 3, ...]],
[39, [44, 45, 45, ...]], ...
I want to convert it to ndarray of shape (40000, 51):
the first column will be the same
the every element of the list will be in it's own column.
for my example:
[17, 1, 2, 3, ....],
[39, 44, 45, 45, ...]
How can I do it ?
np.hstack((arr[:,0].reshape(-1,1), np.array(arr[:,1].tolist())))
Example:
>>> arr
array([[75, list([90, 39, 63])],
[20, list([82, 92, 22])],
[80, list([12, 6, 89])],
[79, list([11, 96, 74])],
[96, list([26, 37, 65])]], dtype=object)
>>> np.hstack((arr[:,0].reshape(-1,1),np.array(arr[:,1].tolist()))).astype(int)
array([[75, 90, 39, 63],
[20, 82, 92, 22],
[80, 12, 6, 89],
[79, 11, 96, 74],
[96, 26, 37, 65]])
You can do this for each line of your ndarray , here is an example :
# X = [39, [44, 45, 45, ...]]
newX = numpy.ndarray(shape=(1,51))
new[0] = X[0] # adding the first element
# now the rest of elements
i = 0
for e in X[1] :
newX[i] = e
i = i + 1
You can make this process as a function and apply it in this way :
newArray = numpy.ndarray(shape=(40000,51))
i = 0
for x in oldArray :
Process(newArray[i],x)
i=i+1
I defined the source array (with shorter lists in column 1) as:
X = np.array([[17, [1, 2, 3, 4]], [39, [44, 45, 45, 46]]])
To do your task, define the following function:
def myExplode(row):
tbl = [row[0]]
tbl.extend(row[1])
return tbl
Then apply it to each row:
np.apply_along_axis(myExplode, axis=1, arr=X)
The result is:
array([[17, 1, 2, 3, 4],
[39, 44, 45, 45, 46]])
I have a Pandas DataFrame with MultiIndex on columns (lets say 3 levels):
MultiIndex(levels=[['BA-10.0', 'BA-2.5', ..., 'p'], ['41B004', '41B005', ..., 'T1M003', 'T1M011'], [25, 26, ..., 276, 277]],
labels=[[0, 0, 0, ..., 18, 19, 19], [4, 5, 6,..., 14, 12, 13], [24, 33, 47, ..., 114, 107, 113]],
names=['measurandkey', 'sitekey', 'channelid'])
When I iter through the first level and yield subset of DataFrame:
def cluster(df):
for key in df.columns.levels[0]:
yield df[key]
for subdf in cluster(df):
print(subdf.columns)
Columns index does have lost its first level, but the MultiIndex still contains reference to all other keys in sub-levels even if they are missing in the subset.
MultiIndex(levels=[['41B004', '41B005', '41B006', '41B008', '41B011', '41MEU1', '41N043', '41R001', '41R002', '41R012', '41WOL1', '41WOL2', 'T1M001', 'T1M003', 'T1M011'], [25, 26, 27, 28, 30, 31, 32, 3, ....
labels=[[4, 5, 6, 7, 9, 10], [24, 33, 47, 61, 83, 98]],
names=['sitekey', 'channelid'])
How can I force subdf to have its columns MultiIndex updated with only keys that are present?
def cluster(df):
for key in df.columns.levels[0]:
d = df[key]
d.columns = pd.MultiIndex.from_tuples(d.columns.to_series())
yield d
This is likely a really simple question, but it's one I've been confused about and stuck on for a while, so I'm hoping I might get some help.
I'm using cross validation to test my data set, but I'm finding that indexing the pandas df is not working as I'm expecting. Specifically, when I print out x_test, I find that there are no data points for x_test. In fact, there are indexes but no columns.
k = 10
N = len(df)
n = N/k + 1
for i in range(k):
print i*n, i*n+n
x_train = df.iloc[i*n: i*n+n]
y_train = df.iloc[i*n: i*n+n]
x_test = df.iloc[0:i*n, i*n+n:-1]
print x_test
Typical output:
0 751
Empty DataFrame
Columns: []
Index: []
751 1502
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]
I'm trying to work out how to get the data to show up. Any thoughts?
Why don't you use sklearn.cross_validation.KFold? There is a clear example on this site...
UPDATE:
At all subsets you have to specify columns as well: at x_train and x_test you have to exclude target column, at y_train only the target column have to be present. See slicing and indexing for more details.
target = 'target' # name of target column
list_features = df.columns.tolist() # use all columns at model training
list_features.remove(target) # excluding "target" column
k = 10
N = len(df)
n = int(N/k) + 1 # 'int()' is necessary at Python 3
for i in range(k):
print i*n, i*n+n
x_train = df.loc[i*n: i*n+n-1, list_features] # '.loc[]' is inclusive, that's why "-1" is present
y_train = df.loc[i*n: i*n+n-1, target] # specify columns after ","
x_test = df.loc[~df.index.isin(range(int(i*n), int(i*n+n))), list_features]
print x_test
In Python 2.7 using numpy or by any means if I had an array of any size and wanted to excluded certain values and output the new array how would I do that? Here is What I would like
[(1,2,3),
(4,5,6), then exclude [4,2,9] to make the array[(1,5,3),
(7,8,9)] (7,8,6)]
I would always be excluding data the same length as the row length and always only one entry per column. [(1,5,3)] would be another example of data I would want to excluded. So every time I loop the function it reduces the array row size by one. I would imagine I have to use a masked array or convert my mask to a masked array and subtract the two then maybe condense the output but I have no idea how. Thanks for your time.
You can do it very efficiently if you transform your 2-D array in an unraveled 1-D array. Then you repeat the array with the elements to be excluded, called e in order to do an element-wise comparison:
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
e = [1, 5, 3]
ar = a.T.ravel()
er = np.repeat(e, a.shape[0])
ans = ar[er != ar].reshape(a.shape[1], a.shape[0]-1).T
But it will work if each element in e only matches one row of a.
EDIT:
as suggested by #Jaime, you can avoid the ravel() and get the same result doing directly:
ans = a.T[(a != e).T].reshape(a.shape[1], a.shape[0]-1).T
To exclude vector e from matrix a:
import numpy as np
a = np.array([(1,2,3), (4,5,6), (7,8,9)])
e = [4,2,9]
print np.array([ [ i for i in a.transpose()[j] if i != e[j] ]
for j in range(len(e)) ]).transpose()
This would take some work to generalize, but here's something that can handle 2-d cases of the kind you describe. If passed unexpected input, this won't notice and will generate strange results, but it's at least a starting point:
def columnwise_compress(a, values):
a_shape = a.shape
a_trans_flat = a.transpose().reshape(-1)
compressed = a_trans_flat[~numpy.in1d(a_trans_flat, values)]
return compressed.reshape(a_shape[:-1] + ((a_shape[0] - 1),)).transpose()
Tested:
>>> columnwise_compress(numpy.arange(9).reshape(3, 3) + 1, [4, 2, 9])
array([[1, 5, 3],
[7, 8, 6]])
>>> columnwise_compress(numpy.arange(9).reshape(3, 3) + 1, [1, 5, 3])
array([[4, 2, 6],
[7, 8, 9]])
The difficulty is that you're asking for "compression" of a kind that numpy.compress doesn't do (removing different values for each column or row) and you're asking for compression along columns instead of rows. Compressing along rows is easier because it moves along the natural order of the values in memory; you might consider working with transposed arrays for that reason. If you want to do that, things become a bit simpler:
>>> a = numpy. array([[1, 4, 7],
... [2, 5, 8],
... [3, 6, 9]])
>>> a[~numpy.in1d(a, [4, 2, 9]).reshape(3, 3)].reshape(3, 2)
array([[1, 7],
[5, 8],
[3, 6]])
You'll still need to handle shape parameters intelligently if you do it this way, but it will still be simpler. Also, this assumes there are no duplicates in the original array; if there are, this could generate wrong results. Saullo's excellent answer partially avoids the problem, but any value-based approach isn't guaranteed to work unless you're certain that there aren't duplicate values in the columns.
In the spirit of #SaulloCastro's answer, but handling multiple occurrences of items, you can remove the first occurrence on each column doing the following:
def delete_skew_row(a, b) :
rows, cols = a.shape
row_to_remove = np.argmax(a == b, axis=0)
items_to_remove = np.ravel_multi_index((row_to_remove,
np.arange(cols)),
a.shape, order='F')
ret = np.delete(a.T, items_to_remove)
return np.ascontiguousarray(ret.reshape(cols,rows-1).T)
rows, cols = 5, 10
a = np.random.randint(100, size=(rows, cols))
b = np.random.randint(rows, size=(cols,))
b = a[b, np.arange(cols)]
>>> a
array([[50, 46, 85, 82, 27, 41, 45, 27, 17, 26],
[92, 35, 14, 34, 48, 27, 63, 58, 14, 18],
[90, 91, 39, 19, 90, 29, 67, 52, 68, 69],
[10, 99, 33, 58, 46, 71, 43, 23, 58, 49],
[92, 81, 64, 77, 61, 99, 40, 49, 49, 87]])
>>> b
array([92, 81, 14, 82, 46, 29, 67, 58, 14, 69])
>>> delete_skew_row(a, b)
array([[50, 46, 85, 34, 27, 41, 45, 27, 17, 26],
[90, 35, 39, 19, 48, 27, 63, 52, 68, 18],
[10, 91, 33, 58, 90, 71, 43, 23, 58, 49],
[92, 99, 64, 77, 61, 99, 40, 49, 49, 87]])