Your b is a list of arrays:
In[84]: b = list(map(lambda x: np.arange(x, x + 10), np.arange(0, 5)))
In[85]: b
Out[85]: [array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
array([2, 3, 4, 5, 6, 7, 8, 9, 10, 11]),
array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12]),
array([4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
]When used as an index:
In[86]: np.arange(1000)[b] /
usr / local / bin / ipython3: 1: FutureWarning: Using a non - tuple sequence
for multidimensional
indexing is deprecated;
use `arr[tuple(seq)]`
instead of `arr[seq]`.
In the future this will be interpreted as an array index, `arr[np.array(seq)]`,
which will result either in an error or a different result.
#!/usr/bin / python3
-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -
IndexError: too many indices
for arrayThere are various ways we can use your b to index a 1d array:
In[87]: np.arange(1000)[np.hstack(b)]
Out[87]:
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
])
In[89]: np.arange(1000)[np.array(b)] # or np.vstack(b)
Out[89]:
array([
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
[4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
])
In[90]: np.arange(1000)[b, ] # 1 d tuple containing b
Out[90]:
array([
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
[4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
])First of all, you're not slicing 0->9, 10->19, 20->29; your slices advance by 1 only: 0->9, 1->10, 11->20. Instead, try this:
n = 32 size = 10 b = list(map(lambda x: np.arange(x, x + size), np.arange(0, n * size, size)))
Note that if b is a ragged list - one or anycodings_index-error more of the arrays is shorter, only the anycodings_index-error hstack version works.,There are various ways we can use your b anycodings_index-error to index a 1d array:,Your b is a list of arrays:,First of all, you're not slicing anycodings_index-error 0->9, 10->19, 20->29; your anycodings_index-error slices advance by 1 only: 0->9, anycodings_index-error 1->10, 11->20. Instead, try this:
Let's create a large np array 'a' with anycodings_python 10,000 entries
import numpy as np
a = np.arange(0, 10000)Let's slice the array with 'n' indices anycodings_python 0->9, 1->10, 2->11, etc.
n = 32 b = list(map(lambda x: np.arange(x, x + 10), np.arange(0, n))) c = a[b]
Your b is a list of arrays:
In[84]: b = list(map(lambda x: np.arange(x, x + 10), np.arange(0, 5)))
In[85]: b
Out[85]: [array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
array([2, 3, 4, 5, 6, 7, 8, 9, 10, 11]),
array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12]),
array([4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
]When used as an index:
In[86]: np.arange(1000)[b] /
usr / local / bin / ipython3: 1: FutureWarning: Using a non - tuple sequence
for multidimensional
indexing is deprecated;
use `arr[tuple(seq)]`
instead of `arr[seq]`.
In the future this will be interpreted as an array index, `arr[np.array(seq)]`,
which will result either in an error or a different result.
#!/usr/bin / python3
-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -
IndexError: too many indices
for arrayThere are various ways we can use your b anycodings_index-error to index a 1d array:
In[87]: np.arange(1000)[np.hstack(b)]
Out[87]:
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
])
In[89]: np.arange(1000)[np.array(b)] # or np.vstack(b)
Out[89]:
array([
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
[4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
])
In[90]: np.arange(1000)[b, ] # 1 d tuple containing b
Out[90]:
array([
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
[4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
])First of all, you're not slicing anycodings_index-error 0->9, 10->19, 20->29; your anycodings_index-error slices advance by 1 only: 0->9, anycodings_index-error 1->10, 11->20. Instead, try this:
n = 32 size = 10 b = list(map(lambda x: np.arange(x, x + size), np.arange(0, n * size, size)))
I am reading a file in python using pandas and then saving it in a numpy array. The file has the dimension of 11303402 rows x 10 columns. I need to split the data for cross validation and for that I sliced the data into 11303402 rows x 9 columns of examples and 1 array of 11303402 rows x 1 col of labels. The following is the code:, 2 days ago Nov 24, 2021 · The indexerror: too many indices for an array means that you have a declared an array in a different dimension and trying to index it in another dimension. For example, … , 1 day ago Apr 22, 2022 · The Python IndexError: too many indices for array occurs when we specify too many index values when accessing a one-dimensional numpy array. To solve the error, … ,The display of third-party trademarks and trade names on this site does not necessarily indicate any affiliation or endorsement of FaqCode4U.com.
tdata = pd.read_csv('train.csv') tdata.columns = 'Arrival_Time', 'Creation_Time', 'x', 'y', 'z', 'User', 'Model', 'Device', 'sensor', 'gt'] User_Data = np.array(tdata) features = User_Data[: , 0: 9] labels = User_Data[: , 9: 10]Xt = features[idx[: , 0],: ]
pets = ["cat", "dog", "hamster"]pets = ["cat", "dog", "hamster"]print(pets[3])print(pets[3])-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- - IndexError Traceback(most recent call last) 1 print(pets[3]) IndexError: list index out of rangeimport numpy as np arr = np.array([[2,3,4,5,6], [8, 4, 3, 2, 1]]) print('3rd element on 1st row: ', arr[0,2])3 rd element on 1 st row: 4
3rd element on 1st row: 4import numpy as np x = np.array([45, 12, 55, 99, 10, 5, 2]) print(x[0, 3])Tell us what’s happening: How can I solve this Error? : IndexError: too many indices for array: array is 1-dimensional, but 3 were indexed,How can I solve this Error? :,If so, please edit your post to include it in the Tell us what’s happening section.,So yeah, you are telling the list to grab the entry on index (0,1,2) - which is a tuple and thus a 3D coordinate. Hence it fails.
if(len(list) !=9):
raise ValueError("List not contain nine numbers.")else:
ls = np.array(list) print(ls) mean_rows = [ls[(0, 1, 2)].mean(), ls[(3, 4, 5)].mean(), ls[(6, 7, 8)].mean()] mean_columns = [ls[(0, 3, 6)].mean(), ls[(1, 4, 7)].mean(), ls[(2, 5, 8)].mean()]
What happens if we only supply one index to our array? It may be surprising that grades[0] does not throw an error since we are specifying only one index to access data from a 2-dimensional array. Instead, NumPy it will return all of the exam scores for student-0 (Ashley):,This is because NumPy will automatically insert trailing slices for you if you don’t provide as many indices as there are dimensions for your array. grades[0] was treated as grades[0, :].,The output of grades[:, :1] might look somewhat funny. Because the axis-1 slice only includes one column of numbers, the shape of the resulting array is (3, 1). 0 is thus only valid (non-negative) index for axis-1, since there is only one column to specify in the array.,Similar to Python’s sequences, we use 0-based indices and slicing to access the content of an array. However, we must specify an index/slice for each dimension of an array:
# A 0 - D array
np.array(8)
# A 1 - D array, shape - (3, )
np.array([2.3, 0.1, -9.1])
# A 2 - D array, shape - (3, 2)
np.array([
[93, 95],
[84, 100],
[99, 87]
])
# A 3 - D array, shape - (2, 2, 2)
np.array([
[
[0, 1],
[2, 3]
],
[
[4, 5],
[6, 7]
]
])>>>
import numpy as np
# A 3 - D array >>>
x = np.array([
[
[0, 1],
...[2, 3]
],
...
...[
[4, 5],
...[6, 7]
]
])
# get: sheet - 0, both rows, flip order of columns >>>
x[0,: , ::-1]
array([
[1, 0],
[3, 2]
])>>> simple_array = np.array([2.3, 0.1, -9.1])
+ -- -- -- + -- -- -- + -- -- -- + | 2.3 | 0.1 | -9.1 | + -- -- -- + -- -- -- + -- -- -- + 0 1 2 - 3 - 2 - 1
>>> simple_array[0]
2.3
>>>
simple_array[-2]
0.1
>>>
simple_array[1: 3]
array([0.1, -9.1])
>>>
simple_array[3]
IndexError: index 3 is out of bounds
for axis 0 with size 3# using a 1 - dimensional array to store the grades >>> grades = np.array([93, 95, 84, 100, 99, 87])