For example:
import cPickle
from numpy
import array, ones
a = array((5, 2))
b = ones((10, 2))
c = array((20, 2))
all = [a, b, c]
cPickle.dump(all, open('all_my_arrays', 'w'))You can then retrieve them with:
all2 = cPickle.load(open('all_my_arrays'))You can split an array into several smaller arrays using hsplit. You can specify either the number of equally shaped arrays to return or the columns after which the division should occur.,You may want to take a section of your array or specific array elements to use in further analysis or additional operations. To do that, you’ll need to subset, slice, and/or index your arrays.,You can explicitly convert a 1D array with either a row vector or a column vector using np.newaxis. For example, you can convert a 1D array to a row vector by inserting an axis along the first dimension:,ndarray.shape will display a tuple of integers that indicate the number of elements stored along each dimension of the array. If, for example, you have a 2-D array with 2 rows and 3 columns, the shape of your array is (2, 3).
conda install numpy
pip install numpy
import numpy as np>>> a = np.arange(6) >>> a2 = a[np.newaxis,: ] >>> a2.shape(1, 6)
>>> a = np.array([1, 2, 3, 4, 5, 6])
>>> a = np.array([ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12] ])
There are different methods by which we can save the NumPy array into a CSV file Method 1: Using Dataframe.to_csv (). This method is used to write a Dataframe into a CSV file. Example: Converting the array into pandas Dataframe and then saving it to CSV format. , 1 week ago Aug 29, 2020 · CSV file format is the easiest and useful format for storing data. There are different methods by which we can save the NumPy array into a CSV file. Method 1: Using Dataframe.to_csv (). This method is used to write a Dataframe into a CSV file. Example: Converting the array into pandas Dataframe and then saving it to CSV format. Python3. ,I am working with 1d numpy arrays, first doing some math then saving everything to a single csv file. The data sets are often of different lengths and I cannot flatten them together. This is the best I could come up with but there must be a more elegant way.,The display of third-party trademarks and trade names on this site does not necessarily indicate any affiliation or endorsement of FaqCode4U.com.
import numpy as np
import pandas as pd
import os array1 = np.linspace(1, 20, 10) array2 = np.linspace(12, 230, 10) array3 = np.linspace(7, 82, 20) array4 = np.linspace(6, 55, 20) output1 = np.column_stack((array1.flatten(), array2.flatten())) #saving first array set to file np.savetxt("tempfile1.csv", output1, delimiter = ',') output2 = np.column_stack((array3.flatten(), array4.flatten())) # doing it again second array np.savetxt("tempfile2.csv", output2, delimiter = ',') a = pd.read_csv('tempfile1.csv') # use pandas to read both files b = pd.read_csv("tempfile2.csv") merged = b.join(a, rsuffix = '*') # merge with panda
for single file os.remove('tempfile1.csv') os.remove("tempfile2.csv") # delete temp files merged.to_csv('savefile.csv', index = False) # save merged fileFor more details on using NumPy’s sorting methods, and more advanced techniques like indirect sorts, see Chapter 12. Several other kinds of data manipulations related to sorting (for example, sorting a table of data by one or more columns) are also to be found in pandas.,Fast vectorized array operations for data munging and cleaning, subsetting and filtering, transformation, and any other kinds of computations,Data alignment and relational data manipulations for merging and joining together heterogeneous data sets,The data type or dtype is a special object containing the information the ndarray needs to interpret a chunk of memory as a particular type of data:
In[83]: names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
In[84]: data = np.random.randn(7, 4)
In[85]: names
Out[85]:
array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'],
dtype = '|S4')
In[86]: data
Out[86]:
array([
[-0.048, 0.5433, -0.2349, 1.2792],
[-0.268, 0.5465, 0.0939, -2.0445],
[-0.047, -2.026, 0.7719, 0.3103],
[2.1452, 0.8799, -0.0523, 0.0672],
[-1.0023, -0.1698, 1.1503, 1.7289],
[0.1913, 0.4544, 0.4519, 0.5535],
[0.5994, 0.8174, -0.9297, -1.2564]
])Last Updated : 29 Jul, 2022
Median = 3
10
10.000000
Median of the two arrays are 3
09/15/2021
The following is a declaration of a single-dimensional array that has three elements, each of which is a single-dimensional array of integers:
int[][] jaggedArray = new int[3][];Before you can use jaggedArray, its elements must be initialized. You can initialize the elements like this:
jaggedArray[0] = new int[5];
jaggedArray[1] = new int[4];
jaggedArray[2] = new int[2];It is also possible to use initializers to fill the array elements with values, in which case you do not need the array size. For example:
jaggedArray[0] = new int[] {
1,
3,
5,
7,
9
};
jaggedArray[1] = new int[] {
0,
2,
4,
6
};
jaggedArray[2] = new int[] {
11,
22
};You can use the following shorthand form. Notice that you cannot omit the new operator from the elements initialization because there is no default initialization for the elements:
int[][] jaggedArray3 = {
new int[] {
1,
3,
5,
7,
9
},
new int[] {
0,
2,
4,
6
},
new int[] {
11,
22
}
};You can access individual array elements like these examples:
// Assign 77 to the second element ([1]) of the first array ([0]):
jaggedArray3[0][1] = 77;
// Assign 88 to the second element ([1]) of the third array ([2]):
jaggedArray3[2][1] = 88;The following example creates an array of two 3x3 matrices each with 3 rows and 3 columns.,Arrays are the R data objects which can store data in more than two dimensions. For example − If we create an array of dimension (2, 3, 4) then it creates 4 rectangular matrices each with 2 rows and 3 columns. Arrays can store only data type.,We can give names to the rows, columns and matrices in the array by using the dimnames parameter.,We use the apply() function below to calculate the sum of the elements in the rows of an array across all the matrices.
# Create two vectors of different lengths. vector1 < -c(5, 9, 3) vector2 < -c(10, 11, 12, 13, 14, 15) # Take these vectors as input to the array. result < -array(c(vector1, vector2), dim = c(3, 3, 2)) print(result)
When we execute the above code, it produces the following result −
, , 1
[, 1][, 2][, 3]
[1, ] 5 10 13[2, ] 9 11 14[3, ] 3 12 15
, , 2
[, 1][, 2][, 3]
[1, ] 5 10 13
[2, ] 9 11 14[3, ] 3 12 15# Create two vectors of different lengths.
vector1 < -c(5, 9, 3)
vector2 < -c(10, 11, 12, 13, 14, 15)
column.names < -c("COL1", "COL2", "COL3")
row.names < -c("ROW1", "ROW2", "ROW3")
matrix.names < -c("Matrix1", "Matrix2")
# Take these vectors as input to the array.
result < -array(c(vector1, vector2), dim = c(3, 3, 2), dimnames = list(row.names, column.names,
matrix.names))
print(result)# Create two vectors of different lengths. vector1 < -c(5, 9, 3) vector2 < -c(10, 11, 12, 13, 14, 15) # Take these vectors as input to the array. array1 < -array(c(vector1, vector2), dim = c(3, 3, 2)) # Create two vectors of different lengths. vector3 < -c(9, 1, 0) vector4 < -c(6, 0, 11, 3, 14, 1, 2, 6, 9) array2 < -array(c(vector1, vector2), dim = c(3, 3, 2)) # create matrices from these arrays. matrix1 < -array1[, , 2] matrix2 < -array2[, , 2] # Add the matrices. result < -matrix1 + matrix2 print(result)
Syntax
apply(x, margin, fun)