Description
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Calculate the mean of array ignoring the NaN value
|
https://www.geeksforgeeks.org/python-numpy-nanmean-function/
|
# Python code to demonstrate the
# use of numpy.nanmean
import numpy as np
# create 2d array with nan value.
arr = np.array([[20, 15, 37], [47, 13, np.nan]])
print("Shape of array is", arr.shape)
print("Mean of array without using nanmean function:", np.mean(arr))
print("Using nanmean function:", np.nanmean(arr))
|
#Output : Shape of array is (2, 3)
|
Calculate the mean of array ignoring the NaN value
# Python code to demonstrate the
# use of numpy.nanmean
import numpy as np
# create 2d array with nan value.
arr = np.array([[20, 15, 37], [47, 13, np.nan]])
print("Shape of array is", arr.shape)
print("Mean of array without using nanmean function:", np.mean(arr))
print("Using nanmean function:", np.nanmean(arr))
#Output : Shape of array is (2, 3)
[END]
|
Calculate the mean of array ignoring the NaN value
|
https://www.geeksforgeeks.org/python-numpy-nanmean-function/
|
# Python code to demonstrate the
# use of numpy.nanmean
# with axis = 0
import numpy as np
# create 2d matrix with nan value
arr = np.array([[32, 20, 24], [47, 63, np.nan], [17, 28, np.nan], [10, 8, 9]])
print("Shape of array is", arr.shape)
print("Mean of array with axis = 0:", np.mean(arr, axis=0))
print("Using nanmedian function:", np.nanmean(arr, axis=0))
|
#Output : Shape of array is (2, 3)
|
Calculate the mean of array ignoring the NaN value
# Python code to demonstrate the
# use of numpy.nanmean
# with axis = 0
import numpy as np
# create 2d matrix with nan value
arr = np.array([[32, 20, 24], [47, 63, np.nan], [17, 28, np.nan], [10, 8, 9]])
print("Shape of array is", arr.shape)
print("Mean of array with axis = 0:", np.mean(arr, axis=0))
print("Using nanmedian function:", np.nanmean(arr, axis=0))
#Output : Shape of array is (2, 3)
[END]
|
Calculate the mean of array ignoring the NaN value
|
https://www.geeksforgeeks.org/python-numpy-nanmean-function/
|
# Python code to demonstrate the
# use of numpy.nanmedian
# with axis = 1
import numpy as np
# create 2d matrix with nan value
arr = np.array([[32, 20, 24], [47, 63, np.nan], [17, 28, np.nan], [10, 8, 9]])
print("Shape of array is", arr.shape)
print("Mean of array with axis = 1:", np.mean(arr, axis=1))
print("Using nanmedian function:", np.nanmean(arr, axis=1))
|
#Output : Shape of array is (2, 3)
|
Calculate the mean of array ignoring the NaN value
# Python code to demonstrate the
# use of numpy.nanmedian
# with axis = 1
import numpy as np
# create 2d matrix with nan value
arr = np.array([[32, 20, 24], [47, 63, np.nan], [17, 28, np.nan], [10, 8, 9]])
print("Shape of array is", arr.shape)
print("Mean of array with axis = 1:", np.mean(arr, axis=1))
print("Using nanmedian function:", np.nanmean(arr, axis=1))
#Output : Shape of array is (2, 3)
[END]
|
Get the mean value from given matrix
|
https://www.geeksforgeeks.org/python-numpy-matrix-mean/
|
# import the important module in python
import numpy as np
# make matrix with numpy
gfg = np.matrix("[64, 1; 12, 3]")
# applying matrix.mean() method
geeks = gfg.mean()
print(geeks)
|
#Output :
|
Get the mean value from given matrix
# import the important module in python
import numpy as np
# make matrix with numpy
gfg = np.matrix("[64, 1; 12, 3]")
# applying matrix.mean() method
geeks = gfg.mean()
print(geeks)
#Output :
[END]
|
Get the mean value from given matrix
|
https://www.geeksforgeeks.org/python-numpy-matrix-mean/
|
# import the important module in python
import numpy as np
# make a matrix with numpy
gfg = np.matrix("[1, 2, 3; 4, 5, 6; 7, 8, 9]")
# applying matrix.mean() method
geeks = gfg.mean()
print(geeks)
|
#Output :
|
Get the mean value from given matrix
# import the important module in python
import numpy as np
# make a matrix with numpy
gfg = np.matrix("[1, 2, 3; 4, 5, 6; 7, 8, 9]")
# applying matrix.mean() method
geeks = gfg.mean()
print(geeks)
#Output :
[END]
|
Compute the variance of the NumPy array
|
https://www.geeksforgeeks.org/numpy-var-in-python/
|
# Python Program illustrating
# numpy.var() method
import numpy as np
# 1D array
arr = [20, 2, 7, 1, 34]
print("arr : ", arr)
print("var of arr : ", np.var(arr))
print("\nvar of arr : ", np.var(arr, dtype=np.float32))
print("\nvar of arr : ", np.var(arr, dtype=np.float64))
|
#Output :
|
Compute the variance of the NumPy array
# Python Program illustrating
# numpy.var() method
import numpy as np
# 1D array
arr = [20, 2, 7, 1, 34]
print("arr : ", arr)
print("var of arr : ", np.var(arr))
print("\nvar of arr : ", np.var(arr, dtype=np.float32))
print("\nvar of arr : ", np.var(arr, dtype=np.float64))
#Output :
[END]
|
Compute the variance of the NumPy array
|
https://www.geeksforgeeks.org/numpy-var-in-python/
|
# Python Program illustrating
# numpy.var() method
import numpy as np
# 2D array
arr = [
[2, 2, 2, 2, 2],
[15, 6, 27, 8, 2],
[
23,
2,
54,
1,
2,
],
[11, 44, 34, 7, 2],
]
# var of the flattened array
print("\nvar of arr, axis = None : ", np.var(arr))
# var along the axis = 0
print("\nvar of arr, axis = 0 : ", np.var(arr, axis=0))
# var along the axis = 1
print("\nvar of arr, axis = 1 : ", np.var(arr, axis=1))
|
#Output :
|
Compute the variance of the NumPy array
# Python Program illustrating
# numpy.var() method
import numpy as np
# 2D array
arr = [
[2, 2, 2, 2, 2],
[15, 6, 27, 8, 2],
[
23,
2,
54,
1,
2,
],
[11, 44, 34, 7, 2],
]
# var of the flattened array
print("\nvar of arr, axis = None : ", np.var(arr))
# var along the axis = 0
print("\nvar of arr, axis = 0 : ", np.var(arr, axis=0))
# var along the axis = 1
print("\nvar of arr, axis = 1 : ", np.var(arr, axis=1))
#Output :
[END]
|
Compute the standard deviation of the NumPy array
|
https://www.geeksforgeeks.org/numpy-std-in-python/
|
# Python Program illustrating
# numpy.std() method
import numpy as np
# 1D array
arr = [20, 2, 7, 1, 34]
print("arr : ", arr)
print("std of arr : ", np.std(arr))
print("\nMore precision with float32")
print("std of arr : ", np.std(arr, dtype=np.float32))
print("\nMore accuracy with float64")
print("std of arr : ", np.std(arr, dtype=np.float64))
|
#Output :
|
Compute the standard deviation of the NumPy array
# Python Program illustrating
# numpy.std() method
import numpy as np
# 1D array
arr = [20, 2, 7, 1, 34]
print("arr : ", arr)
print("std of arr : ", np.std(arr))
print("\nMore precision with float32")
print("std of arr : ", np.std(arr, dtype=np.float32))
print("\nMore accuracy with float64")
print("std of arr : ", np.std(arr, dtype=np.float64))
#Output :
[END]
|
Compute the standard deviation of the NumPy array
|
https://www.geeksforgeeks.org/numpy-std-in-python/
|
# Python Program illustrating
# numpy.std() method
import numpy as np
# 2D array
arr = [
[2, 2, 2, 2, 2],
[15, 6, 27, 8, 2],
[
23,
2,
54,
1,
2,
],
[11, 44, 34, 7, 2],
]
# std of the flattened array
print("\nstd of arr, axis = None : ", np.std(arr))
# std along the axis = 0
print("\nstd of arr, axis = 0 : ", np.std(arr, axis=0))
# std along the axis = 1
print("\nstd of arr, axis = 1 : ", np.std(arr, axis=1))
|
#Output :
|
Compute the standard deviation of the NumPy array
# Python Program illustrating
# numpy.std() method
import numpy as np
# 2D array
arr = [
[2, 2, 2, 2, 2],
[15, 6, 27, 8, 2],
[
23,
2,
54,
1,
2,
],
[11, 44, 34, 7, 2],
]
# std of the flattened array
print("\nstd of arr, axis = None : ", np.std(arr))
# std along the axis = 0
print("\nstd of arr, axis = 0 : ", np.std(arr, axis=0))
# std along the axis = 1
print("\nstd of arr, axis = 1 : ", np.std(arr, axis=1))
#Output :
[END]
|
Compute pearson product-moment correlementsation coefficients of two given NumPy arrays
|
https://www.geeksforgeeks.org/compute-pearson-product-moment-correlation-coefficients-of-two-given-numpy-arrays/
|
# import library
import numpy as np
# create numpy 1d-array
array1 = np.array([0, 1, 2])
array2 = np.array([3, 4, 5])
# pearson product-moment correlation
# coefficients of the arrays
rslt = np.corrcoef(array1, array2)
print(rslt)
|
#Output :
|
Compute pearson product-moment correlementsation coefficients of two given NumPy arrays
# import library
import numpy as np
# create numpy 1d-array
array1 = np.array([0, 1, 2])
array2 = np.array([3, 4, 5])
# pearson product-moment correlation
# coefficients of the arrays
rslt = np.corrcoef(array1, array2)
print(rslt)
#Output :
[END]
|
Compute pearson product-moment correlementsation coefficients of two given NumPy arrays
|
https://www.geeksforgeeks.org/compute-pearson-product-moment-correlation-coefficients-of-two-given-numpy-arrays/
|
# import numpy library
import numpy as np
# create a numpy 1d-array
array1 = np.array([2, 4, 8])
array2 = np.array([3, 2, 1])
# pearson product-moment correlation
# coefficients of the arrays
rslt2 = np.corrcoef(array1, array2)
print(rslt2)
|
#Output :
|
Compute pearson product-moment correlementsation coefficients of two given NumPy arrays
# import numpy library
import numpy as np
# create a numpy 1d-array
array1 = np.array([2, 4, 8])
array2 = np.array([3, 2, 1])
# pearson product-moment correlation
# coefficients of the arrays
rslt2 = np.corrcoef(array1, array2)
print(rslt2)
#Output :
[END]
|
Calculate the mean across dimension in a 2D NumPy array
|
https://www.geeksforgeeks.org/calculate-the-mean-across-dimension-in-a-2d-numpy-array/
|
# Importing Library
import numpy as np
# creating 2d array
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# Calculating mean across Rows
row_mean = np.mean(arr, axis=1)
row1_mean = row_mean[0]
print("Mean of Row 1 is", row1_mean)
row2_mean = row_mean[1]
print("Mean of Row 2 is", row2_mean)
row3_mean = row_mean[2]
print("Mean of Row 3 is", row3_mean)
# Calculating mean across Columns
column_mean = np.mean(arr, axis=0)
column1_mean = column_mean[0]
print("Mean of column 1 is", column1_mean)
column2_mean = column_mean[1]
print("Mean of column 2 is", column2_mean)
column3_mean = column_mean[2]
print("Mean of column 3 is", column3_mean)
|
#Output : Mean of Row 1 is 2.0
|
Calculate the mean across dimension in a 2D NumPy array
# Importing Library
import numpy as np
# creating 2d array
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# Calculating mean across Rows
row_mean = np.mean(arr, axis=1)
row1_mean = row_mean[0]
print("Mean of Row 1 is", row1_mean)
row2_mean = row_mean[1]
print("Mean of Row 2 is", row2_mean)
row3_mean = row_mean[2]
print("Mean of Row 3 is", row3_mean)
# Calculating mean across Columns
column_mean = np.mean(arr, axis=0)
column1_mean = column_mean[0]
print("Mean of column 1 is", column1_mean)
column2_mean = column_mean[1]
print("Mean of column 2 is", column2_mean)
column3_mean = column_mean[2]
print("Mean of column 3 is", column3_mean)
#Output : Mean of Row 1 is 2.0
[END]
|
Calculate the average, variance and standard deviation in Python using NumPy
|
https://www.geeksforgeeks.org/calculate-the-average-variance-and-standard-deviation-in-python-using-numpy/
|
# Python program to get average of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [2, 4, 4, 4, 5, 5, 7, 9]
# Calculating average using average()
print(np.average(list))
|
#Output : 5.0
|
Calculate the average, variance and standard deviation in Python using NumPy
# Python program to get average of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [2, 4, 4, 4, 5, 5, 7, 9]
# Calculating average using average()
print(np.average(list))
#Output : 5.0
[END]
|
Calculate the average, variance and standard deviation in Python using NumPy
|
https://www.geeksforgeeks.org/calculate-the-average-variance-and-standard-deviation-in-python-using-numpy/
|
# Python program to get average of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [2, 40, 2, 502, 177, 7, 9]
# Calculating average using average()
print(np.average(list))
|
#Output : 5.0
|
Calculate the average, variance and standard deviation in Python using NumPy
# Python program to get average of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [2, 40, 2, 502, 177, 7, 9]
# Calculating average using average()
print(np.average(list))
#Output : 5.0
[END]
|
Calculate the average, variance and standard deviation in Python using NumPy
|
https://www.geeksforgeeks.org/calculate-the-average-variance-and-standard-deviation-in-python-using-numpy/
|
# Python program to get variance of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [2, 4, 4, 4, 5, 5, 7, 9]
# Calculating variance using var()
print(np.var(list))
|
#Output : 5.0
|
Calculate the average, variance and standard deviation in Python using NumPy
# Python program to get variance of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [2, 4, 4, 4, 5, 5, 7, 9]
# Calculating variance using var()
print(np.var(list))
#Output : 5.0
[END]
|
Calculate the average, variance and standard deviation in Python using NumPy
|
https://www.geeksforgeeks.org/calculate-the-average-variance-and-standard-deviation-in-python-using-numpy/
|
# Python program to get variance of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [212, 231, 234, 564, 235]
# Calculating variance using var()
print(np.var(list))
|
#Output : 5.0
|
Calculate the average, variance and standard deviation in Python using NumPy
# Python program to get variance of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [212, 231, 234, 564, 235]
# Calculating variance using var()
print(np.var(list))
#Output : 5.0
[END]
|
Calculate the average, variance and standard deviation in Python using NumPy
|
https://www.geeksforgeeks.org/calculate-the-average-variance-and-standard-deviation-in-python-using-numpy/
|
# Python program to get
# standard deviation of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [2, 4, 4, 4, 5, 5, 7, 9]
# Calculating standard
# deviation using var()
print(np.std(list))
|
#Output : 5.0
|
Calculate the average, variance and standard deviation in Python using NumPy
# Python program to get
# standard deviation of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [2, 4, 4, 4, 5, 5, 7, 9]
# Calculating standard
# deviation using var()
print(np.std(list))
#Output : 5.0
[END]
|
Calculate the average, variance and standard deviation in Python using NumPy
|
https://www.geeksforgeeks.org/calculate-the-average-variance-and-standard-deviation-in-python-using-numpy/
|
# Python program to get
# standard deviation of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [290, 124, 127, 899]
# Calculating standard
# deviation using var()
print(np.std(list))
|
#Output : 5.0
|
Calculate the average, variance and standard deviation in Python using NumPy
# Python program to get
# standard deviation of a list
# Importing the NumPy module
import numpy as np
# Taking a list of elements
list = [290, 124, 127, 899]
# Calculating standard
# deviation using var()
print(np.std(list))
#Output : 5.0
[END]
|
Describe a NumPy Array in Python
|
https://www.geeksforgeeks.org/describe-a-numpy-array-in-python/
|
import numpy as np
# sample array
arr = np.array([4, 5, 8, 5, 6, 4, 9, 2, 4, 3, 6])
print(arr)
|
#Output : [4 5 8 5 6 4 9 2 4 3 6]
|
Describe a NumPy Array in Python
import numpy as np
# sample array
arr = np.array([4, 5, 8, 5, 6, 4, 9, 2, 4, 3, 6])
print(arr)
#Output : [4 5 8 5 6 4 9 2 4 3 6]
[END]
|
Describe a NumPy Array in Python
|
https://www.geeksforgeeks.org/describe-a-numpy-array-in-python/
|
import numpy as np
arr = np.array([4, 5, 8, 5, 6, 4, 9, 2, 4, 3, 6])
# measures of central tendency
mean = np.mean(arr)
median = np.median(arr)
print("Array =", arr)
print("Mean =", mean)
print("Median =", median)
|
#Output : [4 5 8 5 6 4 9 2 4 3 6]
|
Describe a NumPy Array in Python
import numpy as np
arr = np.array([4, 5, 8, 5, 6, 4, 9, 2, 4, 3, 6])
# measures of central tendency
mean = np.mean(arr)
median = np.median(arr)
print("Array =", arr)
print("Mean =", mean)
print("Median =", median)
#Output : [4 5 8 5 6 4 9 2 4 3 6]
[END]
|
Describe a NumPy Array in Python
|
https://www.geeksforgeeks.org/describe-a-numpy-array-in-python/
|
import numpy as np
arr = np.array([4, 5, 8, 5, 6, 4, 9, 2, 4, 3, 6])
# measures of dispersion
min = np.amin(arr)
max = np.amax(arr)
range = np.ptp(arr)
variance = np.var(arr)
sd = np.std(arr)
print("Array =", arr)
print("Measures of Dispersion")
print("Minimum =", min)
print("Maximum =", max)
print("Range =", range)
print("Variance =", variance)
print("Standard Deviation =", sd)
|
#Output : [4 5 8 5 6 4 9 2 4 3 6]
|
Describe a NumPy Array in Python
import numpy as np
arr = np.array([4, 5, 8, 5, 6, 4, 9, 2, 4, 3, 6])
# measures of dispersion
min = np.amin(arr)
max = np.amax(arr)
range = np.ptp(arr)
variance = np.var(arr)
sd = np.std(arr)
print("Array =", arr)
print("Measures of Dispersion")
print("Minimum =", min)
print("Maximum =", max)
print("Range =", range)
print("Variance =", variance)
print("Standard Deviation =", sd)
#Output : [4 5 8 5 6 4 9 2 4 3 6]
[END]
|
Describe a NumPy Array in Python
|
https://www.geeksforgeeks.org/describe-a-numpy-array-in-python/
|
import numpy as np
arr = np.array([4, 5, 8, 5, 6, 4, 9, 2, 4, 3, 6])
# measures of central tendency
mean = np.mean(arr)
median = np.median(arr)
# measures of dispersion
min = np.amin(arr)
max = np.amax(arr)
range = np.ptp(arr)
variance = np.var(arr)
sd = np.std(arr)
print("Descriptive analysis")
print("Array =", arr)
print("Measures of Central Tendency")
print("Mean =", mean)
print("Median =", median)
print("Measures of Dispersion")
print("Minimum =", min)
print("Maximum =", max)
print("Range =", range)
print("Variance =", variance)
print("Standard Deviation =", sd)
|
#Output : [4 5 8 5 6 4 9 2 4 3 6]
|
Describe a NumPy Array in Python
import numpy as np
arr = np.array([4, 5, 8, 5, 6, 4, 9, 2, 4, 3, 6])
# measures of central tendency
mean = np.mean(arr)
median = np.median(arr)
# measures of dispersion
min = np.amin(arr)
max = np.amax(arr)
range = np.ptp(arr)
variance = np.var(arr)
sd = np.std(arr)
print("Descriptive analysis")
print("Array =", arr)
print("Measures of Central Tendency")
print("Mean =", mean)
print("Median =", median)
print("Measures of Dispersion")
print("Minimum =", min)
print("Maximum =", max)
print("Range =", range)
print("Variance =", variance)
print("Standard Deviation =", sd)
#Output : [4 5 8 5 6 4 9 2 4 3 6]
[END]
|
Define a polynomial function
|
https://www.geeksforgeeks.org/numpy-poly1d-in-python/
|
# Python code explaining
# numpy.poly1d()
# importing libraries
import numpy as np
# Constructing polynomial
p1 = np.poly1d([1, 2])
p2 = np.poly1d([4, 9, 5, 4])
print("P1 : ", p1)
print("\n p2 : \n", p2)
# Solve for x = 2
print("\n\np1 at x = 2 : ", p1(2))
print("p2 at x = 2 : ", p2(2))
# Finding Roots
print("\n\nRoots of P1 : ", p1.r)
print("Roots of P2 : ", p2.r)
# Finding Coefficients
print("\n\nCoefficients of P1 : ", p1.c)
print("Coefficients of P2 : ", p2.coeffs)
# Finding Order
print("\n\nOrder / Degree of P1 : ", p1.o)
print("Order / Degree of P2 : ", p2.order)
|
#Output : P1 :
|
Define a polynomial function
# Python code explaining
# numpy.poly1d()
# importing libraries
import numpy as np
# Constructing polynomial
p1 = np.poly1d([1, 2])
p2 = np.poly1d([4, 9, 5, 4])
print("P1 : ", p1)
print("\n p2 : \n", p2)
# Solve for x = 2
print("\n\np1 at x = 2 : ", p1(2))
print("p2 at x = 2 : ", p2(2))
# Finding Roots
print("\n\nRoots of P1 : ", p1.r)
print("Roots of P2 : ", p2.r)
# Finding Coefficients
print("\n\nCoefficients of P1 : ", p1.c)
print("Coefficients of P2 : ", p2.coeffs)
# Finding Order
print("\n\nOrder / Degree of P1 : ", p1.o)
print("Order / Degree of P2 : ", p2.order)
#Output : P1 :
[END]
|
Define a polynomial function
|
https://www.geeksforgeeks.org/numpy-poly1d-in-python/
|
# Python code explaining
# numpy.poly1d()
# importing libraries
import numpy as np
# Constructing polynomial
p1 = np.poly1d([1, 2])
p2 = np.poly1d([4, 9, 5, 4])
print("P1 : ", p1)
print("\n p2 : \n", p2)
print("\n\np1 ^ 2 : \n", p1**2)
print("p2 ^ 2 : \n", np.square(p2))
p3 = np.poly1d([1, 2], variable="y")
print("\n\np3 : ", p3)
print("\n\np1 * p2 : \n", p1 * p2)
print("\nMultiplying two polynimials : \n", np.poly1d([1, -1]) * np.poly1d([1, -2]))
|
#Output : P1 :
|
Define a polynomial function
# Python code explaining
# numpy.poly1d()
# importing libraries
import numpy as np
# Constructing polynomial
p1 = np.poly1d([1, 2])
p2 = np.poly1d([4, 9, 5, 4])
print("P1 : ", p1)
print("\n p2 : \n", p2)
print("\n\np1 ^ 2 : \n", p1**2)
print("p2 ^ 2 : \n", np.square(p2))
p3 = np.poly1d([1, 2], variable="y")
print("\n\np3 : ", p3)
print("\n\np1 * p2 : \n", p1 * p2)
print("\nMultiplying two polynimials : \n", np.poly1d([1, -1]) * np.poly1d([1, -2]))
#Output : P1 :
[END]
|
How to add one polynomial to another listr using NumPy in Python?
|
https://www.geeksforgeeks.org/how-to-add-one-polynomial-to-another-using-numpy-in-python/
|
# importing package
import numpy
# define the polynomials
# p(x) = 5(x**2) + (-2)x +5
px = (5, -2, 5)
# q(x) = 2(x**2) + (-5)x +2
qx = (2, -5, 2)
# add the polynomials
rx = numpy.polynomial.polynomial.polyadd(px, qx)
# print the resultant polynomial
print(rx)
|
#Output : If p(x) = A3 x2 + A2 x + A1
|
How to add one polynomial to another listr using NumPy in Python?
# importing package
import numpy
# define the polynomials
# p(x) = 5(x**2) + (-2)x +5
px = (5, -2, 5)
# q(x) = 2(x**2) + (-5)x +2
qx = (2, -5, 2)
# add the polynomials
rx = numpy.polynomial.polynomial.polyadd(px, qx)
# print the resultant polynomial
print(rx)
#Output : If p(x) = A3 x2 + A2 x + A1
[END]
|
How to add one polynomial to another listr using NumPy in Python?
|
https://www.geeksforgeeks.org/how-to-add-one-polynomial-to-another-using-numpy-in-python/
|
# importing package
import numpy
# define the polynomials
# p(x) = 2.2
px = (0, 0, 2.2)
# q(x) = 9.8(x**2) + 4
qx = (9.8, 0, 4)
# add the polynomials
rx = numpy.polynomial.polynomial.polyadd(px, qx)
# print the resultant polynomial
print(rx)
|
#Output : If p(x) = A3 x2 + A2 x + A1
|
How to add one polynomial to another listr using NumPy in Python?
# importing package
import numpy
# define the polynomials
# p(x) = 2.2
px = (0, 0, 2.2)
# q(x) = 9.8(x**2) + 4
qx = (9.8, 0, 4)
# add the polynomials
rx = numpy.polynomial.polynomial.polyadd(px, qx)
# print the resultant polynomial
print(rx)
#Output : If p(x) = A3 x2 + A2 x + A1
[END]
|
How to add one polynomial to another listr using NumPy in Python?
|
https://www.geeksforgeeks.org/how-to-add-one-polynomial-to-another-using-numpy-in-python/
|
# importing package
import numpy
# define the polynomials
# p(x) = (5/3)x
px = (0, 5 / 3, 0)
# q(x) = (-7/4)(x**2) + (9/5)
qx = (-7 / 4, 0, 9 / 5)
# add the polynomials
rx = numpy.polynomial.polynomial.polyadd(px, qx)
# print the resultant polynomial
print(rx)
|
#Output : If p(x) = A3 x2 + A2 x + A1
|
How to add one polynomial to another listr using NumPy in Python?
# importing package
import numpy
# define the polynomials
# p(x) = (5/3)x
px = (0, 5 / 3, 0)
# q(x) = (-7/4)(x**2) + (9/5)
qx = (-7 / 4, 0, 9 / 5)
# add the polynomials
rx = numpy.polynomial.polynomial.polyadd(px, qx)
# print the resultant polynomial
print(rx)
#Output : If p(x) = A3 x2 + A2 x + A1
[END]
|
How to subtract one polynomial to another listr using NumPy in Python?
|
https://www.geeksforgeeks.org/how-to-subtract-one-polynomial-to-another-using-numpy-in-python/
|
# importing package
import numpy
# define the polynomials
# p(x) = 5(x**2) + (-2)x +5
px = (5, -2, 5)
# q(x) = 2(x**2) + (-5)x +2
qx = (2, -5, 2)
# subtract the polynomials
rx = numpy.polynomial.polynomial.polysub(px, qx)
# print the resultant polynomial
print(rx)
|
#Output : If p(x) = A3 x2 + A2 x + A1
|
How to subtract one polynomial to another listr using NumPy in Python?
# importing package
import numpy
# define the polynomials
# p(x) = 5(x**2) + (-2)x +5
px = (5, -2, 5)
# q(x) = 2(x**2) + (-5)x +2
qx = (2, -5, 2)
# subtract the polynomials
rx = numpy.polynomial.polynomial.polysub(px, qx)
# print the resultant polynomial
print(rx)
#Output : If p(x) = A3 x2 + A2 x + A1
[END]
|
How to subtract one polynomial to another listr using NumPy in Python?
|
https://www.geeksforgeeks.org/how-to-subtract-one-polynomial-to-another-using-numpy-in-python/
|
# importing package
import numpy
# define the polynomials
# p(x) = 2.2
px = (0, 0, 2.2)
# q(x) = 9.8(x**2) + 4
qx = (9.8, 0, 4)
# subtract the polynomials
rx = numpy.polynomial.polynomial.polysub(px, qx)
# print the resultant polynomial
print(rx)
|
#Output : If p(x) = A3 x2 + A2 x + A1
|
How to subtract one polynomial to another listr using NumPy in Python?
# importing package
import numpy
# define the polynomials
# p(x) = 2.2
px = (0, 0, 2.2)
# q(x) = 9.8(x**2) + 4
qx = (9.8, 0, 4)
# subtract the polynomials
rx = numpy.polynomial.polynomial.polysub(px, qx)
# print the resultant polynomial
print(rx)
#Output : If p(x) = A3 x2 + A2 x + A1
[END]
|
How to subtract one polynomial to another listr using NumPy in Python?
|
https://www.geeksforgeeks.org/how-to-subtract-one-polynomial-to-another-using-numpy-in-python/
|
# importing package
import numpy
# define the polynomials
# p(x) = (5/3)x
px = (0, 5 / 3, 0)
# q(x) = (-7/4)(x**2) + (9/5)
qx = (-7 / 4, 0, 9 / 5)
# subtract the polynomials
rx = numpy.polynomial.polynomial.polysub(px, qx)
# print the resultant polynomial
print(rx)
|
#Output : If p(x) = A3 x2 + A2 x + A1
|
How to subtract one polynomial to another listr using NumPy in Python?
# importing package
import numpy
# define the polynomials
# p(x) = (5/3)x
px = (0, 5 / 3, 0)
# q(x) = (-7/4)(x**2) + (9/5)
qx = (-7 / 4, 0, 9 / 5)
# subtract the polynomials
rx = numpy.polynomial.polynomial.polysub(px, qx)
# print the resultant polynomial
print(rx)
#Output : If p(x) = A3 x2 + A2 x + A1
[END]
|
How to multiply a polynomial to another listr using NumPy in Python?
|
https://www.geeksforgeeks.org/how-to-multiply-a-polynomial-to-another-using-numpy-in-python/
|
# importing package
import numpy
# define the polynomials
# p(x) = 5(x**2) + (-2)x +5
px = (5, -2, 5)
# q(x) = 2(x**2) + (-5)x +2
qx = (2, -5, 2)
# mul the polynomials
rx = numpy.polynomial.polynomial.polymul(px, qx)
# print the resultant polynomial
print(rx)
|
#Output : If p(x) = A3 x2 + A2 x + A1
|
How to multiply a polynomial to another listr using NumPy in Python?
# importing package
import numpy
# define the polynomials
# p(x) = 5(x**2) + (-2)x +5
px = (5, -2, 5)
# q(x) = 2(x**2) + (-5)x +2
qx = (2, -5, 2)
# mul the polynomials
rx = numpy.polynomial.polynomial.polymul(px, qx)
# print the resultant polynomial
print(rx)
#Output : If p(x) = A3 x2 + A2 x + A1
[END]
|
How to multiply a polynomial to another listr using NumPy in Python?
|
https://www.geeksforgeeks.org/how-to-multiply-a-polynomial-to-another-using-numpy-in-python/
|
# importing package
import numpy
# define the polynomials
# p(x) = 2.2
px = (0, 0, 2.2)
# q(x) = 9.8(x**2) + 4
qx = (9.8, 0, 4)
# mul the polynomials
rx = numpy.polynomial.polynomial.polymul(px, qx)
# print the resultant polynomial
print(rx)
|
#Output : If p(x) = A3 x2 + A2 x + A1
|
How to multiply a polynomial to another listr using NumPy in Python?
# importing package
import numpy
# define the polynomials
# p(x) = 2.2
px = (0, 0, 2.2)
# q(x) = 9.8(x**2) + 4
qx = (9.8, 0, 4)
# mul the polynomials
rx = numpy.polynomial.polynomial.polymul(px, qx)
# print the resultant polynomial
print(rx)
#Output : If p(x) = A3 x2 + A2 x + A1
[END]
|
How to multiply a polynomial to another listr using NumPy in Python?
|
https://www.geeksforgeeks.org/how-to-multiply-a-polynomial-to-another-using-numpy-in-python/
|
# importing package
import numpy
# define the polynomials
# p(x) = (5/3)x
px = (0, 5 / 3, 0)
# q(x) = (-7/4)(x**2) + (9/5)
qx = (-7 / 4, 0, 9 / 5)
# mul the polynomials
rx = numpy.polynomial.polynomial.polymul(px, qx)
# print the resultant polynomial
print(rx)
|
#Output : If p(x) = A3 x2 + A2 x + A1
|
How to multiply a polynomial to another listr using NumPy in Python?
# importing package
import numpy
# define the polynomials
# p(x) = (5/3)x
px = (0, 5 / 3, 0)
# q(x) = (-7/4)(x**2) + (9/5)
qx = (-7 / 4, 0, 9 / 5)
# mul the polynomials
rx = numpy.polynomial.polynomial.polymul(px, qx)
# print the resultant polynomial
print(rx)
#Output : If p(x) = A3 x2 + A2 x + A1
[END]
|
How to divide a polynomial to another listr using NumPy in Python?
|
https://www.geeksforgeeks.org/how-to-divide-a-polynomial-to-another-using-numpy-in-python/
|
# importing package
import numpy
# define the polynomials
# p(x) = 5(x**2) + (-2)x +5
px = (5, -2, 5)
# g(x) = x +2
gx = (2, 1, 0)
# divide the polynomials
qx, rx = numpy.polynomial.polynomial.polydiv(px, gx)
# print the result
# quotient
print(qx)
# remainder
print(rx)
|
#Output : If p(x) = A3 x2 + A2 x + A1
|
How to divide a polynomial to another listr using NumPy in Python?
# importing package
import numpy
# define the polynomials
# p(x) = 5(x**2) + (-2)x +5
px = (5, -2, 5)
# g(x) = x +2
gx = (2, 1, 0)
# divide the polynomials
qx, rx = numpy.polynomial.polynomial.polydiv(px, gx)
# print the result
# quotient
print(qx)
# remainder
print(rx)
#Output : If p(x) = A3 x2 + A2 x + A1
[END]
|
How to divide a polynomial to another listr using NumPy in Python?
|
https://www.geeksforgeeks.org/how-to-divide-a-polynomial-to-another-using-numpy-in-python/
|
# importing package
import numpy
# define the polynomials
# p(x) = (x**2) + 3x + 2
px = (1, 3, 2)
# g(x) = x + 1
gx = (1, 1, 0)
# divide the polynomials
qx, rx = numpy.polynomial.polynomial.polydiv(px, gx)
# print the result
# quotient
print(qx)
# remainder
print(rx)
|
#Output : If p(x) = A3 x2 + A2 x + A1
|
How to divide a polynomial to another listr using NumPy in Python?
# importing package
import numpy
# define the polynomials
# p(x) = (x**2) + 3x + 2
px = (1, 3, 2)
# g(x) = x + 1
gx = (1, 1, 0)
# divide the polynomials
qx, rx = numpy.polynomial.polynomial.polydiv(px, gx)
# print the result
# quotient
print(qx)
# remainder
print(rx)
#Output : If p(x) = A3 x2 + A2 x + A1
[END]
|
Find the roots of the polynomials using NumPy
|
https://www.geeksforgeeks.org/find-the-roots-of-the-polynomials-using-numpy/
|
# import numpy library
import numpy as np
# Enter the coefficients of the poly in the array
coeff = [1, 2, 1]
print(np.roots(coeff))
|
#Output : [-1. -1.]
|
Find the roots of the polynomials using NumPy
# import numpy library
import numpy as np
# Enter the coefficients of the poly in the array
coeff = [1, 2, 1]
print(np.roots(coeff))
#Output : [-1. -1.]
[END]
|
Find the roots of the polynomials using NumPy
|
https://www.geeksforgeeks.org/find-the-roots-of-the-polynomials-using-numpy/
|
# import numpy library
import numpy as np
# Enter the coefficients of the poly
# in the array
coeff = [1, 3, 2, 1]
print(np.roots(coeff))
|
#Output : [-1. -1.]
|
Find the roots of the polynomials using NumPy
# import numpy library
import numpy as np
# Enter the coefficients of the poly
# in the array
coeff = [1, 3, 2, 1]
print(np.roots(coeff))
#Output : [-1. -1.]
[END]
|
Find the roots of the polynomials using NumPy
|
https://www.geeksforgeeks.org/find-the-roots-of-the-polynomials-using-numpy/
|
# import numpy library
import numpy as np
# enter the coefficients of poly
# in the array
p = np.poly1d([1, 2, 1])
# multiplying by r(or roots) to
# get the roots
root_of_poly = p.r
print(root_of_poly)
|
#Output : [-1. -1.]
|
Find the roots of the polynomials using NumPy
# import numpy library
import numpy as np
# enter the coefficients of poly
# in the array
p = np.poly1d([1, 2, 1])
# multiplying by r(or roots) to
# get the roots
root_of_poly = p.r
print(root_of_poly)
#Output : [-1. -1.]
[END]
|
Find the roots of the polynomials using NumPy
|
https://www.geeksforgeeks.org/find-the-roots-of-the-polynomials-using-numpy/
|
# import numpy library
import numpy as np
# enter the coefficients of poly
# in the array
p = np.poly1d([1, 3, 2, 1])
# multiplying by r(or roots) to get
# the roots
root_of_poly = p.r
print(root_of_poly)
|
#Output : [-1. -1.]
|
Find the roots of the polynomials using NumPy
# import numpy library
import numpy as np
# enter the coefficients of poly
# in the array
p = np.poly1d([1, 3, 2, 1])
# multiplying by r(or roots) to get
# the roots
root_of_poly = p.r
print(root_of_poly)
#Output : [-1. -1.]
[END]
|
Evaluate a 2-D polynomial series on the Cartesian product
|
https://www.geeksforgeeks.org/python-numpy-np-polygrid2d-method/
|
# Python program explaining
# numpy.polygrid2d() method
# importing numpy as np
import numpy as np
from numpy.polynomial.polynomial import polygrid2d
# Input polynomial series coefficients
c = np.array([[1, 3, 5], [2, 4, 6]])
# using np.polygrid2d() method
ans = polygrid2d([7, 9], [8, 10], c)
print(ans)
|
#Output :
|
Evaluate a 2-D polynomial series on the Cartesian product
# Python program explaining
# numpy.polygrid2d() method
# importing numpy as np
import numpy as np
from numpy.polynomial.polynomial import polygrid2d
# Input polynomial series coefficients
c = np.array([[1, 3, 5], [2, 4, 6]])
# using np.polygrid2d() method
ans = polygrid2d([7, 9], [8, 10], c)
print(ans)
#Output :
[END]
|
Evaluate a 2-D polynomial series on the Cartesian product
|
https://www.geeksforgeeks.org/python-numpy-np-polygrid2d-method/
|
# Python program explaining
# numpy.polygrid2d() method
# importing numpy as np
import numpy as np
from numpy.polynomial.polynomial import polygrid2d
# Input polynomial series coefficients
c = np.array([[1, 3, 5], [2, 4, 6]])
# using np.polygrid2d() method
ans = polygrid2d(7, 8, c)
print(ans)
|
#Output :
|
Evaluate a 2-D polynomial series on the Cartesian product
# Python program explaining
# numpy.polygrid2d() method
# importing numpy as np
import numpy as np
from numpy.polynomial.polynomial import polygrid2d
# Input polynomial series coefficients
c = np.array([[1, 3, 5], [2, 4, 6]])
# using np.polygrid2d() method
ans = polygrid2d(7, 8, c)
print(ans)
#Output :
[END]
|
Evaluate a 3-D polynomial series on the Cartesian product
|
https://www.geeksforgeeks.org/python-numpy-np-polygrid3d-method/
|
# Python program explaining
# numpy.polygrid3d() method
# importing numpy as np
import numpy as np
from numpy.polynomial.polynomial import polygrid3d
# Input polynomial series coefficients
c = np.array([[1, 3, 5], [2, 4, 6], [10, 11, 12]])
# using np.polygrid3d() method
ans = polygrid3d([7, 9], [8, 10], [5, 6], c)
print(ans)
|
#Output :
|
Evaluate a 3-D polynomial series on the Cartesian product
# Python program explaining
# numpy.polygrid3d() method
# importing numpy as np
import numpy as np
from numpy.polynomial.polynomial import polygrid3d
# Input polynomial series coefficients
c = np.array([[1, 3, 5], [2, 4, 6], [10, 11, 12]])
# using np.polygrid3d() method
ans = polygrid3d([7, 9], [8, 10], [5, 6], c)
print(ans)
#Output :
[END]
|
Evaluate a 3-D polynomial series on the Cartesian product
|
https://www.geeksforgeeks.org/python-numpy-np-polygrid3d-method/
|
# Python program explaining
# numpy.polygrid3d() method
# importing numpy as np
import numpy as np
from numpy.polynomial.polynomial import polygrid3d
# Input polynomial series coefficients
c = np.array([[1, 3, 5], [2, 4, 6], [10, 11, 12]])
# using np.polygrid3d() method
ans = polygrid3d(7, 11, 12, c)
print(ans)
|
#Output :
|
Evaluate a 3-D polynomial series on the Cartesian product
# Python program explaining
# numpy.polygrid3d() method
# importing numpy as np
import numpy as np
from numpy.polynomial.polynomial import polygrid3d
# Input polynomial series coefficients
c = np.array([[1, 3, 5], [2, 4, 6], [10, 11, 12]])
# using np.polygrid3d() method
ans = polygrid3d(7, 11, 12, c)
print(ans)
#Output :
[END]
|
Repeat all the elements of a NumPy array of strings
|
https://www.geeksforgeeks.org/repeat-all-the-elements-of-a-numpy-array-of-strings/
|
# importing the module
import numpy as np
# created array of strings
arr = np.array(["Akash", "Rohit", "Ayush", "Dhruv", "Radhika"], dtype=np.str)
print("Original Array :")
print(arr)
# with the help of np.char.multiply()
# repeating the characters 3 times
new_array = np.char.multiply(arr, 3)
print("\nNew array :")
print(new_array)
|
#Output : Original Array :
|
Repeat all the elements of a NumPy array of strings
# importing the module
import numpy as np
# created array of strings
arr = np.array(["Akash", "Rohit", "Ayush", "Dhruv", "Radhika"], dtype=np.str)
print("Original Array :")
print(arr)
# with the help of np.char.multiply()
# repeating the characters 3 times
new_array = np.char.multiply(arr, 3)
print("\nNew array :")
print(new_array)
#Output : Original Array :
[END]
|
Repeat all the elements of a NumPy array of strings
|
https://www.geeksforgeeks.org/repeat-all-the-elements-of-a-numpy-array-of-strings/
|
# importing the module
import numpy as np
# created array of strings
arr = np.array(["Geeks", "for", "Geeks"])
print("Original Array :")
print(arr)
# with the help of np.char.multiply()
# repeating the characters 3 times
new_array = np.char.multiply(arr, 2)
print("\nNew array :")
print(new_array)
|
#Output : Original Array :
|
Repeat all the elements of a NumPy array of strings
# importing the module
import numpy as np
# created array of strings
arr = np.array(["Geeks", "for", "Geeks"])
print("Original Array :")
print(arr)
# with the help of np.char.multiply()
# repeating the characters 3 times
new_array = np.char.multiply(arr, 2)
print("\nNew array :")
print(new_array)
#Output : Original Array :
[END]
|
Repeat all the elements of a NumPy array of strings
|
https://www.geeksforgeeks.org/repeat-all-the-elements-of-a-numpy-array-of-strings/
|
# importing the module
import numpy as np
# created array of strings
arr = np.array(["Geeks", "for", "Geeks"])
print("Original Array :")
print(arr)
# using list comprehension to repeat each string in the array
n = 2 # number of times to repeat each string
new_array = np.array([s * n for s in arr])
print("\nNew array :")
print(new_array)
|
#Output : Original Array :
|
Repeat all the elements of a NumPy array of strings
# importing the module
import numpy as np
# created array of strings
arr = np.array(["Geeks", "for", "Geeks"])
print("Original Array :")
print(arr)
# using list comprehension to repeat each string in the array
n = 2 # number of times to repeat each string
new_array = np.array([s * n for s in arr])
print("\nNew array :")
print(new_array)
#Output : Original Array :
[END]
|
How to split the element of a given NumPy array with spaces?
|
https://www.geeksforgeeks.org/how-to-split-the-element-of-a-given-numpy-array-with-spaces/
|
import numpy as np
# Original Array
array = np.array(["PHP C# Python C Java C++"], dtype=np.str)
print(array)
# Split the element of the said array with spaces
sparr = np.char.split(array)
print(sparr)
|
#Output : ['PHP C# Python C Java C++']
|
How to split the element of a given NumPy array with spaces?
import numpy as np
# Original Array
array = np.array(["PHP C# Python C Java C++"], dtype=np.str)
print(array)
# Split the element of the said array with spaces
sparr = np.char.split(array)
print(sparr)
#Output : ['PHP C# Python C Java C++']
[END]
|
How to split the element of a given NumPy array with spaces?
|
https://www.geeksforgeeks.org/how-to-split-the-element-of-a-given-numpy-array-with-spaces/
|
import numpy as np
# Original Array
array = np.array(["Geeks For Geeks"], dtype=np.str)
print(array)
# Split the element of the said array
# with spaces
sparr = np.char.split(array)
print(sparr)
|
#Output : ['PHP C# Python C Java C++']
|
How to split the element of a given NumPy array with spaces?
import numpy as np
# Original Array
array = np.array(["Geeks For Geeks"], dtype=np.str)
print(array)
# Split the element of the said array
# with spaces
sparr = np.char.split(array)
print(sparr)
#Output : ['PHP C# Python C Java C++']
[END]
|
How to split the element of a given NumPy array with spaces?
|
https://www.geeksforgeeks.org/how-to-split-the-element-of-a-given-numpy-array-with-spaces/
|
import numpy as np
# Original Array
array = np.array(["DBMS OOPS DS"], dtype=np.str)
print(array)
# Split the element of the said array
# with spaces
sparr = np.char.split(array)
print(sparr)
|
#Output : ['PHP C# Python C Java C++']
|
How to split the element of a given NumPy array with spaces?
import numpy as np
# Original Array
array = np.array(["DBMS OOPS DS"], dtype=np.str)
print(array)
# Split the element of the said array
# with spaces
sparr = np.char.split(array)
print(sparr)
#Output : ['PHP C# Python C Java C++']
[END]
|
How to insert a space between characters of all the elements of a given NumPy array?
|
https://www.geeksforgeeks.org/how-to-insert-a-space-between-characters-of-all-the-elements-of-a-given-numpy-array/
|
# importing numpy as np
import numpy as np
# creating array of string
x = np.array(["geeks", "for", "geeks"], dtype=np.str)
print("Printing the Original Array:")
print(x)
# inserting space using np.char.join()
r = np.char.join(" ", x)
print(
"Printing the array after inserting space\
between the elements"
)
print(r)
|
#Output : Suppose we have an array of string as follows:
|
How to insert a space between characters of all the elements of a given NumPy array?
# importing numpy as np
import numpy as np
# creating array of string
x = np.array(["geeks", "for", "geeks"], dtype=np.str)
print("Printing the Original Array:")
print(x)
# inserting space using np.char.join()
r = np.char.join(" ", x)
print(
"Printing the array after inserting space\
between the elements"
)
print(r)
#Output : Suppose we have an array of string as follows:
[END]
|
Swap the case of an array of string
|
https://www.geeksforgeeks.org/numpy-string-operations-swapcase-function/
|
# Python Program explaining
# numpy.char.swapcase() function
import numpy as geek
in_arr = geek.array(["P4Q R", "4q Rp", "Q Rp4", "rp4q"])
print("input array : ", in_arr)
out_arr = geek.char.swapcase(in_arr)
print("output swapcased array :", out_arr)
|
input array : ['P4Q R' '4q Rp' 'Q Rp4' 'rp4q']
output swapcasecased array : ['p4q r' '4Q rP' 'q rP4' 'RP4Q']
|
Swap the case of an array of string
# Python Program explaining
# numpy.char.swapcase() function
import numpy as geek
in_arr = geek.array(["P4Q R", "4q Rp", "Q Rp4", "rp4q"])
print("input array : ", in_arr)
out_arr = geek.char.swapcase(in_arr)
print("output swapcased array :", out_arr)
input array : ['P4Q R' '4q Rp' 'Q Rp4' 'rp4q']
output swapcasecased array : ['p4q r' '4Q rP' 'q rP4' 'RP4Q']
[END]
|
Swap the case of an array of string
|
https://www.geeksforgeeks.org/numpy-string-operations-swapcase-function/
|
# Python Program explaining
# numpy.char.swapcase() function
import numpy as geek
in_arr = geek.array(["Geeks", "For", "Geeks"])
print("input array : ", in_arr)
out_arr = geek.char.swapcase(in_arr)
print("output swapcasecased array :", out_arr)
|
input array : ['P4Q R' '4q Rp' 'Q Rp4' 'rp4q']
output swapcasecased array : ['p4q r' '4Q rP' 'q rP4' 'RP4Q']
|
Swap the case of an array of string
# Python Program explaining
# numpy.char.swapcase() function
import numpy as geek
in_arr = geek.array(["Geeks", "For", "Geeks"])
print("input array : ", in_arr)
out_arr = geek.char.swapcase(in_arr)
print("output swapcasecased array :", out_arr)
input array : ['P4Q R' '4q Rp' 'Q Rp4' 'rp4q']
output swapcasecased array : ['p4q r' '4Q rP' 'q rP4' 'RP4Q']
[END]
|
Change the case to uppercase of elements of an array
|
https://www.geeksforgeeks.org/numpy-string-operations-upper-function/
|
# Python Program explaining
# numpy.char.upper() function
import numpy as geek
in_arr = geek.array(["p4q r", "4q rp", "q rp4", "rp4q"])
print("input array : ", in_arr)
out_arr = geek.char.upper(in_arr)
print("output uppercased array :", out_arr)
|
input array : ['p4q r' '4q rp' 'q rp4' 'rp4q']
|
Change the case to uppercase of elements of an array
# Python Program explaining
# numpy.char.upper() function
import numpy as geek
in_arr = geek.array(["p4q r", "4q rp", "q rp4", "rp4q"])
print("input array : ", in_arr)
out_arr = geek.char.upper(in_arr)
print("output uppercased array :", out_arr)
input array : ['p4q r' '4q rp' 'q rp4' 'rp4q']
[END]
|
Change the case to uppercase of elements of an array
|
https://www.geeksforgeeks.org/numpy-string-operations-upper-function/
|
# Python Program explaining
# numpy.char.upper() function
import numpy as geek
in_arr = geek.array(["geeks", "for", "geeks"])
print("input array : ", in_arr)
out_arr = geek.char.upper(in_arr)
print("output uppercased array :", out_arr)
|
input array : ['p4q r' '4q rp' 'q rp4' 'rp4q']
|
Change the case to uppercase of elements of an array
# Python Program explaining
# numpy.char.upper() function
import numpy as geek
in_arr = geek.array(["geeks", "for", "geeks"])
print("input array : ", in_arr)
out_arr = geek.char.upper(in_arr)
print("output uppercased array :", out_arr)
input array : ['p4q r' '4q rp' 'q rp4' 'rp4q']
[END]
|
Change the case to lowercase of elements of an array
|
https://www.geeksforgeeks.org/numpy-string-operations-lower-function/
|
# Python Program explaining
# numpy.char.lower() function
import numpy as geek
in_arr = geek.array(["P4Q R", "4Q RP", "Q RP4", "RP4Q"])
print("input array : ", in_arr)
out_arr = geek.char.lower(in_arr)
print("output lowercased array :", out_arr)
|
input array : ['P4Q R' '4Q RP' 'Q RP4' 'RP4Q']
|
Change the case to lowercase of elements of an array
# Python Program explaining
# numpy.char.lower() function
import numpy as geek
in_arr = geek.array(["P4Q R", "4Q RP", "Q RP4", "RP4Q"])
print("input array : ", in_arr)
out_arr = geek.char.lower(in_arr)
print("output lowercased array :", out_arr)
input array : ['P4Q R' '4Q RP' 'Q RP4' 'RP4Q']
[END]
|
Change the case to lowercase of elements of an array
|
https://www.geeksforgeeks.org/numpy-string-operations-lower-function/
|
# Python Program explaining
# numpy.char.lower() function
import numpy as geek
in_arr = geek.array(["GEEKS", "FOR", "GEEKS"])
print("input array : ", in_arr)
out_arr = geek.char.lower(in_arr)
print("output lowercased array :", out_arr)
|
input array : ['P4Q R' '4Q RP' 'Q RP4' 'RP4Q']
|
Change the case to lowercase of elements of an array
# Python Program explaining
# numpy.char.lower() function
import numpy as geek
in_arr = geek.array(["GEEKS", "FOR", "GEEKS"])
print("input array : ", in_arr)
out_arr = geek.char.lower(in_arr)
print("output lowercased array :", out_arr)
input array : ['P4Q R' '4Q RP' 'Q RP4' 'RP4Q']
[END]
|
Join String List by a separator
|
https://www.geeksforgeeks.org/numpy-string-operations-join-function/
|
# Python program explaining
# numpy.core.defchararray.join() method
# importing numpy
import numpy as geek
# input array
in_arr = geek.array(["Python", "Numpy", "Pandas"])
print("Input original array : ", in_arr)
# creating the separator
sep = geek.array(["-", "+", "*"])
out_arr = geek.core.defchararray.join(sep, in_arr)
print("Output joined array: ", out_arr)
|
Input original array : ['Python' 'Numpy' 'Pandas']
|
Join String List by a separator
# Python program explaining
# numpy.core.defchararray.join() method
# importing numpy
import numpy as geek
# input array
in_arr = geek.array(["Python", "Numpy", "Pandas"])
print("Input original array : ", in_arr)
# creating the separator
sep = geek.array(["-", "+", "*"])
out_arr = geek.core.defchararray.join(sep, in_arr)
print("Output joined array: ", out_arr)
Input original array : ['Python' 'Numpy' 'Pandas']
[END]
|
Check if two same shaped string arrayss one by one
|
https://www.geeksforgeeks.org/numpy-string-operations-not_equal-function/
|
# Python program explaining
# numpy.char.not_equal() method
# importing numpy
import numpy as geek
# input arrays
in_arr1 = geek.array("numpy")
print("1st Input array : ", in_arr1)
in_arr2 = geek.array("numpy")
print("2nd Input array : ", in_arr2)
# checking if they are not equal
out_arr = geek.char.not_equal(in_arr1, in_arr2)
print("Output array: ", out_arr)
|
#Output : 1st Input array : numpy
|
Check if two same shaped string arrayss one by one
# Python program explaining
# numpy.char.not_equal() method
# importing numpy
import numpy as geek
# input arrays
in_arr1 = geek.array("numpy")
print("1st Input array : ", in_arr1)
in_arr2 = geek.array("numpy")
print("2nd Input array : ", in_arr2)
# checking if they are not equal
out_arr = geek.char.not_equal(in_arr1, in_arr2)
print("Output array: ", out_arr)
#Output : 1st Input array : numpy
[END]
|
Check if two same shaped string arrayss one by one
|
https://www.geeksforgeeks.org/numpy-string-operations-not_equal-function/
|
# Python program explaining
# numpy.char.not_equal() method
# importing numpy
import numpy as geek
# input arrays
in_arr1 = geek.array(["Geeks", "for", "Geeks"])
print("1st Input array : ", in_arr1)
in_arr2 = geek.array(["Geek", "for", "Geek"])
print("2nd Input array : ", in_arr2)
# checking if they are not equal
out_arr = geek.char.not_equal(in_arr1, in_arr2)
print("Output array: ", out_arr)
|
#Output : 1st Input array : numpy
|
Check if two same shaped string arrayss one by one
# Python program explaining
# numpy.char.not_equal() method
# importing numpy
import numpy as geek
# input arrays
in_arr1 = geek.array(["Geeks", "for", "Geeks"])
print("1st Input array : ", in_arr1)
in_arr2 = geek.array(["Geek", "for", "Geek"])
print("2nd Input array : ", in_arr2)
# checking if they are not equal
out_arr = geek.char.not_equal(in_arr1, in_arr2)
print("Output array: ", out_arr)
#Output : 1st Input array : numpy
[END]
|
Check if two same shaped string arrayss one by one
|
https://www.geeksforgeeks.org/numpy-string-operations-not_equal-function/
|
# Python program explaining
# numpy.char.not_equal() method
# importing numpy
import numpy as geek
# input arrays
in_arr1 = geek.array(["10", "11", "12"])
print("1st Input array : ", in_arr1)
in_arr2 = geek.array(["10", "11", "121"])
print("2nd Input array : ", in_arr2)
# checking if they are not equal
out_arr = geek.char.not_equal(in_arr1, in_arr2)
print("Output array: ", out_arr)
|
#Output : 1st Input array : numpy
|
Check if two same shaped string arrayss one by one
# Python program explaining
# numpy.char.not_equal() method
# importing numpy
import numpy as geek
# input arrays
in_arr1 = geek.array(["10", "11", "12"])
print("1st Input array : ", in_arr1)
in_arr2 = geek.array(["10", "11", "121"])
print("2nd Input array : ", in_arr2)
# checking if they are not equal
out_arr = geek.char.not_equal(in_arr1, in_arr2)
print("Output array: ", out_arr)
#Output : 1st Input array : numpy
[END]
|
Count the number of substrings in an array
|
https://www.geeksforgeeks.org/numpy-string-operations-count-function/
|
# Python program explaining
# numpy.char.count() method
# importing numpy as geek
import numpy as geek
# input arrays
in_arr = geek.array(["Sayantan", " Sayan ", "Sayansubhra"])
print("Input array : ", in_arr)
# output arrays
out_arr = geek.char.count(in_arr, sub="an")
print("Output array: ", out_arr)
|
Input array : ['Sayantan' ' Sayan ' 'Sayansubhra']
|
Count the number of substrings in an array
# Python program explaining
# numpy.char.count() method
# importing numpy as geek
import numpy as geek
# input arrays
in_arr = geek.array(["Sayantan", " Sayan ", "Sayansubhra"])
print("Input array : ", in_arr)
# output arrays
out_arr = geek.char.count(in_arr, sub="an")
print("Output array: ", out_arr)
Input array : ['Sayantan' ' Sayan ' 'Sayansubhra']
[END]
|
Count the number of substrings in an array
|
https://www.geeksforgeeks.org/numpy-string-operations-count-function/
|
# Python program explaining
# numpy.char.count() method
# importing numpy as geek
import numpy as geek
# input arrays
in_arr = geek.array(["Sayantan", " Sayan ", "Sayansubhra"])
print("Input array : ", in_arr)
# output arrays
out_arr = geek.char.count(in_arr, sub="a", start=1, end=8)
print("Output array: ", out_arr)
|
Input array : ['Sayantan' ' Sayan ' 'Sayansubhra']
|
Count the number of substrings in an array
# Python program explaining
# numpy.char.count() method
# importing numpy as geek
import numpy as geek
# input arrays
in_arr = geek.array(["Sayantan", " Sayan ", "Sayansubhra"])
print("Input array : ", in_arr)
# output arrays
out_arr = geek.char.count(in_arr, sub="a", start=1, end=8)
print("Output array: ", out_arr)
Input array : ['Sayantan' ' Sayan ' 'Sayansubhra']
[END]
|
Find the lowest index of the substring in an array
|
https://www.geeksforgeeks.org/numpy-string-operations-find-function/
|
# Python program explaining
# numpy.char.find() method
# importing numpy as geek
import numpy as geek
# input arrays
in_arr = geek.array(["aAaAaA", "baA", "abBABba"])
print("Input array : ", in_arr)
# output arrays
out_arr = geek.char.find(in_arr, sub="A")
print("Output array: ", out_arr)
|
Input array : ['aAaAaA' 'baA' 'abBABba']
|
Find the lowest index of the substring in an array
# Python program explaining
# numpy.char.find() method
# importing numpy as geek
import numpy as geek
# input arrays
in_arr = geek.array(["aAaAaA", "baA", "abBABba"])
print("Input array : ", in_arr)
# output arrays
out_arr = geek.char.find(in_arr, sub="A")
print("Output array: ", out_arr)
Input array : ['aAaAaA' 'baA' 'abBABba']
[END]
|
Find the lowest index of the substring in an array
|
https://www.geeksforgeeks.org/numpy-string-operations-find-function/
|
# Python program explaining
# numpy.char.find() method
# importing numpy as geek
import numpy as geek
# input arrays
in_arr = geek.array(["aAaAaA", "aA", "abBABba"])
print("Input array : ", in_arr)
# output arrays
out_arr = geek.char.find(in_arr, sub="a", start=3, end=7)
print("Output array: ", out_arr)
|
Input array : ['aAaAaA' 'baA' 'abBABba']
|
Find the lowest index of the substring in an array
# Python program explaining
# numpy.char.find() method
# importing numpy as geek
import numpy as geek
# input arrays
in_arr = geek.array(["aAaAaA", "aA", "abBABba"])
print("Input array : ", in_arr)
# output arrays
out_arr = geek.char.find(in_arr, sub="a", start=3, end=7)
print("Output array: ", out_arr)
Input array : ['aAaAaA' 'baA' 'abBABba']
[END]
|
Different ways to convert a Python dictionary to a NumPy array
|
https://www.geeksforgeeks.org/different-ways-to-convert-a-python-dictionary-to-a-numpy-array/
|
# importing required librariess
import numpy as np
from ast import literal_eval
# creating class of string
name_list = """{
"column0": {"First_Name": "Akash",
"Second_Name": "kumar", "Interest": "Coding"},
"column1": {"First_Name": "Ayush",
"Second_Name": "Sharma", "Interest": "Cricket"},
"column2": {"First_Name": "Diksha",
"Second_Name": "Sharma","Interest": "Reading"},
"column3": {"First_Name":" Priyanka",
"Second_Name": "Kumari", "Interest": "Dancing"}
}"""
print("Type of name_list created:\n", type(name_list))
# converting string type to dictionary
t = literal_eval(name_list)
# printing the original dictionary
print("\nPrinting the original Name_list dictionary:\n", t)
print("Type of original dictionary:\n", type(t))
# converting dictionary to numpy array
result_nparra = np.array(
[[v[j] for j in ["First_Name", "Second_Name", "Interest"]] for k, v in t.items()]
)
print("\nConverted ndarray from the Original dictionary:\n", result_nparra)
# printing the type of converted array
print("Type:\n", type(result_nparra))
|
#Output : <class 'dict'>
|
Different ways to convert a Python dictionary to a NumPy array
# importing required librariess
import numpy as np
from ast import literal_eval
# creating class of string
name_list = """{
"column0": {"First_Name": "Akash",
"Second_Name": "kumar", "Interest": "Coding"},
"column1": {"First_Name": "Ayush",
"Second_Name": "Sharma", "Interest": "Cricket"},
"column2": {"First_Name": "Diksha",
"Second_Name": "Sharma","Interest": "Reading"},
"column3": {"First_Name":" Priyanka",
"Second_Name": "Kumari", "Interest": "Dancing"}
}"""
print("Type of name_list created:\n", type(name_list))
# converting string type to dictionary
t = literal_eval(name_list)
# printing the original dictionary
print("\nPrinting the original Name_list dictionary:\n", t)
print("Type of original dictionary:\n", type(t))
# converting dictionary to numpy array
result_nparra = np.array(
[[v[j] for j in ["First_Name", "Second_Name", "Interest"]] for k, v in t.items()]
)
print("\nConverted ndarray from the Original dictionary:\n", result_nparra)
# printing the type of converted array
print("Type:\n", type(result_nparra))
#Output : <class 'dict'>
[END]
|
Different ways to convert a Python dictionary to a NumPy array
|
https://www.geeksforgeeks.org/different-ways-to-convert-a-python-dictionary-to-a-numpy-array/
|
# importing library
import numpy as np
# creating dictionary as key as
# a number and value as its cube
dict_created = {0: 0, 1: 1, 2: 8, 3: 27, 4: 64, 5: 125, 6: 216}
# printing type of dictionary created
print(type(dict_created))
# converting dictionary to
# numpy array
res_array = np.array(list(dict_created.items()))
# printing the converted array
print(res_array)
# printing type of converted array
print(type(res_array))
|
#Output : <class 'dict'>
|
Different ways to convert a Python dictionary to a NumPy array
# importing library
import numpy as np
# creating dictionary as key as
# a number and value as its cube
dict_created = {0: 0, 1: 1, 2: 8, 3: 27, 4: 64, 5: 125, 6: 216}
# printing type of dictionary created
print(type(dict_created))
# converting dictionary to
# numpy array
res_array = np.array(list(dict_created.items()))
# printing the converted array
print(res_array)
# printing type of converted array
print(type(res_array))
#Output : <class 'dict'>
[END]
|
How to convert a list and tuple into NumPy arrays?
|
https://www.geeksforgeeks.org/how-to-convert-a-list-and-tuple-into-numpy-arrays/
|
import numpy as np
# list
list1 = [3, 4, 5, 6]
print(type(list1))
print(list1)
print()
# conversion
array1 = np.asarray(list1)
print(type(array1))
print(array1)
print()
# tuple
tuple1 = ([8, 4, 6], [1, 2, 3])
print(type(tuple1))
print(tuple1)
print()
# conversion
array2 = np.asarray(tuple1)
print(type(array2))
print(array2)
|
#Output : numpy.asarray( a, type = None, order = None )
|
How to convert a list and tuple into NumPy arrays?
import numpy as np
# list
list1 = [3, 4, 5, 6]
print(type(list1))
print(list1)
print()
# conversion
array1 = np.asarray(list1)
print(type(array1))
print(array1)
print()
# tuple
tuple1 = ([8, 4, 6], [1, 2, 3])
print(type(tuple1))
print(tuple1)
print()
# conversion
array2 = np.asarray(tuple1)
print(type(array2))
print(array2)
#Output : numpy.asarray( a, type = None, order = None )
[END]
|
How to convert a list and tuple into NumPy arrays?
|
https://www.geeksforgeeks.org/how-to-convert-a-list-and-tuple-into-numpy-arrays/
|
import numpy as np
# list
list1 = [1, 2, 3]
print(type(list1))
print(list1)
print()
# conversion
array1 = np.array(list1)
print(type(array1))
print(array1)
print()
# tuple
tuple1 = (1, 2, 3)
print(type(tuple1))
print(tuple1)
print()
# conversion
array2 = np.array(tuple1)
print(type(array2))
print(array2)
print()
# list, array and tuple
array3 = np.array([tuple1, list1, array2])
print(type(array3))
print(array3)
|
#Output : numpy.asarray( a, type = None, order = None )
|
How to convert a list and tuple into NumPy arrays?
import numpy as np
# list
list1 = [1, 2, 3]
print(type(list1))
print(list1)
print()
# conversion
array1 = np.array(list1)
print(type(array1))
print(array1)
print()
# tuple
tuple1 = (1, 2, 3)
print(type(tuple1))
print(tuple1)
print()
# conversion
array2 = np.array(tuple1)
print(type(array2))
print(array2)
print()
# list, array and tuple
array3 = np.array([tuple1, list1, array2])
print(type(array3))
print(array3)
#Output : numpy.asarray( a, type = None, order = None )
[END]
|
Ways to convert array of strings to array of floats
|
https://www.geeksforgeeks.org/python-ways-to-convert-array-of-strings-to-array-of-floats/
|
import numpy as np
# initialising array
ini_array = np.array(["1.1", "1.5", "2.7", "8.9"])
# printing initial array
print("initial array", str(ini_array))
# converting to array of floats
# using np.astype
res = ini_array.astype(np.float)
# printing final result
print("final array", str(res))
|
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
|
Ways to convert array of strings to array of floats
import numpy as np
# initialising array
ini_array = np.array(["1.1", "1.5", "2.7", "8.9"])
# printing initial array
print("initial array", str(ini_array))
# converting to array of floats
# using np.astype
res = ini_array.astype(np.float)
# printing final result
print("final array", str(res))
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
[END]
|
Ways to convert array of strings to array of floats
|
https://www.geeksforgeeks.org/python-ways-to-convert-array-of-strings-to-array-of-floats/
|
def convert_to_floats(arr):
# Use the map function to apply the float function to each element in the input list
result = map(float, arr)
# Return the resulting iterator as a list
return list(result)
# Test the function
arr = ["1.1", "1.5", "2.7", "8.9"]
print(convert_to_floats(arr))
# This code is contributed by Edula Vinay Kumar Reddy
|
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
|
Ways to convert array of strings to array of floats
def convert_to_floats(arr):
# Use the map function to apply the float function to each element in the input list
result = map(float, arr)
# Return the resulting iterator as a list
return list(result)
# Test the function
arr = ["1.1", "1.5", "2.7", "8.9"]
print(convert_to_floats(arr))
# This code is contributed by Edula Vinay Kumar Reddy
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
[END]
|
Ways to convert array of strings to array of floats
|
https://www.geeksforgeeks.org/python-ways-to-convert-array-of-strings-to-array-of-floats/
|
import numpy as np
# initialising array
ini_array = np.array(["1.1", "1.5", "2.7", "8.9"])
# printing initial array
print("initial array", str(ini_array))
# converting to array of floats
# using np.fromstring
ini_array = ", ".join(ini_array)
ini_array = np.fromstring(ini_array, dtype=np.float, sep=", ")
# printing final result
print("final array", str(ini_array))
|
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
|
Ways to convert array of strings to array of floats
import numpy as np
# initialising array
ini_array = np.array(["1.1", "1.5", "2.7", "8.9"])
# printing initial array
print("initial array", str(ini_array))
# converting to array of floats
# using np.fromstring
ini_array = ", ".join(ini_array)
ini_array = np.fromstring(ini_array, dtype=np.float, sep=", ")
# printing final result
print("final array", str(ini_array))
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
[END]
|
Ways to convert array of strings to array of floats
|
https://www.geeksforgeeks.org/python-ways-to-convert-array-of-strings-to-array-of-floats/
|
import numpy as np
# initialising array
ini_array = np.array(["1.1", "1.5", "2.7", "8.9"])
# printing initial array
print("initial array", str(ini_array))
# converting to array of floats
# using np.asarray
final_array = b = np.asarray(ini_array, dtype=np.float64, order="C")
# printing final result
print("final array", str(final_array))
|
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
|
Ways to convert array of strings to array of floats
import numpy as np
# initialising array
ini_array = np.array(["1.1", "1.5", "2.7", "8.9"])
# printing initial array
print("initial array", str(ini_array))
# converting to array of floats
# using np.asarray
final_array = b = np.asarray(ini_array, dtype=np.float64, order="C")
# printing final result
print("final array", str(final_array))
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
[END]
|
Ways to convert array of strings to array of floats
|
https://www.geeksforgeeks.org/python-ways-to-convert-array-of-strings-to-array-of-floats/
|
import numpy as np
# initialising array
ini_array = np.array(["1.1", "1.5", "2.7", "8.9"])
# printing initial array
print("initial array", str(ini_array))
# converting to array of floats
# using np.asarray
final_array = b = np.asfarray(ini_array, dtype=float)
# printing final result
print("final array", str(final_array))
|
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
|
Ways to convert array of strings to array of floats
import numpy as np
# initialising array
ini_array = np.array(["1.1", "1.5", "2.7", "8.9"])
# printing initial array
print("initial array", str(ini_array))
# converting to array of floats
# using np.asarray
final_array = b = np.asfarray(ini_array, dtype=float)
# printing final result
print("final array", str(final_array))
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
[END]
|
Ways to convert array of strings to array of floats
|
https://www.geeksforgeeks.org/python-ways-to-convert-array-of-strings-to-array-of-floats/
|
def convert_strings_to_floats(input_array):
output_array = []
for element in input_array:
converted_float = float(element)
output_array.append(converted_float)
return output_array
input_array = ["1.1", "1.5", "2.7", "8.9"]
output_array = convert_strings_to_floats(input_array)
print(output_array)
|
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
|
Ways to convert array of strings to array of floats
def convert_strings_to_floats(input_array):
output_array = []
for element in input_array:
converted_float = float(element)
output_array.append(converted_float)
return output_array
input_array = ["1.1", "1.5", "2.7", "8.9"]
output_array = convert_strings_to_floats(input_array)
print(output_array)
#Output : initial array: ['1.1' '1.5' '2.7' '8.9']
[END]
|
How to Convert an image to NumPy array and save it to CSV file using Python?
|
https://www.geeksforgeeks.org/how-to-convert-an-image-to-numpy-array-and-saveit-to-csv-file-using-python/
|
# import required libraries
from PIL import Image
import numpy as gfg
# read an image
img = Image.open("geeksforgeeks.jpg")
# convert image object into array
imageToMatrice = gfg.asarray(img)
# printing shape of image
print(imageToMatrice.shape)
|
#Output : (251, 335, 3)
|
How to Convert an image to NumPy array and save it to CSV file using Python?
# import required libraries
from PIL import Image
import numpy as gfg
# read an image
img = Image.open("geeksforgeeks.jpg")
# convert image object into array
imageToMatrice = gfg.asarray(img)
# printing shape of image
print(imageToMatrice.shape)
#Output : (251, 335, 3)
[END]
|
How to Convert an image to NumPy array and save it to CSV file using Python?
|
https://www.geeksforgeeks.org/how-to-convert-an-image-to-numpy-array-and-saveit-to-csv-file-using-python/
|
# import library
from matplotlib.image import imread
# read an image
imageToMatrice = imread("geeksforgeeks.jpg")
# show shape of the image
print(imageToMatrice.shape)
|
#Output : (251, 335, 3)
|
How to Convert an image to NumPy array and save it to CSV file using Python?
# import library
from matplotlib.image import imread
# read an image
imageToMatrice = imread("geeksforgeeks.jpg")
# show shape of the image
print(imageToMatrice.shape)
#Output : (251, 335, 3)
[END]
|
How to Convert an image to NumPy array and save it to CSV file using Python?
|
https://www.geeksforgeeks.org/how-to-convert-an-image-to-numpy-array-and-saveit-to-csv-file-using-python/
|
# import required libraries
import numpy as gfg
import matplotlib.image as img
# read an image
imageMat = img.imread("gfg.jpg")
print("Image shape:", imageMat.shape)
# if image is colored (RGB)
if imageMat.shape[2] == 3:
# reshape it from 3D matrice to 2D matrice
imageMat_reshape = imageMat.reshape(imageMat.shape[0], -1)
print("Reshaping to 2D array:", imageMat_reshape.shape)
# if image is grayscale
else:
# remain as it is
imageMat_reshape = imageMat
# saving matrice to .csv file
gfg.savetxt("geek.csv", imageMat_reshape)
# retrieving matrice from the .csv file
loaded_2D_mat = gfg.loadtxt("geek.csv")
# reshaping it to 3D matrice
loaded_mat = loaded_2D_mat.reshape(
loaded_2D_mat.shape[0],
loaded_2D_mat.shape[1] // imageMat.shape[2],
imageMat.shape[2],
)
print("Image shape of loaded Image:", loaded_mat.shape)
# check if both matrice have same shape or not
if (imageMat == loaded_mat).all():
print(
"\n\nYes", "The loaded matrice from CSV file is same as original image matrice"
)
|
#Output : (251, 335, 3)
|
How to Convert an image to NumPy array and save it to CSV file using Python?
# import required libraries
import numpy as gfg
import matplotlib.image as img
# read an image
imageMat = img.imread("gfg.jpg")
print("Image shape:", imageMat.shape)
# if image is colored (RGB)
if imageMat.shape[2] == 3:
# reshape it from 3D matrice to 2D matrice
imageMat_reshape = imageMat.reshape(imageMat.shape[0], -1)
print("Reshaping to 2D array:", imageMat_reshape.shape)
# if image is grayscale
else:
# remain as it is
imageMat_reshape = imageMat
# saving matrice to .csv file
gfg.savetxt("geek.csv", imageMat_reshape)
# retrieving matrice from the .csv file
loaded_2D_mat = gfg.loadtxt("geek.csv")
# reshaping it to 3D matrice
loaded_mat = loaded_2D_mat.reshape(
loaded_2D_mat.shape[0],
loaded_2D_mat.shape[1] // imageMat.shape[2],
imageMat.shape[2],
)
print("Image shape of loaded Image:", loaded_mat.shape)
# check if both matrice have same shape or not
if (imageMat == loaded_mat).all():
print(
"\n\nYes", "The loaded matrice from CSV file is same as original image matrice"
)
#Output : (251, 335, 3)
[END]
|
How to Convert an image to NumPy array and save it to CSV file using Python?
|
https://www.geeksforgeeks.org/how-to-convert-an-image-to-numpy-array-and-saveit-to-csv-file-using-python/
|
# import required libraries
import numpy as gfg
import matplotlib.image as img
import pandas as pd
# read an image
imageMat = img.imread("gfg.jpg")
print("Image shape:", imageMat.shape)
# if image is colored (RGB)
if imageMat.shape[2] == 3:
# reshape it from 3D matrice to 2D matrice
imageMat_reshape = imageMat.reshape(imageMat.shape[0], -1)
print("Reshaping to 2D array:", imageMat_reshape.shape)
# if image is grayscale
else:
# remain as it is
imageMat_reshape = imageMat
# converting it to dataframe.
mat_df = pd.DataFrame(imageMat_reshape)
# exporting dataframe to CSV file.
mat_df.to_csv("gfgfile.csv", header=None, index=None)
# retrieving dataframe from CSV file
loaded_df = pd.read_csv("gfgfile.csv", sep=",", header=None)
# getting matrice values.
loaded_2D_mat = loaded_df.values
# reshaping it to 3D matrice
loaded_mat = loaded_2D_mat.reshape(
loaded_2D_mat.shape[0],
loaded_2D_mat.shape[1] // imageMat.shape[2],
imageMat.shape[2],
)
print("Image shape of loaded Image :", loaded_mat.shape)
# check if both matrice have same shape or not
if (imageMat == loaded_mat).all():
print(
"\n\nYes", "The loaded matrice from CSV file is same as original image matrice"
)
|
#Output : (251, 335, 3)
|
How to Convert an image to NumPy array and save it to CSV file using Python?
# import required libraries
import numpy as gfg
import matplotlib.image as img
import pandas as pd
# read an image
imageMat = img.imread("gfg.jpg")
print("Image shape:", imageMat.shape)
# if image is colored (RGB)
if imageMat.shape[2] == 3:
# reshape it from 3D matrice to 2D matrice
imageMat_reshape = imageMat.reshape(imageMat.shape[0], -1)
print("Reshaping to 2D array:", imageMat_reshape.shape)
# if image is grayscale
else:
# remain as it is
imageMat_reshape = imageMat
# converting it to dataframe.
mat_df = pd.DataFrame(imageMat_reshape)
# exporting dataframe to CSV file.
mat_df.to_csv("gfgfile.csv", header=None, index=None)
# retrieving dataframe from CSV file
loaded_df = pd.read_csv("gfgfile.csv", sep=",", header=None)
# getting matrice values.
loaded_2D_mat = loaded_df.values
# reshaping it to 3D matrice
loaded_mat = loaded_2D_mat.reshape(
loaded_2D_mat.shape[0],
loaded_2D_mat.shape[1] // imageMat.shape[2],
imageMat.shape[2],
)
print("Image shape of loaded Image :", loaded_mat.shape)
# check if both matrice have same shape or not
if (imageMat == loaded_mat).all():
print(
"\n\nYes", "The loaded matrice from CSV file is same as original image matrice"
)
#Output : (251, 335, 3)
[END]
|
How to save a NumPy array to a text file?
|
https://www.geeksforgeeks.org/how-to-save-a-numpy-array-to-a-text-file/
|
# Program to save a NumPy array to a text file
# Importing required libraries
import numpy
# Creating an array
List = [1, 2, 3, 4, 5]
Array = numpy.array(List)
# Displaying the array
print("Array:\n", Array)
file = open("file1.txt", "w+")
# Saving the array in a text file
content = str(Array)
file.write(content)
file.close()
# Displaying the contents of the text file
file = open("file1.txt", "r")
content = file.read()
print("\nContent in file1.txt:\n", content)
file.close()
|
#Output : Array:
|
How to save a NumPy array to a text file?
# Program to save a NumPy array to a text file
# Importing required libraries
import numpy
# Creating an array
List = [1, 2, 3, 4, 5]
Array = numpy.array(List)
# Displaying the array
print("Array:\n", Array)
file = open("file1.txt", "w+")
# Saving the array in a text file
content = str(Array)
file.write(content)
file.close()
# Displaying the contents of the text file
file = open("file1.txt", "r")
content = file.read()
print("\nContent in file1.txt:\n", content)
file.close()
#Output : Array:
[END]
|
How to save a NumPy array to a text file?
|
https://www.geeksforgeeks.org/how-to-save-a-numpy-array-to-a-text-file/
|
# Program to save a NumPy array to a text file
# Importing required libraries
import numpy
# Creating 2D array
List = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Array = numpy.array(List)
# Displaying the array
print("Array:\n", Array)
file = open("file2.txt", "w+")
# Saving the 2D array in a text file
content = str(Array)
file.write(content)
file.close()
# Displaying the contents of the text file
file = open("file2.txt", "r")
content = file.read()
print("\nContent in file2.txt:\n", content)
file.close()
|
#Output : Array:
|
How to save a NumPy array to a text file?
# Program to save a NumPy array to a text file
# Importing required libraries
import numpy
# Creating 2D array
List = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Array = numpy.array(List)
# Displaying the array
print("Array:\n", Array)
file = open("file2.txt", "w+")
# Saving the 2D array in a text file
content = str(Array)
file.write(content)
file.close()
# Displaying the contents of the text file
file = open("file2.txt", "r")
content = file.read()
print("\nContent in file2.txt:\n", content)
file.close()
#Output : Array:
[END]
|
How to save a NumPy array to a text file?
|
https://www.geeksforgeeks.org/how-to-save-a-numpy-array-to-a-text-file/
|
# Program to save a NumPy array to a text file
# Importing required libraries
import numpy
# Creating an array
List = [1, 2, 3, 4, 5]
Array = numpy.array(List)
# Displaying the array
print("Array:\n", Array)
# Saving the array in a text file
numpy.savetxt("file1.txt", Array)
# Displaying the contents of the text file
content = numpy.loadtxt("file1.txt")
print("\nContent in file1.txt:\n", content)
|
#Output : Array:
|
How to save a NumPy array to a text file?
# Program to save a NumPy array to a text file
# Importing required libraries
import numpy
# Creating an array
List = [1, 2, 3, 4, 5]
Array = numpy.array(List)
# Displaying the array
print("Array:\n", Array)
# Saving the array in a text file
numpy.savetxt("file1.txt", Array)
# Displaying the contents of the text file
content = numpy.loadtxt("file1.txt")
print("\nContent in file1.txt:\n", content)
#Output : Array:
[END]
|
How to save a NumPy array to a text file?
|
https://www.geeksforgeeks.org/how-to-save-a-numpy-array-to-a-text-file/
|
# Program to save a NumPy array to a text file
# Importing required libraries
import numpy
# Creating 2D array
List = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Array = numpy.array(List)
# Displaying the array
print("Array:\n", Array)
# Saving the 2D array in a text file
numpy.savetxt("file2.txt", Array)
# Displaying the contents of the text file
content = numpy.loadtxt("file2.txt")
print("\nContent in file2.txt:\n", content)
|
#Output : Array:
|
How to save a NumPy array to a text file?
# Program to save a NumPy array to a text file
# Importing required libraries
import numpy
# Creating 2D array
List = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Array = numpy.array(List)
# Displaying the array
print("Array:\n", Array)
# Saving the 2D array in a text file
numpy.savetxt("file2.txt", Array)
# Displaying the contents of the text file
content = numpy.loadtxt("file2.txt")
print("\nContent in file2.txt:\n", content)
#Output : Array:
[END]
|
Load data from a text file
|
https://www.geeksforgeeks.org/numpy-loadtxt-in-python/
|
# Python program explaining
# loadtxt() function
import numpy as geek
# StringIO behaves like a file object
from io import StringIO
c = StringIO("0 1 2 \n3 4 5")
d = geek.loadtxt(c)
print(d)
|
#Output : [[ 0. 1. 2.]
|
Load data from a text file
# Python program explaining
# loadtxt() function
import numpy as geek
# StringIO behaves like a file object
from io import StringIO
c = StringIO("0 1 2 \n3 4 5")
d = geek.loadtxt(c)
print(d)
#Output : [[ 0. 1. 2.]
[END]
|
Load data from a text file
|
https://www.geeksforgeeks.org/numpy-loadtxt-in-python/
|
# Python program explaining
# loadtxt() function
import numpy as geek
# StringIO behaves like a file object
from io import StringIO
c = StringIO("1, 2, 3\n4, 5, 6")
x, y, z = geek.loadtxt(c, delimiter=", ", usecols=(0, 1, 2), unpack=True)
print("x is: ", x)
print("y is: ", y)
print("z is: ", z)
|
#Output : [[ 0. 1. 2.]
|
Load data from a text file
# Python program explaining
# loadtxt() function
import numpy as geek
# StringIO behaves like a file object
from io import StringIO
c = StringIO("1, 2, 3\n4, 5, 6")
x, y, z = geek.loadtxt(c, delimiter=", ", usecols=(0, 1, 2), unpack=True)
print("x is: ", x)
print("y is: ", y)
print("z is: ", z)
#Output : [[ 0. 1. 2.]
[END]
|
Load data from a text file
|
https://www.geeksforgeeks.org/numpy-loadtxt-in-python/
|
# Python program explaining
# loadtxt() function
import numpy as geek
# StringIO behaves like a file object
from io import StringIO
d = StringIO("M 21 72\nF 35 58")
e = geek.loadtxt(
d, dtype={"names": ("gender", "age", "weight"), "formats": ("S1", "i4", "f4")}
)
print(e)
|
#Output : [[ 0. 1. 2.]
|
Load data from a text file
# Python program explaining
# loadtxt() function
import numpy as geek
# StringIO behaves like a file object
from io import StringIO
d = StringIO("M 21 72\nF 35 58")
e = geek.loadtxt(
d, dtype={"names": ("gender", "age", "weight"), "formats": ("S1", "i4", "f4")}
)
print(e)
#Output : [[ 0. 1. 2.]
[END]
|
Make a Pandas DataFrame with two-dimensional list | Python
|
https://www.geeksforgeeks.org/make-a-pandas-dataframe-with-two-dimensional-list-python/
|
# import pandas as pd
import pandas as pd
# List1
lst = [["Geek", 25], ["is", 30], ["for", 26], ["Geeksforgeeks", 22]]
# creating df object with columns specified
df = pd.DataFrame(lst, columns=["Tag", "number"])
print(df)
|
#Output : Tag number
|
Make a Pandas DataFrame with two-dimensional list | Python
# import pandas as pd
import pandas as pd
# List1
lst = [["Geek", 25], ["is", 30], ["for", 26], ["Geeksforgeeks", 22]]
# creating df object with columns specified
df = pd.DataFrame(lst, columns=["Tag", "number"])
print(df)
#Output : Tag number
[END]
|
Make a Pandas DataFrame with two-dimensional list | Python
|
https://www.geeksforgeeks.org/make-a-pandas-dataframe-with-two-dimensional-list-python/
|
import pandas as pd
# List1
lst = [
["tom", "reacher", 25],
["krish", "pete", 30],
["nick", "wilson", 26],
["juli", "williams", 22],
]
df = pd.DataFrame(lst, columns=["FName", "LName", "Age"], dtype=float)
print(df)
|
#Output : Tag number
|
Make a Pandas DataFrame with two-dimensional list | Python
import pandas as pd
# List1
lst = [
["tom", "reacher", 25],
["krish", "pete", 30],
["nick", "wilson", 26],
["juli", "williams", 22],
]
df = pd.DataFrame(lst, columns=["FName", "LName", "Age"], dtype=float)
print(df)
#Output : Tag number
[END]
|
Python | Creating DataFrame from dict of narray/lists
|
https://www.geeksforgeeks.org/python-creating-dataframe-from-dict-of-narray-lists/
|
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {"Category": ["Array", "Stack", "Queue"], "Marks": [20, 21, 19]}
# Create DataFrame
df = pd.DataFrame(data)
# Print the output.
print(df)
|
#Output : Category Marks
|
Python | Creating DataFrame from dict of narray/lists
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {"Category": ["Array", "Stack", "Queue"], "Marks": [20, 21, 19]}
# Create DataFrame
df = pd.DataFrame(data)
# Print the output.
print(df)
#Output : Category Marks
[END]
|
Python | Creating DataFrame from dict of narray/lists
|
https://www.geeksforgeeks.org/python-creating-dataframe-from-dict-of-narray-lists/
|
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {
"Category": ["Array", "Stack", "Queue"],
"Student_1": [20, 21, 19],
"Student_2": [15, 20, 14],
}
# Create DataFrame
df = pd.DataFrame(data)
# Print the output.
print(df.transpose())
|
#Output : Category Marks
|
Python | Creating DataFrame from dict of narray/lists
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {
"Category": ["Array", "Stack", "Queue"],
"Student_1": [20, 21, 19],
"Student_2": [15, 20, 14],
}
# Create DataFrame
df = pd.DataFrame(data)
# Print the output.
print(df.transpose())
#Output : Category Marks
[END]
|
Python | Creating DataFrame from dict of narray/lists
|
https://www.geeksforgeeks.org/python-creating-dataframe-from-dict-of-narray-lists/
|
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {
"Area": ["Array", "Stack", "Queue"],
"Student_1": [20, 21, 19],
"Student_2": [15, 20, 14],
}
# Create DataFrame
df = pd.DataFrame(data, index=["Cat_1", "Cat_2", "Cat_3"])
# Print the output.
print(df)
|
#Output : Category Marks
|
Python | Creating DataFrame from dict of narray/lists
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {
"Area": ["Array", "Stack", "Queue"],
"Student_1": [20, 21, 19],
"Student_2": [15, 20, 14],
}
# Create DataFrame
df = pd.DataFrame(data, index=["Cat_1", "Cat_2", "Cat_3"])
# Print the output.
print(df)
#Output : Category Marks
[END]
|
Python | Creating DataFrame from dict of narray/lists
|
https://www.geeksforgeeks.org/python-creating-dataframe-from-dict-of-narray-lists/
|
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {"Category": ["Array", "Stack", "Queue"], "Marks": [20, 21, 19]}
# Create DataFrame
df = pd.DataFrame(data)
# Print the output.
print(df)
|
#Output : Category Marks
|
Python | Creating DataFrame from dict of narray/lists
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {"Category": ["Array", "Stack", "Queue"], "Marks": [20, 21, 19]}
# Create DataFrame
df = pd.DataFrame(data)
# Print the output.
print(df)
#Output : Category Marks
[END]
|
Python | Creating DataFrame from dict of narray/lists
|
https://www.geeksforgeeks.org/python-creating-dataframe-from-dict-of-narray-lists/
|
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {
"Category": ["Array", "Stack", "Queue"],
"Student_1": [20, 21, 19],
"Student_2": [15, 20, 14],
}
# Create DataFrame
df = pd.DataFrame(data)
# Print the output.
print(df.transpose())
|
#Output : Category Marks
|
Python | Creating DataFrame from dict of narray/lists
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {
"Category": ["Array", "Stack", "Queue"],
"Student_1": [20, 21, 19],
"Student_2": [15, 20, 14],
}
# Create DataFrame
df = pd.DataFrame(data)
# Print the output.
print(df.transpose())
#Output : Category Marks
[END]
|
Python | Creating DataFrame from dict of narray/lists
|
https://www.geeksforgeeks.org/python-creating-dataframe-from-dict-of-narray-lists/
|
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {
"Area": ["Array", "Stack", "Queue"],
"Student_1": [20, 21, 19],
"Student_2": [15, 20, 14],
}
# Create DataFrame
df = pd.DataFrame(data, index=["Cat_1", "Cat_2", "Cat_3"])
# Print the output.
print(df)
|
#Output : Category Marks
|
Python | Creating DataFrame from dict of narray/lists
# Python code demonstrate creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialise data of lists.
data = {
"Area": ["Array", "Stack", "Queue"],
"Student_1": [20, 21, 19],
"Student_2": [15, 20, 14],
}
# Create DataFrame
df = pd.DataFrame(data, index=["Cat_1", "Cat_2", "Cat_3"])
# Print the output.
print(df)
#Output : Category Marks
[END]
|
Creating Pandas dataframe using list of lists
|
https://www.geeksforgeeks.org/creating-pandas-dataframe-using-list-of-lists/
|
# Import pandas library
import pandas as pd
# initialize list of lists
data = [["Geeks", 10], ["for", 15], ["geeks", 20]]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns=["Name", "Age"])
# print dataframe.
print(df)
|
#Output : Name Age
|
Creating Pandas dataframe using list of lists
# Import pandas library
import pandas as pd
# initialize list of lists
data = [["Geeks", 10], ["for", 15], ["geeks", 20]]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns=["Name", "Age"])
# print dataframe.
print(df)
#Output : Name Age
[END]
|
Creating Pandas dataframe using list of lists
|
https://www.geeksforgeeks.org/creating-pandas-dataframe-using-list-of-lists/
|
# Import pandas library
import pandas as pd
# initialize list of lists
data = [
["DS", "Linked_list", 10],
["DS", "Stack", 9],
["DS", "Queue", 7],
["Algo", "Greedy", 8],
["Algo", "DP", 6],
["Algo", "BackTrack", 5],
]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns=["Category", "Name", "Marks"])
# print dataframe.
print(df)
|
#Output : Name Age
|
Creating Pandas dataframe using list of lists
# Import pandas library
import pandas as pd
# initialize list of lists
data = [
["DS", "Linked_list", 10],
["DS", "Stack", 9],
["DS", "Queue", 7],
["Algo", "Greedy", 8],
["Algo", "DP", 6],
["Algo", "BackTrack", 5],
]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns=["Category", "Name", "Marks"])
# print dataframe.
print(df)
#Output : Name Age
[END]
|
Creating Pandas dataframe using list of lists
|
https://www.geeksforgeeks.org/creating-pandas-dataframe-using-list-of-lists/
|
# Import pandas library
import pandas as pd
# initialize list of lists
data = [[1, 5, 10], [2, 6, 9], [3, 7, 8]]
# Create the pandas DataFrame
df = pd.DataFrame(data)
# specifying column names
df.columns = ["Col_1", "Col_2", "Col_3"]
# print dataframe.
print(df, "\n")
# transpose of dataframe
df = df.transpose()
print("Transpose of above dataframe is-\n", df)
|
#Output : Name Age
|
Creating Pandas dataframe using list of lists
# Import pandas library
import pandas as pd
# initialize list of lists
data = [[1, 5, 10], [2, 6, 9], [3, 7, 8]]
# Create the pandas DataFrame
df = pd.DataFrame(data)
# specifying column names
df.columns = ["Col_1", "Col_2", "Col_3"]
# print dataframe.
print(df, "\n")
# transpose of dataframe
df = df.transpose()
print("Transpose of above dataframe is-\n", df)
#Output : Name Age
[END]
|
Create a Pandas DataFrame from List of Dicts
|
https://www.geeksforgeeks.org/create-a-pandas-dataframe-from-list-of-dicts/
|
import pandas as pd
# Initialise data to lists.
data = [
{"Geeks": "dataframe", "For": "using", "geeks": "list"},
{"Geeks": 10, "For": 20, "geeks": 30},
]
df = pd.DataFrame.from_records(data, index=["1", "2"])
print(df)
|
#Output : Geeks For geeks
|
Create a Pandas DataFrame from List of Dicts
import pandas as pd
# Initialise data to lists.
data = [
{"Geeks": "dataframe", "For": "using", "geeks": "list"},
{"Geeks": 10, "For": 20, "geeks": 30},
]
df = pd.DataFrame.from_records(data, index=["1", "2"])
print(df)
#Output : Geeks For geeks
[END]
|
Create a Pandas DataFrame from List of Dicts
|
https://www.geeksforgeeks.org/create-a-pandas-dataframe-from-list-of-dicts/
|
import pandas as pd
# Initialise data to lists.
data = [
{"Geeks": "dataframe", "For": "using", "geeks": "list"},
{"Geeks": 10, "For": 20, "geeks": 30},
]
df = pd.DataFrame.from_dict(data)
print(df)
|
#Output : Geeks For geeks
|
Create a Pandas DataFrame from List of Dicts
import pandas as pd
# Initialise data to lists.
data = [
{"Geeks": "dataframe", "For": "using", "geeks": "list"},
{"Geeks": 10, "For": 20, "geeks": 30},
]
df = pd.DataFrame.from_dict(data)
print(df)
#Output : Geeks For geeks
[END]
|
Create a Pandas DataFrame from List of Dicts
|
https://www.geeksforgeeks.org/create-a-pandas-dataframe-from-list-of-dicts/
|
import pandas as pd
# Initialise data to lists.
data = [
{"Geeks": "dataframe", "For": "using", "geeks": "list"},
{"Geeks": 10, "For": 20, "geeks": 30},
]
df = pd.json_normalize(data)
print(df)
|
#Output : Geeks For geeks
|
Create a Pandas DataFrame from List of Dicts
import pandas as pd
# Initialise data to lists.
data = [
{"Geeks": "dataframe", "For": "using", "geeks": "list"},
{"Geeks": 10, "For": 20, "geeks": 30},
]
df = pd.json_normalize(data)
print(df)
#Output : Geeks For geeks
[END]
|
Create a Pandas DataFrame from List of Dicts
|
https://www.geeksforgeeks.org/create-a-pandas-dataframe-from-list-of-dicts/
|
# Python code demonstrate how to create
# Pandas DataFrame by lists of dicts without matching key-value pair
import pandas as pd
# Initialise data to lists.
data = [
{"Geeks": "dataframe", "For": "using", "geeks": "list", "Portal": 10000},
{"Geeks": 10, "For": 20, "geeks": 30},
]
# Creates DataFrame.
df = pd.DataFrame(data)
# Print the data
df
|
#Output : Geeks For geeks
|
Create a Pandas DataFrame from List of Dicts
# Python code demonstrate how to create
# Pandas DataFrame by lists of dicts without matching key-value pair
import pandas as pd
# Initialise data to lists.
data = [
{"Geeks": "dataframe", "For": "using", "geeks": "list", "Portal": 10000},
{"Geeks": 10, "For": 20, "geeks": 30},
]
# Creates DataFrame.
df = pd.DataFrame(data)
# Print the data
df
#Output : Geeks For geeks
[END]
|
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