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How to apply slicing to a pandas DataFrame? [duplicate]

This question already has answers here:
How to slice a pandas DataFrame by position?
(5 answers)
Closed 24 days ago.
I am trying to replace the following code:
DfInt['Closest Service'] = DfInt[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,
78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,
142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,
157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171,
172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186,
187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201,
202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216,
217, 218, 219, 220, 221, 222, 223]
].idxmin(axis=1)
by something like
DfInt['Closest Service'] = DfInt[[0:224]].idxmin(axis=1)
But this is not working... Anyone an idea?

If need select by labels use DataFrame.loc, : is for select all rows:
DfInt['Closest Service'] = DfInt.loc[:, :223].idxmin(axis=1)
If select by positions - first 223 columns use DataFrame.iloc by 224:
DfInt['Closest Service'] = DfInt.iloc[:, :224].idxmin(axis=1)

how to convert list to RGB value in python

I'm trying to convert fibonacci series to rgb image. so
import matplotlib.pyplot as plt
import numpy as np
N = int(input("Number of elements in Fibonacci Series, N, (N>=2) : "))
#starting elements: 0, 1
fibonacciSeries = [0,1]
if N>2:
for i in range(2, N):
nextElement = fibonacciSeries[i-1] + fibonacciSeries[i-2]
fibonacciSeries.append(nextElement)
print(fibonacciSeries)
fib_arr = np.array(fibonacciSeries)
fib_arr
img =np.zeros((100,100,4))
rgb = []
for i in fibonacciSeries:
rgb.append(i % 255)
print(rgb)
all this process ı have a list of mod each index of fib_Arr like that!
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 122, 100, 222, 67, 34, 101, 135, 236, 116, 97, 213, 55, 13, 68, 81, 149, 230, 124, 99, 223, 67, 35, 102, 137, 239, 121, 105, 226, 76, 47, 123, 170, 38, 208, 246, 199, 190, 134, 69, 203, 17, 220, 237, 202, 184, 131, 60, 191, 251, 187, 183, 115, 43, 158, 201, 104, 50, 154, 204, 103, 52, 155, 207, 107, 59, 166, 225, 136, 106, 242, 93, 80, 173, 253, 171, 169, 85, 254, 84, 83, 167, 250, 162, 157, 64, 221]
now how to convert this value to RGB image
I try to
plt.imshow(rgb)
plt.savefig("rgb.png")
but doesn't work
edited:
rgb_arr = np.array(rgb)
rgb_arr
array([ 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144,
233, 122, 100, 222, 67, 34, 101, 135, 236, 116, 97, 213, 55,
13, 68, 81, 149, 230, 124, 99, 223, 67, 35, 102, 137, 239,
121, 105, 226, 76, 47, 123, 170, 38, 208, 246, 199, 190, 134,
69, 203, 17, 220, 237, 202, 184, 131, 60, 191, 251, 187, 183,
115, 43, 158, 201, 104, 50, 154, 204, 103, 52, 155, 207, 107,
59, 166, 225, 136, 106, 242, 93, 80, 173, 253, 171, 169, 85,
254, 84, 83, 167, 250, 162, 157, 64])
from PIL import Image
img = Image.fromarray(rgb_arr, 'RGB')
img.save('test.png')
img.show()
picture

Hot to sketch a curve and convert it to numpy

I am in a real need of a tool that does the following:
You draw with your mouse a curve from a starting point to a finish point and then it exports this to an object, which can then be interpolated as a numpy array to a given number of points.
Is anybody aware of such tool, or a way to achieve something similar?
Thanks

This might get you started. Draw your curve with Photoshop, GIMP or your favourite painting/drawing program and save it as a white line on a black background in PNG/GIF/JPEG/TIFF format, or any other format that PIL/Pillow understands:
Load the image with PIL/Pillow, blur it a little to allow for discontinuities in your hand-drawn-curve, then find the brightest pixel in each column. Save as a ".npy" file that you can load and interpolate to any dimension of array you want:
#!/usr/bin/env python3
import sys
import numpy as np
from PIL import Image, ImageFilter
# Check an image file was supplied
if len(sys.argv) != 2:
print("Usage: curve2array IMAGE", file=sys.stderr)
exit(1)
# Assign filename and open image as greyscale
filename = sys.argv[1]
im = Image.open(filename).convert('L')
# OPTIONAL = Blur the image a little to allow discontinuities in hand-sketched curves
im = im.filter(ImageFilter.GaussianBlur(1))
# Convert image to Numpy array and get row number of brightest pixel in each column
na = np.array(im)
yvals = np.argmax(im, axis=0)
yvals = na.shape[0] - yvals # Make origin at bottom-left instead of top-left
# Write result as ".npy" file
np.save('result.npy', yvals)
My input image is 600px wide by 400 pixels tall, so there are 600 y-values and the last is the largest one at 394 because it's near the top of a 400 px high image. The array looks like this:
array([ 1, 1, 3, 6, 10, 13, 16, 20, 23, 26, 29, 32, 36,
39, 42, 45, 48, 51, 54, 57, 59, 62, 65, 68, 71, 74,
76, 79, 82, 84, 87, 90, 92, 95, 97, 100, 102, 104, 107,
109, 111, 114, 116, 118, 120, 123, 125, 127, 129, 131, 133, 135,
137, 139, 140, 142, 144, 146, 148, 149, 151, 153, 154, 156, 157,
159, 160, 162, 163, 164, 166, 167, 168, 169, 171, 172, 173, 174,
175, 176, 177, 178, 179, 180, 180, 181, 182, 182, 183, 184, 184,
185, 185, 186, 186, 187, 187, 187, 188, 188, 188, 188, 188, 188,
188, 188, 188, 188, 188, 187, 187, 187, 186, 186, 185, 185, 184,
184, 183, 182, 181, 180, 180, 179, 178, 177, 176, 175, 173, 172,
171, 170, 169, 167, 166, 165, 163, 162, 160, 159, 157, 156, 155,
153, 152, 150, 149, 147, 145, 144, 142, 141, 139, 138, 136, 135,
133, 132, 130, 129, 127, 126, 124, 123, 121, 120, 119, 117, 116,
114, 113, 112, 110, 109, 108, 106, 105, 104, 103, 101, 100, 99,
98, 97, 96, 95, 94, 92, 91, 90, 89, 88, 87, 86, 85,
85, 84, 83, 82, 81, 80, 79, 79, 78, 77, 76, 75, 75,
74, 73, 72, 72, 71, 70, 70, 69, 68, 68, 67, 66, 66,
65, 64, 64, 63, 63, 62, 61, 61, 60, 60, 59, 58, 58,
57, 57, 56, 56, 55, 55, 54, 54, 53, 53, 52, 52, 51,
51, 50, 50, 49, 49, 48, 48, 47, 47, 47, 46, 46, 45,
45, 45, 44, 44, 43, 43, 43, 42, 42, 42, 41, 41, 41,
40, 40, 40, 39, 39, 39, 38, 38, 38, 38, 37, 37, 37,
37, 36, 36, 36, 36, 36, 35, 35, 35, 35, 35, 34, 34,
34, 34, 34, 34, 33, 33, 33, 33, 33, 33, 33, 33, 33,
33, 33, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,
32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33,
33, 33, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35,
36, 36, 36, 36, 36, 37, 37, 37, 37, 38, 38, 38, 39,
39, 39, 40, 40, 40, 41, 41, 41, 42, 42, 42, 43, 43,
43, 44, 44, 45, 45, 46, 46, 46, 47, 47, 48, 48, 49,
49, 50, 50, 51, 51, 52, 52, 53, 53, 54, 55, 55, 56,
56, 57, 57, 58, 59, 59, 60, 60, 61, 62, 62, 63, 64,
64, 65, 66, 66, 67, 68, 69, 69, 70, 71, 72, 72, 73,
74, 75, 75, 76, 77, 78, 79, 79, 80, 81, 82, 83, 84,
85, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
110, 111, 112, 113, 115, 116, 117, 118, 119, 120, 121, 123, 124,
125, 126, 128, 129, 130, 131, 133, 134, 135, 136, 138, 139, 140,
142, 143, 145, 146, 147, 149, 150, 152, 153, 155, 156, 157, 159,
160, 162, 163, 165, 167, 168, 170, 171, 173, 175, 176, 178, 179,
181, 183, 185, 186, 188, 190, 192, 193, 195, 197, 199, 201, 203,
204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 229,
231, 233, 235, 237, 239, 242, 244, 246, 248, 251, 253, 256, 258,
261, 263, 266, 268, 271, 273, 276, 279, 281, 284, 287, 290, 292,
295, 298, 301, 304, 307, 310, 314, 317, 320, 323, 327, 330, 334,
337, 341, 344, 348, 352, 356, 360, 364, 368, 372, 376, 381, 385,
390, 394])
I saved the script above as curve2array, so I run it with:
./curve2array image.png

How to convert byte array to picture [closed]

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How do I convert my byte array to a picture? I want it to be in saved JPG or BMP format, not just displayed as text or on the console.
This is sample array:
[255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0, 1, 1, 1, 0, 72, 0, 72, 0, 0, 255, 219, 0, 67, 0, 14, 10, 11, 13, 11, 9, 14, 13, 12, 13, 16, 15, 14, 17, 22, 36, 23, 22, 20, 20, 22, 44, 32, 33, 26, 36, 52, 46, 55, 54, 51, 46, 50, 50, 58, 65, 83, 70, 58, 61, 78, 62, 50, 50, 72, 98, 73, 78, 86, 88, 93, 94, 93, 56, 69, 102, 109, 101, 90, 108, 83, 91, 93, 89, 255, 219, 0, 67, 1, 15, 16, 16, 22, 19, 22, 42, 23, 23, 42, 89, 59, 50, 59, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 255, 192, 0, 17, 8, 0, 67, 0, 90, 3, 1, 34, 0, 2, 17, 1, 3, 17, 1, 255, 196, 0, 27, 0, 0, 2, 3, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 0, 2, 4, 6, 1, 7, 255, 196, 0, 46, 16, 0, 2, 2, 1, 3, 4, 0, 6, 0, 6, 3, 0, 0, 0, 0, 0, 1, 2, 0, 3, 17, 4, 18, 33, 19, 49, 65, 81, 5, 20, 34, 50, 97, 129, 35, 51, 66, 82, 113, 161, 145, 177, 241, 255, 196, 0, 25, 1, 0, 3, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 3, 0, 5, 255, 196, 0, 32, 17, 0, 3, 1, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 17, 3, 33, 49, 4, 18, 65, 50, 81, 97, 255, 218, 0, 12, 3, 1, 0, 2, 17, 3, 17, 0, 63, 0, 64, 23, 230, 43, 64, 123, 146, 48, 33, 151, 109, 78, 89, 185, 43, 244, 129, 234, 99, 171, 80, 90, 157, 152, 25, 7, 32, 226, 93, 89, 152, 41, 83, 245, 99, 13, 159, 18, 55, 254, 138, 57, 125, 101, 122, 132, 167, 40, 168, 16, 99, 129, 222, 69, 189, 107, 185, 171, 80, 25, 118, 131, 144, 115, 156, 192, 81, 131, 65, 177, 215, 42, 190, 120, 154, 180, 250, 122, 41, 2, 203, 47, 80, 196, 239, 36, 140, 136, 97, 82, 237, 5, 231, 140, 211, 165, 109, 129, 54, 18, 135, 183, 7, 17, 169, 212, 181, 40, 173, 96, 37, 79, 5, 189, 69, 86, 87, 180, 51, 171, 6, 243, 149, 237, 147, 54, 86, 83, 229, 16, 171, 51, 59, 113, 98, 191, 111, 212, 51, 78, 31, 125, 29, 245, 251, 46, 134, 212, 178, 218, 172, 235, 219, 196, 191, 76, 17, 145, 21, 211, 99, 212, 249, 175, 236, 30, 12, 105, 70, 173, 44, 93, 165, 72, 97, 46, 226, 249, 42, 186, 126, 146, 242, 112, 53, 218, 6, 213, 64, 189, 115, 122, 128, 227, 142, 227, 188, 27, 215, 42, 86, 77, 80, 208, 177, 235, 129, 233, 254, 35, 23, 174, 7, 100, 209, 80, 152, 112, 122, 95, 133, 88, 250, 94, 169, 179, 146, 50, 20, 119, 48, 26, 230, 21, 88, 136, 203, 176, 168, 236, 59, 159, 243, 49, 117, 110, 166, 210, 155, 217, 27, 177, 195, 120, 158, 239, 107, 92, 189, 132, 187, 118, 201, 158, 59, 149, 233, 234, 173, 28, 252, 57, 27, 90, 198, 189, 202, 170, 6, 78, 79, 113, 25, 184, 85, 78, 141, 149, 45, 71, 111, 117, 60, 17, 57, 154, 153, 171, 96, 245, 185, 87, 95, 34, 48, 249, 195, 171, 176, 29, 77, 228, 17, 129, 192, 238, 34, 185, 77, 96, 87, 79, 78, 155, 73, 69, 107, 163, 168, 217, 118, 75, 14, 63, 38, 21, 43, 61, 82, 21, 51, 248, 204, 207, 162, 27, 40, 110, 144, 123, 213, 120, 33, 187, 16, 61, 67, 13, 90, 223, 96, 122, 198, 8, 238, 177, 111, 142, 91, 218, 120, 141, 38, 158, 98, 44, 247, 109, 109, 140, 155, 88, 113, 159, 115, 222, 166, 208, 48, 199, 62, 79, 169, 109, 37, 226, 250, 109, 107, 16, 99, 171, 129, 159, 56, 255, 0, 201, 235, 168, 119, 42, 7, 62, 132, 202, 184, 190, 189, 203, 208, 167, 189, 52, 30, 141, 83, 161, 250, 142, 76, 217, 166, 212, 245, 9, 15, 199, 168, 156, 0, 167, 151, 24, 252, 201, 212, 193, 36, 28, 48, 154, 71, 45, 194, 79, 240, 202, 162, 95, 76, 126, 193, 76, 15, 76, 123, 139, 244, 218, 227, 140, 62, 61, 77, 95, 48, 159, 221, 44, 143, 149, 45, 118, 77, 92, 31, 209, 242, 64, 25, 156, 177, 28, 137, 161, 114, 216, 68, 4, 177, 30, 60, 192, 86, 72, 76, 159, 50, 201, 97, 22, 6, 4, 140, 30, 49, 49, 101, 33, 129, 193, 43, 130, 8, 134, 210, 186, 163, 146, 232, 31, 140, 115, 50, 245, 9, 39, 39, 36, 249, 133, 76, 240, 124, 67, 157, 4, 102, 150, 220, 41, 10, 150, 176, 172, 182, 72, 7, 180, 53, 90, 183, 210, 53, 157, 59, 3, 2, 177, 117, 119, 109, 98, 15, 57, 148, 123, 13, 231, 10, 112, 1, 201, 49, 90, 213, 140, 100, 240, 125, 240, 91, 152, 216, 149, 217, 119, 76, 31, 63, 153, 208, 116, 159, 78, 253, 71, 110, 165, 64, 99, 32, 114, 63, 83, 153, 248, 78, 152, 234, 237, 8, 70, 43, 79, 184, 231, 152, 254, 212, 170, 141, 37, 148, 37, 238, 89, 148, 133, 70, 96, 73, 63, 129, 30, 97, 53, 184, 35, 166, 186, 60, 179, 54, 218, 203, 75, 86, 219, 70, 67, 19, 156, 254, 160, 116, 203, 103, 68, 245, 173, 47, 110, 123, 145, 142, 34, 202, 236, 178, 187, 48, 114, 174, 135, 4, 71, 58, 166, 83, 66, 217, 253, 88, 236, 61, 153, 55, 29, 43, 151, 45, 102, 15, 200, 156, 180, 244, 206, 199, 248, 187, 43, 5, 136, 245, 39, 92, 249, 99, 42, 157, 55, 168, 216, 44, 41, 98, 248, 50, 163, 86, 216, 28, 87, 251, 19, 47, 4, 211, 131, 15, 244, 224, 152, 90, 233, 118, 165, 173, 82, 187, 83, 190, 76, 207, 129, 220, 96, 201, 146, 20, 228, 247, 241, 46, 195, 131, 212, 195, 60, 241, 152, 69, 99, 187, 129, 218, 100, 76, 130, 8, 239, 53, 212, 113, 201, 61, 160, 163, 130, 237, 12, 64, 108, 224, 247, 34, 71, 57, 33, 107, 224, 123, 131, 107, 25, 92, 21, 238, 124, 120, 158, 179, 251, 63, 84, 84, 16, 160, 90, 78, 90, 246, 3, 200, 94, 39, 77, 240, 199, 210, 232, 168, 172, 82, 203, 110, 170, 252, 2, 65, 201, 231, 223, 224, 78, 91, 113, 11, 140, 126, 230, 173, 14, 165, 180, 215, 173, 136, 161, 136, 247, 26, 107, 5, 164, 118, 90, 141, 62, 158, 203, 1, 101, 27, 148, 100, 145, 231, 159, 48, 26, 183, 85, 96, 120, 13, 216, 204, 21, 124, 75, 115, 90, 109, 82, 166, 204, 5, 2, 94, 219, 69, 141, 147, 140, 227, 7, 62, 102, 124, 183, 56, 243, 214, 116, 39, 189, 148, 102, 57, 35, 141, 179, 211, 91, 103, 238, 31, 238, 2, 195, 140, 159, 18, 163, 81, 102, 62, 227, 255, 0, 50, 83, 70, 142, 67, 204, 186, 114, 192, 30, 210, 73, 61, 1, 2, 40, 195, 15, 243, 46, 126, 252, 120, 245, 36, 145, 25, 199, 172, 126, 185, 226, 242, 78, 121, 146, 73, 223, 135, 4, 63, 202, 111, 212, 215, 167, 39, 253, 73, 36, 74, 240, 43, 211, 161, 214, 86, 155, 40, 59, 70, 70, 63, 234, 101, 14, 193, 73, 7, 28, 201, 36, 199, 155, 249, 6, 60, 51, 218, 236, 119, 101, 143, 136, 61, 199, 220, 146, 64, 135, 71, 255, 217]

Each MIME type has a signature(magic number). By first bytes its a JPEG img.
# your array
arr = [255, 216, 255, 224, 0, ...]
>>> bytearray(arr[:4])
bytearray(b'\xff\xd8\xff\xe0')
FF D8 FF E0 - its a jpeg signature image.
I tried:
f = open('/tmp/myimage.jpeg', 'wb')
f.write(bytearray(arr))
f.close()
and got a next image:

resizing image with numpy

lets say i have an image presented as this numpy array:
array([[ 55, 229, 185, 21, 128, 50, 109, 121, 251],
[138, 0, 143, 153, 22, 244, 102, 6, 63],
[250, 235, 57, 28, 220, 15, 217, 147, 70],
[121, 164, 128, 224, 56, 206, 104, 87, 154],
[232, 51, 20, 235, 8, 200, 119, 234, 180],
[182, 79, 79, 22, 221, 233, 54, 11, 209],
[249, 64, 92, 70, 167, 151, 214, 188, 213]], dtype=uint8)
this is 7X9 matrix and i want to double the width of the image to 7x18.
i know what to do when you want to compress an image, but im not sure what i supposed to do if i want to increase the size.
thanks!
`

Put your array in a, then
np.repeat(a, 2, axis=1)
gives
array([[ 55, 55, 229, 229, 185, 185, 21, 21, 128, 128, 50, 50, 109,
109, 121, 121, 251, 251],
[138, 138, 0, 0, 143, 143, 153, 153, 22, 22, 244, 244, 102,
102, 6, 6, 63, 63],
[250, 250, 235, 235, 57, 57, 28, 28, 220, 220, 15, 15, 217,
217, 147, 147, 70, 70],
[121, 121, 164, 164, 128, 128, 224, 224, 56, 56, 206, 206, 104,
104, 87, 87, 154, 154],
[232, 232, 51, 51, 20, 20, 235, 235, 8, 8, 200, 200, 119,
119, 234, 234, 180, 180],
[182, 182, 79, 79, 79, 79, 22, 22, 221, 221, 233, 233, 54,
54, 11, 11, 209, 209],
[249, 249, 64, 64, 92, 92, 70, 70, 167, 167, 151, 151, 214,
214, 188, 188, 213, 213]])
Which has shape 7x18.