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How to add value labels on a bar chart
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How to print y-values above each bar, please? Thank you
import matplotlib.pyplot as plt
import seaborn as sns
data = [6, 3, 1, 5, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 1, 7, 3, 1, 9, 2, 1, 4, 2, 1, 3, 1, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 1, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 9, 4, 1, 7, 2, 1, 3, 1, 4, 7, 3, 1, 9, 2, 1, 4, 2, 1, 3, 1, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 1, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 9, 4, 1, 7, 2, 1, 3, 1, 4, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 2, 1, 5, 1, 8, 4, 1, 6, 3, 9, 4, 2, 1, 6, 1, 9, 4, 2, 7, 3, 1, 5, 1, 8, 4, 2, 1, 5, 1, 8, 4, 2, 1, 9, 4, 1, 7, 3, 1, 5, 1, 8, 2, 1, 6, 3, 9, 4, 2, 1, 5, 2, 1, 4, 2, 6, 3, 1, 5, 2, 1, 6, 3, 9, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 1, 7, 3, 1, 9, 2, 1, 4, 2, 1, 3, 1, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 1, 8, 4, 1, 6, 3, 1, 5, 2]
fig, ax = plt.subplots()
ax = sns.histplot(data, discrete=True, kde=False, stat='percent')
plt.show()
If you have matplotlib 3.4.0 or higher, you can use bar_label. More info here
Code would be something like this...
ax = sns.histplot(data, discrete=True, kde=False, stat='percent')
ax.bar_label(ax.containers[0])
How to display the percentage on the y-axis, please? Doesn't stat='frequency' do this?
import matplotlib.pyplot as plt
import seaborn as sns
data = [6, 3, 1, 5, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 1, 7, 3, 1, 9, 2, 1, 4, 2, 1, 3, 1, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 1, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 9, 4, 1, 7, 2, 1, 3, 1, 4, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 2, 1, 5, 1, 8, 4, 1, 6, 3, 9, 4, 2, 1, 6, 1, 9, 4, 2, 7, 3, 1, 5, 1, 8, 4, 2, 1, 5, 1, 8, 4, 2, 1, 9, 2, 1, 7, 2, 1, 5, 2, 8, 4, 2, 6, 3, 9, 4, 2, 1, 5, 2, 1, 4, 2, 6, 3, 1, 5, 2, 1, 6, 3, 9, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 1, 7, 3, 1, 9, 2, 1, 4, 2, 1, 3, 1, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 1, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 9, 4, 1, 7, 2, 1, 3, 1, 4, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 2, 1, 5, 1, 8, 4, 1, 6, 3, 9, 4, 2, 1, 6, 1, 9, 4, 2, 7, 3, 1, 5, 1, 8, 4, 2, 1, 5, 1, 8, 4, 2, 1, 9, 4, 1, 7, 3, 1, 5, 1, 8, 2, 1, 6, 3, 9, 4, 2, 1, 5, 2, 1, 4, 2, 6, 3, 1, 5, 2, 1, 6, 3, 9, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 1, 7, 3, 1, 9, 2, 1, 4, 2, 1, 3, 1, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 1, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 9, 4, 1, 7, 2, 1, 3, 1, 4, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 2, 1, 5, 1, 8, 4, 1, 6, 3, 9, 4, 2, 1, 6, 1, 9, 4, 2, 7, 3, 1, 5, 1, 8, 4, 2, 1, 5, 1, 8, 4, 2, 1, 9, 2, 1, 4, 2, 1, 5, 1, 8, 2, 1, 6, 3, 9, 4, 2, 1, 5, 2, 1, 4, 2, 6, 3, 1, 5, 2, 1, 6, 3, 9, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 1, 7, 3, 1, 9, 2, 1, 4, 2, 1, 3, 1, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 1, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 9, 4, 1, 7, 2, 1, 3, 1, 4, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 2, 1, 5, 1, 8, 4, 1, 6, 3, 9, 4, 2, 1, 6, 1, 9, 4, 2, 7, 3, 1, 5, 1, 8, 4, 2, 1, 5, 1, 8, 4, 2, 1, 9, 4, 2, 7, 3, 1, 9, 4, 1, 7, 2, 1, 3, 1, 4, 2, 1, 5, 1, 8, 4, 2, 6, 3, 1, 5, 2, 1, 6, 3, 9, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 1, 7, 3, 1, 9, 2, 1, 4, 2, 1, 3, 1, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 1, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 9, 4, 1, 7, 2, 1, 3, 1, 4, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 2, 1, 5, 1, 8, 4, 1, 6, 3, 9, 4, 2, 1, 6, 1, 9, 4, 2, 7, 3, 1, 5, 1, 8, 4, 2, 1, 5, 1, 8, 4, 2, 1, 9, 2, 1, 7, 3, 1, 9, 4, 1, 7, 2, 1, 3, 1, 4, 2, 1, 5, 1, 8, 4, 2, 6, 3, 1, 5, 2, 1, 6, 3, 9, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 1, 7, 3, 1, 9, 2, 1, 4, 2, 1, 3, 1, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 1, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 9, 4, 1, 7, 2, 1, 3, 1, 4, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 2, 1, 5, 1, 8, 4, 1, 6, 3, 9, 4, 2, 1, 6, 1, 9, 4, 2, 7, 3, 1, 5, 1, 8, 4, 2, 1, 5, 1, 8, 4, 2, 1, 9, 4, 1, 7, 2, 1, 5, 1, 8, 4, 2, 1, 5, 1, 7, 3, 1, 5, 1, 8, 4, 2, 6, 3, 1, 5, 2, 1, 6, 3, 9, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 9, 2, 1, 4, 2, 6, 3, 9, 4, 1, 7, 3, 1, 9, 2, 1, 4, 2, 1, 3, 1, 4, 2, 7, 3, 1, 5, 2, 7, 3, 1, 5, 1, 8, 4, 1, 6, 3, 1, 5, 2, 7, 3, 1, 5, 2, 8, 4, 1, 6, 3, 9, 4, 1, 7, 2, 1, 3, 1, 4, 2, 7, 3, 1, 9, 2, 1, 4]
fig, ax = plt.subplots()
ax = sns.histplot(data, discrete=True, kde=False, stat='frequency')
plt.show()
I'm trying to knowing which is the color of a pixel through it's x and y. The colors are from this image.
Capturing the colors with Photoshop I've got this list of colors:
"#5D385A", "#6D3B47", "#6F5C4B", "#50717A", "#547057", "#4C6180", "#717080", "#705574", "#726B59", "#5E4854", "#415A4B", "#425A64", "#3A4E6F"
However, when I try to get the color of a pixel from the image, this color doesn't match with the previous list. And, I've got 95 different colors when in the image there are only 13 different colors.
I open the image and get the color from a pixel with this class:
import PIL.Image
class Image:
def __init__(self, file):
self.image = PIL.Image.open(file).convert("RGB")
def get_color(self, x, y):
color = self.image.getpixel((x,y))
color = ("#%02x%02x%02x" % color).upper()
return color
Here is a short list of x and y of positions where I take the color:
144, 74
140, 46
150, 53
85, 87
160, 48
147, 60
137, 49
149, 53
148, 60
143, 52
161, 30
166, 23
134, 38
146, 29
155, 40
129, 37
154, 66
153, 38
151, 33
128, 36
How is that possible? How can I get 95 different colors from the image when there is only 13 different colors?
Edit I:
I have get all the colors from each pixel in the image and no one has the color what I get with Photoshop.
I have got 256 different colors, this is the list and number times found it.
{'#885F7D': 15, '#541B47': 15, '#68355B': 819, '#65355D': 17, '#78384A': 19, '#7E3942': 19, '#7B3846': 4588, '#7C3346': 39, '#7D3046': 50, '#773F4C': 21, '#785A49': 4, '#775F49': 35, '#765C49': 17540, '#7A4648': 21, '#756349': 62, '#785B49': 56, '#7C3546': 14, '#765D49': 12, '#7A4F48': 14, '#7C3746': 29, '#785549': 7, '#775D4A': 8, '#785749': 8, '#551743': 1, '#6A3158': 39, '#68325A': 6, '#86617E': 1, '#66385D': 31, '#6C2C56': 6, '#6C2A56': 7, '#6D2B54': 3, '#678D97': 88, '#2C5B6A': 60, '#416C79': 43, '#3F717A': 7, '#43686A': 64, '#5C5F71': 32, '#465771': 3, '#5E5666': 14, '#5D4C66': 7, '#644160': 2, '#683C5F': 2, '#659197': 2, '#1C606C': 88, '#32767E': 61, '#227B84': 59, '#3A757A': 60, '#803342': 16, '#7D3745': 6, '#3A727B': 7374, '#3B7479': 3, '#36747C': 11, '#6C4450': 104, '#82303F': 18, '#852B3B': 28, '#694A56': 3, '#3D7179': 15, '#694E59': 15, '#7D3545': 11, '#387283': 30, '#3B717B': 17, '#3A727D': 16, '#7B5A48': 37, '#832B43': 11, '#3B7184': 21, '#2A7C66': 1, '#5D5D4E': 2, '#3B7180': 23, '#41715A': 6, '#45714D': 44, '#297D59': 6, '#407256': 32, '#417160': 13, '#437155': 5275, '#467055': 16, '#327A58': 7, '#68514E': 4, '#407756': 2, '#3C7356': 22, '#56654F': 17, '#437154': 15, '#387457': 30, '#3F7169': 14, '#4B6D54': 9, '#805C49': 105, '#735E4A': 10, '#7F5747': 63, '#755C49': 9, '#457154': 16, '#337558': 45, '#536B52': 18, '#735944': 95, '#7B614F': 96, '#5D6750': 36, '#437156': 43, '#69624D': 21, '#457151': 29, '#3D7172': 10, '#70604B': 10, '#487458': 2, '#45744D': 96, '#447352': 2, '#23596C': 2, '#3C6A7E': 59, '#3F696B': 41, '#64819B': 37, '#204D73': 92, '#3C5E82': 60, '#3A5E8A': 93, '#385B92': 1, '#3C6182': 4212, '#5D7F9A': 1, '#0C4A72': 2, '#305E82': 118, '#5C6982': 118, '#8F8D9B': 26, '#646473': 3, '#7B7482': 118, '#5A7169': 14, '#39714D': 12, '#727182': 2691, '#797189': 13, '#3E724D': 1, '#3B7155': 51, '#885947': 1, '#7D5744': 1, '#866251': 1, '#4F7056': 51, '#675C48': 106, '#707289': 10, '#736E6C': 11, '#746B51': 12, '#756C58': 116, '#82705C': 27, '#135941': 27, '#235D44': 105, '#255B44': 24, '#1D5943': 45, '#2B5C46': 108, '#2B5C45': 8, '#746C58': 5469, '#2E5C46': 17561, '#7E705B': 32, '#4F634D': 10, '#7B6E5A': 32, '#45614B': 14, '#707584': 117, '#6E788C': 1, '#72716E': 1, '#75677E': 117, '#746684': 1, '#766D59': 26, '#3D5F49': 11, '#255943': 33, '#957890': 39, '#7A5174': 117, '#7C4B7C': 2, '#775E6A': 62, '#727152': 39, '#726C58': 32, '#365E47': 12, '#683F63': 37, '#7A5476': 4212, '#79507A': 37, '#766166': 38, '#7A6D57': 15, '#6E6B56': 13, '#2D5D46': 5, '#696A54': 4, '#2C5B45': 8, '#626852': 8, '#305C46': 24, '#2E5C44': 26, '#7E577B': 2, '#7C567A': 55, '#7A517A': 58, '#784F79': 1, '#5F3855': 1, '#724F68': 57, '#727053': 59, '#856C77': 89, '#51303E': 91, '#62444F': 56, '#60404E': 1, '#767558': 56, '#654654': 7521, '#623F53': 16, '#674B54': 7, '#747057': 25, '#746B58': 40, '#623E53': 15, '#654754': 40, '#757158': 11, '#6F6C56': 2, '#644554': 29, '#613D53': 16, '#6B5555': 15, '#6F5E56': 15, '#756D57': 11, '#634354': 7, '#634153': 13, '#716457': 7, '#644254': 7, '#654354': 4, '#305C48': 3, '#726C59': 2, '#7E7055': 6, '#817155': 7, '#48615F': 4, '#0A5649': 1, '#2E5C3E': 26, '#135669': 2, '#2C5B68': 34, '#2B5C53': 21, '#2E5C41': 58, '#415F60': 3, '#0F5667': 5, '#2C5B64': 4676, '#2C5B66': 19, '#2C5B5B': 17, '#2E5C4D': 8, '#175966': 7, '#375D61': 2, '#61675B': 1, '#2F5B64': 20, '#2C5B60': 16, '#2F5B4A': 3, '#55675E': 2, '#2E5C4A': 8, '#275C64': 23, '#674654': 10, '#385260': 1, '#684553': 26, '#1C5E66': 46, '#564D59': 5, '#3D5660': 8, '#4F4F5B': 10, '#5E4A57': 7, '#365961': 5, '#47525D': 8, '#5C4B57': 4, '#614756': 2, '#5A4759': 36, '#504A60': 10, '#404B67': 7, '#2C5667': 18, '#8B6B75': 1, '#2B4D71': 876, '#2D5D62': 18, '#7C6D7B': 1, '#58728D': 16, '#0A365F': 16, '#21553E': 4, '#335F4B': 1, '#35624D': 20, '#3D6752': 4}
I don't understand anything. How is it possible that no one pixel has the color that I've got in Photoshop?
Edit II:
With the same code, I have got the color map of another image. This is the image:
The predominant colors that you can see in this image are these:
"#F50A22", "#00EC83", "#00A200", "#0007A4", "#9D132B", "#734500", "#6230FF", "#F42AFF", "#BEFF00", "#EC7800", "#65DCD1", "#FF6D00" : "#004500"
Executing the test, how I said, the same code. I've got that all these colors are found it in the image among others! And no one of them how in the first image.
The results are:
Colors matched: {'#F50A22': 2245, '#00EC83': 9437, '#00A200': 21039, '#0007A4': 8772, '#9D132B': 99, '#734500': 2970, '#6230FF': 112, '#F42AFF': 5271, '#BEFF00': 2380, '#EC7800': 3076, '#65DCD1': 6503, '#FF6D00': 4709, '#004500': 6612}
colors matched: 13
And other colors found it in the image are:
Other colors: {'#FFFFFF': 1931, '#FCFFFD': 27, '#FAFFFB': 2, '#F7FEF9': 12, '#F4FEF7': 10, '#F6FEF8': 20, '#F6FDF8': 1, '#F9FEFA': 12, '#FBFEFC': 9, '#FEFFFE': 40, '#FAFEFB': 12, '#FBFFFC': 7, '#F3FEF6': 7, '#F4FDF6': 2, '#F5FDF7': 1, '#F2FDF5': 3, '#EEFDF2': 3, '#F2FDF6': 7, '#F4FEF8': 12, '#EFFDF4': 3, '#E5FCEC': 4, '#DAFAE5': 1, '#D3FAE0': 3, '#D4FAE0': 1, '#DAFAE4': 1, '#DFFBE8': 1, '#E9FCEF': 3, '#EDFDF2': 2, '#EFFDF3': 3, '#E2FBEA': 3, '#E2FCEA': 3, '#EFFEF3': 1, '#F2FEF5': 1, '#EDFCF1': 2, '#EBFDF0': 1, '#F1FDF4': 1, '#F3FEF7': 4, '#EDFDF1': 2, '#E7FCEE': 3, '#E3FCEB': 1, '#E0FCE9': 1, '#DCFBE6': 5, '#DAFBE5': 1, '#D9FAE4': 1, '#D9FAE3': 1, '#E3FCEC': 1, '#EEFDF3': 1, '#D7FAE2': 1, '#D1FADF': 1, '#D1FADE': 1, '#D6FAE2': 1, '#E1FBEA': 2, '#EBFDF1': 1, '#DFFBE9': 1, '#DEFBE7': 2, '#DBFBE5': 1, '#F6132A': 111, '#00EC84': 33, '#00EC85': 16, '#04EC86': 11, '#14EC87': 3, '#F40D23': 3, '#F20E24': 1, '#F50B22': 8, '#F11426': 2, '#F40C23': 1, '#EF1A28': 1, '#EE1B29': 1, '#F01827': 1, '#F21125': 1, '#F40D24': 1, '#F40E24': 1, '#774A03': 165, '#F40E23': 1, '#F50C22': 1, '#F6142A': 3, '#00EC82': 1, '#00EB82': 2, '#00EA7F': 1, '#00EB81': 1, '#6FE09C': 1, '#7E5416': 2, '#00A300': 78, '#00A500': 43, '#D9403B': 1, '#00AB16': 1, '#00A600': 40, '#00A700': 1123, '#5E2AFF': 2471, '#00B213': 2, '#00AA00': 6, '#7A4F0D': 3, '#6636FF': 2, '#00AE02': 2, '#00AC00': 3, '#00AB08': 2, '#00A800': 12, '#00A900': 8, '#00B317': 1, '#6C3CFF': 1, '#00AE00': 2, '#00AE14': 1, '#00A903': 1, '#7F55FE': 1, '#6CEE9F': 1, '#00AD00': 2, '#6CDCD2': 268, '#6A3CFE': 2, '#7549FF': 1, '#4ED688': 1, '#6B3DFF': 1, '#5E2BFF': 24, '#6839FD': 1, '#6231FE': 1, '#5E31FC': 2, '#00AF08': 1, '#00AC07': 1, '#6339FA': 1, '#5F33FB': 3, '#5F30FD': 3, '#00B10E': 1, '#656565': 1, '#00AB00': 2, '#00B02D': 2, '#6037F9': 1, '#5F2EFE': 2, '#5F3EF5': 1, '#5F32FC': 1, '#6040F4': 1, '#5F32FB': 2, '#6041F3': 1, '#6042F2': 1, '#7145FC': 1, '#5F2CFF': 10, '#6147EF': 1, '#6454EA': 1, '#6036F9': 1, '#685AEA': 1, '#00AF2F': 1, '#6B57EE': 1, '#00B110': 1, '#00AA02': 1, '#8ADBD3': 3, '#683CFB': 1, '#72DDD2': 3, '#6D47F8': 1, '#775EF3': 1, '#9CD7D1': 2, '#5E31FD': 1, '#00AB18': 1, '#82DCD3': 1, '#673EFB': 1, '#7450F9': 1, '#612EFF': 8, '#6236FB': 1, '#602CFF': 5, '#6B49F7': 1, '#602DFF': 7, '#5F2BFF': 6, '#6334FD': 1, '#2EEB8B': 1, '#704AFB': 1, '#6231FF': 1, '#6738FE': 1, '#612DFF': 3, '#3FEB8F': 1, '#66DBD1': 5, '#67D8D2': 1, '#00AE2B': 1, '#65DAD2': 1, '#F42DFF': 15, '#FC67FF': 6, '#F246FA': 1, '#F84CFF': 7, '#6233FF': 1, '#6ADCD2': 22, '#6132FE': 1, '#FBFEFE': 2, '#F434FF': 5, '#F8FDFC': 1, '#68DCD1': 33, '#6034FE': 1, '#FB5DFF': 2, '#FAFEFD': 2, '#F2FBFA': 1, '#6442FA': 1, '#6031FF': 1, '#F539FF': 7, '#F5FCFC': 1, '#E7F9F6': 1, '#F02AFF': 5, '#EFFBF9': 2, '#DDF6F3': 1, '#5F2EFF': 1, '#DD2BFF': 1, '#E82AFF': 1, '#F32AFF': 8, '#F744FF': 3, '#E7F9F7': 1, '#CFF2EF': 1, '#6136FD': 1, '#5F2AFF': 1, '#DD2AFF': 1, '#E42AFF': 1, '#EC2AFF': 2, '#E1F7F4': 1, '#C3EFEA': 1, '#6031FE': 1, '#EA2AFF': 2, '#ED2AFF': 1, '#DAF6F2': 1, '#BAEEE7': 1, '#6DDDD3': 2, '#6937FF': 1, '#ED37FE': 1, '#D7F5F1': 2, '#B6EDE6': 1, '#69DDD2': 2, '#74DFD4': 1, '#81DED9': 1, '#EF2BFF': 1, '#B3ECE7': 1, '#7ED7D4': 1, '#F22AFF': 2, '#D9F5F2': 1, '#B7EDE7': 1, '#DB39FC': 1, '#F12EFF': 1, '#E0F7F4': 1, '#C2EFEA': 1, '#87DBD3': 1, '#E737FE': 1, '#E6F8F6': 2, '#CCF2EE': 1, '#84DCD3': 1, '#ECFAF9': 1, '#D8F5F2': 1, '#65DCD0': 5, '#69DBCF': 6, '#6ADCD1': 1, '#98D3CD': 1, '#F440FC': 1, '#F42CFF': 7, '#F4FCFB': 1, '#6FD9CC': 2, '#6FD9CB': 3, '#6BDBCF': 1, '#7ED7C8': 1, '#80D3C1': 1, '#F531FF': 3, '#F42BFF': 35, '#FDFEFE': 2, '#F8FDFD': 1, '#83D8CB': 1, '#7ED3C2': 1, '#FF7100': 78, '#FEFFFF': 1, '#97D2CC': 1, '#FF7000': 40, '#FF6E00': 40, '#FF7925': 1, '#F33FF7': 1, '#6FDDD2': 2, '#FF6B00': 8, '#F62DF4': 1, '#F52BFB': 1, '#FF7409': 1, '#F62DF3': 1, '#F52BFC': 3, '#A2CFCA': 1, '#F73FE3': 1, '#F52DF9': 1, '#F42AFE': 1, '#FF7400': 4, '#FF730E': 1, '#FC36D5': 1, '#F62DF1': 1, '#F52BFD': 1, '#F52CFF': 6, '#F52DFF': 13, '#76DDD3': 2, '#FF6C00': 8, '#F831EA': 1, '#F52BFA': 3, '#F632FF': 1, '#8DDAD2': 1, '#F836E6': 1, '#F52BF9': 2, '#A4CCC8': 1, '#FF6A08': 1, '#7ADDD3': 1, '#FF690B': 1, '#F42BFE': 1, '#92D9D2': 2, '#FF6E0B': 1, '#F031FA': 1, '#A7C8C5': 1, '#FF6429': 1, '#FF7200': 62, '#FF671A': 1, '#7EDCD3': 1, '#EC35F6': 1, '#6CDACE': 1, '#6DDBD0': 1, '#FF671C': 1, '#FF7104': 1, '#FF6911': 1, '#FF642C': 1, '#FF6B23': 1, '#FF6E13': 1, '#FF7300': 5, '#F530FF': 1, '#F532FF': 3, '#6DDDD2': 1, '#F533FF': 1, '#F635FF': 1, '#F537FF': 8, '#F539FE': 2, '#F538FF': 9, '#00AC1A': 4, '#FF780E': 1, '#004B04': 29, '#FF873C': 1, '#FF7C1B': 1, '#FF7606': 4, '#FF780C': 1, '#FF7502': 1, '#FF7504': 1, '#FF770A': 2, '#004A03': 9, '#004A02': 5, '#F73FFF': 2, '#F435FF': 1, '#004700': 9, '#FF7A0F': 1, '#F52EFF': 1, '#F63BFF': 1, '#F638FF': 1, '#004600': 22, '#004B03': 3, '#004901': 8, '#FF7D1D': 1, '#F43EFB': 1, '#FF8533': 1, '#F62DF6': 1, '#FF7F24': 1, '#004902': 3, '#004900': 1, '#F441FC': 1, '#C1E057': 1, '#C2FD00': 5, '#C1F700': 1, '#C0FE00': 176, '#C4EE30': 2, '#C3E846': 1, '#C2FB00': 2, '#FEFEFE': 9, '#004C07': 11, '#B8FB00': 2, '#C3FB00': 1, '#FDFEFD': 5, '#BAFB00': 2, '#C5F11A': 2, '#B3F600': 2, '#BEFC00': 1, '#C1FD00': 7, '#FBFCFB': 3, '#BCDE52': 1, '#BBFE00': 9, '#FAFBFA': 2, '#B6F700': 1, '#BDFB00': 1, '#C3F800': 5, '#F331FF': 1, '#B2F500': 1, '#BDF900': 1, '#BDFD00': 1, '#BBFC00': 2, '#BDFE00': 10, '#C3EA40': 1, '#FCFDFC': 4, '#B5F600': 2, '#BCFD00': 7, '#C4E847': 1, '#CDFD09': 3, '#2337B3': 1, '#4251B6': 1, '#C5ED37': 1, '#D5FF3E': 17, '#0012A7': 625, '#004B06': 1, '#CFFE22': 5, '#B6F900': 2, '#C5FD00': 5, '#D3FF3C': 3, '#005010': 1, '#CBFD00': 5, '#C2FE00': 3, '#B8F900': 2, '#D2FE31': 8, '#C8FD00': 3, '#B9FA00': 2, '#C4FD00': 3, '#F8FBF9': 2, '#CCFE08': 3, '#F4F6F4': 2, '#C7FD00': 5, '#EBF1EC': 1, '#F8F9F7': 1, '#E1EAE4': 1, '#004701': 1, '#132AAF': 2, '#D5E2D8': 1, '#F1F5F2': 2, '#D1DFD5': 1, '#EC8417': 1, '#D4E1D7': 1, '#F3F5F3': 1, '#D9E4DC': 1, '#EB7800': 15, '#ED7B00': 133, '#F6F8F5': 1, '#DEE8E0': 1, '#E6EDE6': 1, '#FAFDFB': 1, '#EAF0EB': 1, '#EEF3EF': 1, '#EA7700': 2, '#F5F7F5': 1, '#C2E64E': 1, '#CAFD00': 2, '#F7FAF8': 1, '#E87700': 2, '#EA7800': 7, '#004C06': 2, '#CFFE20': 2, '#004A05': 1, '#E37600': 1, '#E67700': 1, '#00591D': 1, '#990A22': 2077, '#A6293C': 1, '#021EAA': 1, '#0007A3': 6, '#0009A1': 2, '#001697': 2, '#000B9F': 2, '#00119B': 1})
total other colors: 448
Both images are png.
How is it possible that I found all the colors among others in the second image and not found anyone of the color searched in the first image?
you can see 13 colors yes! but the code doesn't because it's more precise than your eyes.
try zooming into the picture more, you'll see that between the colors there is another lighter one, which can consist of more than one color to go from one to the other, also I noticed some black and white at the left side "maybe it's just from your snipping tool or something"
but what I'm saying is, the code is right :)
you can try and create a photo using paint and only two colors with the fill tool, and make sure it's only one color without any gradient.
I found the problem and the solution. The problem is that I'm using images which has been created from a previous export. I mean, I have resized and make an export from an original imagin and in this momento something happens in Photoshop or whatever other program which produce an image with many other colors and not the original colors.
So, you have to run the process over the original version of the image, the export from the vectorized image. If you make an export from this export and then run the process, you will have problems like me.
I have frequency of each bigrams of a dataset.I need to sort it by descending order and visualise the top n bigrams.This is my frequency associated with each bigrams
{('best', 'price'): 95, ('price', 'range'): 190, ('range', 'got'): 5, ('got', 'diwali'): 2, ('diwali', 'sale'): 2, ('sale', 'simply'): 1, ('simply', 'amazed'): 1, ('amazed', 'performance'): 1, ('performance', 'camera'): 30, ('camera', 'clarity'): 35, ('clarity', 'device'): 1, ('device', 'speed'): 1, ('speed', 'looks'): 1, ('looks', 'display'): 1, ('display', 'everything'): 2, ('everything', 'nice'): 5, ('nice', 'heats'): 2, ('heats', 'lot'): 14, ('lot', 'u'): 2, ('u', 'using'): 3, ('using', 'months'): 20, ('months', 'no'): 10, ('no', 'problems'): 8, ('problems', 'whatsoever'): 1, ('whatsoever', 'great'): 1
Can anyone help me visualise these bigrams?
If I understand you correctly, this is what you need
import seaborn as sns
bg_dict = {('best', 'price'): 95, ('price', 'range'): 190, ('range', 'got'): 5, ('got', 'diwali'): 2, ('diwali', 'sale'): 2, ('sale', 'simply'): 1,
('simply', 'amazed'): 1, ('amazed', 'performance'): 1, ('performance', 'camera'): 30, ('camera', 'clarity'): 35, ('clarity', 'device'): 1,
('device', 'speed'): 1, ('speed', 'looks'): 1, ('looks', 'display'): 1, ('display', 'everything'): 2, ('everything', 'nice'): 5, ('nice', 'heats'): 2, ('heats', 'lot'): 14,
('lot', 'u'): 2, ('u', 'using'): 3, ('using', 'months'): 20, ('months', 'no'): 10, ('no', 'problems'): 8, ('problems', 'whatsoever'): 1, ('whatsoever', 'great'): 1}
bg_dict_sorted = sorted(bg_dict.items(), key=lambda kv: kv[1], reverse=True)
bg, counts = list(zip(*bg_dict_sorted))
bg_str = list(map(lambda x: '-'.join(x), bg))
sns.barplot(bg_str, counts)
I have a numpy array of milliseconds in integers, which I want to convert to an array of Python datetimes via a timedelta operation.
The following MWE works, but I'm convinced there is a more elegant approach or with better performence than multiplication by 1 ms.
start = pd.Timestamp('2016-01-02 03:04:56.789101').to_pydatetime()
dt = np.array([ 19, 14980, 19620, 54964615, 54964655, 86433958])
time_arr = start + dt * timedelta(milliseconds=1)
So your approach produces:
In [56]: start = pd.Timestamp('2016-01-02 03:04:56.789101').to_pydatetime()
In [57]: start
Out[57]: datetime.datetime(2016, 1, 2, 3, 4, 56, 789101)
In [58]: dt = np.array([ 19, 14980, 19620, 54964615, 54964655, 86433958])
In [59]: time_arr = start + dt * timedelta(milliseconds=1)
In [60]: time_arr
Out[60]:
array([datetime.datetime(2016, 1, 2, 3, 4, 56, 808101),
datetime.datetime(2016, 1, 2, 3, 5, 11, 769101),
datetime.datetime(2016, 1, 2, 3, 5, 16, 409101),
datetime.datetime(2016, 1, 2, 18, 21, 1, 404101),
datetime.datetime(2016, 1, 2, 18, 21, 1, 444101),
datetime.datetime(2016, 1, 3, 3, 5, 30, 747101)], dtype=object)
The equivalent using np.datetime64 types:
In [61]: dt.astype('timedelta64[ms]')
Out[61]: array([ 19, 14980, 19620, 54964615, 54964655, 86433958], dtype='timedelta64[ms]')
In [62]: np.datetime64(start)
Out[62]: numpy.datetime64('2016-01-02T03:04:56.789101')
In [63]: np.datetime64(start) + dt.astype('timedelta64[ms]')
Out[63]:
array(['2016-01-02T03:04:56.808101', '2016-01-02T03:05:11.769101',
'2016-01-02T03:05:16.409101', '2016-01-02T18:21:01.404101',
'2016-01-02T18:21:01.444101', '2016-01-03T03:05:30.747101'], dtype='datetime64[us]')
I can produce the same array from your time_arr with np.array(time_arr, dtype='datetime64[us]').
tolist converts these datetime64 items to datetime objects:
In [97]: t1=np.datetime64(start) + dt.astype('timedelta64[ms]')
In [98]: t1.tolist()
Out[98]:
[datetime.datetime(2016, 1, 2, 3, 4, 56, 808101),
datetime.datetime(2016, 1, 2, 3, 5, 11, 769101),
datetime.datetime(2016, 1, 2, 3, 5, 16, 409101),
datetime.datetime(2016, 1, 2, 18, 21, 1, 404101),
datetime.datetime(2016, 1, 2, 18, 21, 1, 444101),
datetime.datetime(2016, 1, 3, 3, 5, 30, 747101)]
or wrap it back in an array to get your time_arr:
In [99]: np.array(t1.tolist())
Out[99]:
array([datetime.datetime(2016, 1, 2, 3, 4, 56, 808101),
...
datetime.datetime(2016, 1, 3, 3, 5, 30, 747101)], dtype=object)
Just for the calculation datatime64 is faster, but with the conversions, it may not be the fastest overall.
https://docs.scipy.org/doc/numpy/reference/arrays.datetime.html