以百分比、多列、python为单位计算值在列中出现的频率

2024-09-24 06:35:15 发布

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我正在努力计算1-10的值相对于总值（百分比）在一列中出现的频率。获取4个不同列中的值1-10的百分比。我尝试使用以下代码，但这只适用于第1列和第2列，并为第3列和第4列提供NAN值。有人知道为什么吗？谢谢大家!

{'job1_category': {0: nan, 1: 4.0, 2: 5.0, 3: 5.0, 4: 4.0, 5: 4.0, 6: 5.0, 7: 4.0, 8: 4.0, 9: 4.0, 10: 4.0, 11: 4.0, 12: 4.0, 13: 4.0, 14: nan, 15: 4.0, 16: 3.0, 17: 7.0, 18: 4.0, 19: 4.0, 20: nan, 21: nan, 22: 4.0, 23: 1.0, 24: 4.0, 25: 1.0, 26: 4.0, 27: 2.0, 28: 5.0, 29: 3.0, 30: 4.0, 31: 5.0, 32: 4.0, 33: 4.0, 34: 4.0, 35: 4.0, 36: 3.0, 37: 4.0, 38: 4.0, 39: 4.0, 40: 9.0, 41: 4.0, 42: 4.0, 43: 3.0, 44: 4.0, 45: 9.0, 46: 10.0, 47: nan, 48: 10.0, 49: 4.0, 50: 8.0, 51: nan, 52: 5.0, 53: 8.0, 54: 4.0, 55: nan, 56: 4.0, 57: 8.0, 58: 4.0, 59: 4.0, 60: 4.0, 61: 4.0, 62: 8.0, 63: 4.0, 64: 8.0, 65: 7.0, 66: 4.0, 67: 7.0, 68: 8.0, 69: 7.0, 70: nan, 71: 7.0, 72: nan, 73: 10.0, 74: 7.0, 75: 6.0, 76: 7.0, 77: 4.0, 78: 7.0, 79: 7.0, 80: 7.0, 81: 7.0, 82: 4.0, 83: nan, 84: 4.0, 85: nan, 86: 4.0, 87: 9.0, 88: 4.0, 89: 4.0, 90: 4.0, 91: 4.0, 92: 4.0, 93: 7.0, 94: 2.0, 95: 4.0, 96: 4.0, 97: nan, 98: 1.0, 99: 9.0}, 'job2_category': {0: 6.0, 1: 5.0, 2: 4.0, 3: 5.0, 4: 6.0, 5: 4.0, 6: 5.0, 7: 5.0, 8: 2.0, 9: 4.0, 10: 4.0, 11: nan, 12: 5.0, 13: 4.0, 14: nan, 15: 1.0, 16: 4.0, 17: 4.0, 18: 4.0, 19: 4.0, 20: 4.0, 21: 3.0, 22: 4.0, 23: 4.0, 24: 4.0, 25: 4.0, 26: 8.0, 27: 6.0, 28: 6.0, 29: 3.0, 30: 4.0, 31: 5.0, 32: 7.0, 33: 4.0, 34: 4.0, 35: 4.0, 36: 4.0, 37: 4.0, 38: 4.0, 39: 4.0, 40: 4.0, 41: 5.0, 42: 5.0, 43: 4.0, 44: 4.0, 45: 1.0, 46: 10.0, 47: 10.0, 48: 1.0, 49: 4.0, 50: 4.0, 51: nan, 52: 7.0, 53: 4.0, 54: 4.0, 55: 9.0, 56: 9.0, 57: 4.0, 58: 3.0, 59: 4.0, 60: 4.0, 61: nan, 62: 7.0, 63: nan, 64: 7.0, 65: 7.0, 66: 7.0, 67: 7.0, 68: 8.0, 69: 7.0, 70: 7.0, 71: 7.0, 72: nan, 73: 10.0, 74: 8.0, 75: 7.0, 76: 8.0, 77: 5.0, 78: nan, 79: 7.0, 80: nan, 81: 4.0, 82: 7.0, 83: nan, 84: 4.0, 85: nan, 86: 4.0, 87: 4.0, 88: 4.0, 89: 4.0, 90: 10.0, 91: 5.0, 92: nan, 93: 7.0, 94: 4.0, 95: 4.0, 96: 4.0, 97: nan, 98: 1.0, 99: 9.0}, 'job3_category': {0: 5.0, 1: 5.0, 2: 4.0, 3: nan, 4: 10.0, 5: nan, 6: nan, 7: nan, 8: nan, 9: nan, 10: 4.0, 11: 4.0, 12: 5.0, 13: nan, 14: 4.0, 15: 1.0, 16: 4.0, 17: 4.0, 18: 4.0, 19: 4.0, 20: nan, 21: nan, 22: nan, 23: nan, 24: nan, 25: 1.0, 26: 4.0, 27: nan, 28: nan, 29: 3.0, 30: 4.0, 31: 3.0, 32: 4.0, 33: nan, 34: nan, 35: nan, 36: 4.0, 37: nan, 38: nan, 39: 4.0, 40: 7.0, 41: 5.0, 42: nan, 43: 4.0, 44: 4.0, 45: 1.0, 46: 7.0, 47: 1.0, 48: 7.0, 49: 7.0, 50: 4.0, 51: 8.0, 52: nan, 53: 4.0, 54: 7.0, 55: nan, 56: 8.0, 57: nan, 58: nan, 59: 3.0, 60: nan, 61: nan, 62: nan, 63: nan, 64: nan, 65: 7.0, 66: 7.0, 67: 7.0, 68: 7.0, 69: 8.0, 70: 7.0, 71: 4.0, 72: 8.0, 73: nan, 74: 3.0, 75: 10.0, 76: 4.0, 77: 8.0, 78: 8.0, 79: nan, 80: nan, 81: 4.0, 82: 7.0, 83: 4.0, 84: nan, 85: 8.0, 86: 4.0, 87: 4.0, 88: 4.0, 89: nan, 90: 5.0, 91: 7.0, 92: 4.0, 93: nan, 94: 1.0, 95: nan, 96: nan, 97: 10.0, 98: 1.0, 99: 1.0}, 'job4_category': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan, 5: nan, 6: nan, 7: nan, 8: nan, 9: nan, 10: 4.0, 11: nan, 12: nan, 13: nan, 14: 4.0, 15: 8.0, 16: nan, 17: 4.0, 18: 3.0, 19: nan, 20: nan, 21: nan, 22: nan, 23: nan, 24: nan, 25: nan, 26: nan, 27: nan, 28: nan, 29: nan, 30: nan, 31: nan, 32: 4.0, 33: nan, 34: nan, 35: nan, 36: 4.0, 37: nan, 38: nan, 39: nan, 40: 4.0, 41: nan, 42: nan, 43: nan, 44: nan, 45: nan, 46: nan, 47: 7.0, 48: nan, 49: nan, 50: 5.0, 51: 8.0, 52: nan, 53: 4.0, 54: nan, 55: nan, 56: 8.0, 57: nan, 58: nan, 59: nan, 60: nan, 61: nan, 62: nan, 63: nan, 64: nan, 65: 8.0, 66: 7.0, 67: nan, 68: nan, 69: nan, 70: 8.0, 71: 5.0, 72: nan, 73: nan, 74: nan, 75: 10.0, 76: nan, 77: 4.0, 78: nan, 79: nan, 80: nan, 81: nan, 82: nan, 83: nan, 84: nan, 85: nan, 86: 10.0, 87: nan, 88: 8.0, 89: nan, 90: nan, 91: nan, 92: nan, 93: nan, 94: 4.0, 95: nan, 96: nan, 97: nan, 98: nan, 99: 5.0}}

job1_freq = data_rel1.groupby('job1_category').size()
job2_freq = data_rel1.groupby('job2_category').size()
job3_freq = data_rel1.groupby('job3_category').size()
job4_freq = data_rel1.groupby('job4_category').size()

data_freq = pd.concat([job1_freq, job2_freq, job3_freq, job4_freq], axis=1)

data_freq.columns = [1,2,3,4]

data_freq["prob_1"] = data_freq[1]/sum(data_freq[1])
data_freq["prob_2"] = data_freq[2]/sum(data_freq[2])
data_freq["prob_3"] = data_freq[3]/sum(data_freq[3])
data_freq["prob_4"] = data_freq[4]/sum(data_freq[4])

Tags： datasizenan百分比sumfreqgroupbycategory
3条回答
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1楼 · 编辑于 2024-09-24 06:35:15

您可以将数据融合，然后将其分组以获得每个双计数，然后再次分组以获得总和。注意.transform()对数据进行分组，然后返回与原始数据帧长度相同的结果

样本数据：

from numpy import nan
import pandas as pd
d = {'job1_category': {0: nan, 1: 4.0, 2: 5.0, 3: 5.0, 4: 4.0, 5: 4.0, 6: 5.0, 7: 4.0, 8: 4.0, 9: 4.0, 10: 4.0, 11: 4.0, 12: 4.0, 13: 4.0, 14: nan, 15: 4.0, 16: 3.0, 17: 7.0, 18: 4.0, 19: 4.0, 20: nan, 21: nan, 22: 4.0, 23: 1.0, 24: 4.0, 25: 1.0, 26: 4.0, 27: 2.0, 28: 5.0, 29: 3.0, 30: 4.0, 31: 5.0, 32: 4.0, 33: 4.0, 34: 4.0, 35: 4.0, 36: 3.0, 37: 4.0, 38: 4.0, 39: 4.0, 40: 9.0, 41: 4.0, 42: 4.0, 43: 3.0, 44: 4.0, 45: 9.0, 46: 10.0, 47: nan, 48: 10.0, 49: 4.0, 50: 8.0, 51: nan, 52: 5.0, 53: 8.0, 54: 4.0, 55: nan, 56: 4.0, 57: 8.0, 58: 4.0, 59: 4.0, 60: 4.0, 61: 4.0, 62: 8.0, 63: 4.0, 64: 8.0, 65: 7.0, 66: 4.0, 67: 7.0, 68: 8.0, 69: 7.0, 70: nan, 71: 7.0, 72: nan, 73: 10.0, 74: 7.0, 75: 6.0, 76: 7.0, 77: 4.0, 78: 7.0, 79: 7.0, 80: 7.0, 81: 7.0, 82: 4.0, 83: nan, 84: 4.0, 85: nan, 86: 4.0, 87: 9.0, 88: 4.0, 89: 4.0, 90: 4.0, 91: 4.0, 92: 4.0, 93: 7.0, 94: 2.0, 95: 4.0, 96: 4.0, 97: nan, 98: 1.0, 99: 9.0}, 'job2_category': {0: 6.0, 1: 5.0, 2: 4.0, 3: 5.0, 4: 6.0, 5: 4.0, 6: 5.0, 7: 5.0, 8: 2.0, 9: 4.0, 10: 4.0, 11: nan, 12: 5.0, 13: 4.0, 14: nan, 15: 1.0, 16: 4.0, 17: 4.0, 18: 4.0, 19: 4.0, 20: 4.0, 21: 3.0, 22: 4.0, 23: 4.0, 24: 4.0, 25: 4.0, 26: 8.0, 27: 6.0, 28: 6.0, 29: 3.0, 30: 4.0, 31: 5.0, 32: 7.0, 33: 4.0, 34: 4.0, 35: 4.0, 36: 4.0, 37: 4.0, 38: 4.0, 39: 4.0, 40: 4.0, 41: 5.0, 42: 5.0, 43: 4.0, 44: 4.0, 45: 1.0, 46: 10.0, 47: 10.0, 48: 1.0, 49: 4.0, 50: 4.0, 51: nan, 52: 7.0, 53: 4.0, 54: 4.0, 55: 9.0, 56: 9.0, 57: 4.0, 58: 3.0, 59: 4.0, 60: 4.0, 61: nan, 62: 7.0, 63: nan, 64: 7.0, 65: 7.0, 66: 7.0, 67: 7.0, 68: 8.0, 69: 7.0, 70: 7.0, 71: 7.0, 72: nan, 73: 10.0, 74: 8.0, 75: 7.0, 76: 8.0, 77: 5.0, 78: nan, 79: 7.0, 80: nan, 81: 4.0, 82: 7.0, 83: nan, 84: 4.0, 85: nan, 86: 4.0, 87: 4.0, 88: 4.0, 89: 4.0, 90: 10.0, 91: 5.0, 92: nan, 93: 7.0, 94: 4.0, 95: 4.0, 96: 4.0, 97: nan, 98: 1.0, 99: 9.0}, 'job3_category': {0: 5.0, 1: 5.0, 2: 4.0, 3: nan, 4: 10.0, 5: nan, 6: nan, 7: nan, 8: nan, 9: nan, 10: 4.0, 11: 4.0, 12: 5.0, 13: nan, 14: 4.0, 15: 1.0, 16: 4.0, 17: 4.0, 18: 4.0, 19: 4.0, 20: nan, 21: nan, 22: nan, 23: nan, 24: nan, 25: 1.0, 26: 4.0, 27: nan, 28: nan, 29: 3.0, 30: 4.0, 31: 3.0, 32: 4.0, 33: nan, 34: nan, 35: nan, 36: 4.0, 37: nan, 38: nan, 39: 4.0, 40: 7.0, 41: 5.0, 42: nan, 43: 4.0, 44: 4.0, 45: 1.0, 46: 7.0, 47: 1.0, 48: 7.0, 49: 7.0, 50: 4.0, 51: 8.0, 52: nan, 53: 4.0, 54: 7.0, 55: nan, 56: 8.0, 57: nan, 58: nan, 59: 3.0, 60: nan, 61: nan, 62: nan, 63: nan, 64: nan, 65: 7.0, 66: 7.0, 67: 7.0, 68: 7.0, 69: 8.0, 70: 7.0, 71: 4.0, 72: 8.0, 73: nan, 74: 3.0, 75: 10.0, 76: 4.0, 77: 8.0, 78: 8.0, 79: nan, 80: nan, 81: 4.0, 82: 7.0, 83: 4.0, 84: nan, 85: 8.0, 86: 4.0, 87: 4.0, 88: 4.0, 89: nan, 90: 5.0, 91: 7.0, 92: 4.0, 93: nan, 94: 1.0, 95: nan, 96: nan, 97: 10.0, 98: 1.0, 99: 1.0}, 'job4_category': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan, 5: nan, 6: nan, 7: nan, 8: nan, 9: nan, 10: 4.0, 11: nan, 12: nan, 13: nan, 14: 4.0, 15: 8.0, 16: nan, 17: 4.0, 18: 3.0, 19: nan, 20: nan, 21: nan, 22: nan, 23: nan, 24: nan, 25: nan, 26: nan, 27: nan, 28: nan, 29: nan, 30: nan, 31: nan, 32: 4.0, 33: nan, 34: nan, 35: nan, 36: 4.0, 37: nan, 38: nan, 39: nan, 40: 4.0, 41: nan, 42: nan, 43: nan, 44: nan, 45: nan, 46: nan, 47: 7.0, 48: nan, 49: nan, 50: 5.0, 51: 8.0, 52: nan, 53: 4.0, 54: nan, 55: nan, 56: 8.0, 57: nan, 58: nan, 59: nan, 60: nan, 61: nan, 62: nan, 63: nan, 64: nan, 65: 8.0, 66: 7.0, 67: nan, 68: nan, 69: nan, 70: 8.0, 71: 5.0, 72: nan, 73: nan, 74: nan, 75: 10.0, 76: nan, 77: 4.0, 78: nan, 79: nan, 80: nan, 81: nan, 82: nan, 83: nan, 84: nan, 85: nan, 86: 10.0, 87: nan, 88: 8.0, 89: nan, 90: nan, 91: nan, 92: nan, 93: nan, 94: 4.0, 95: nan, 96: nan, 97: nan, 98: nan, 99: 5.0}}
df = pd.DataFrame(d)

代码：

df_melted = df.melt()
df_grouped = df_melted.groupby(['variable', 'value'])['value'].count().to_frame()
df_grouped['pct'] = df_grouped['value'] / df_grouped.groupby(['variable'])['value'].transform('count')


df_grouped['pct']

输出：

variable       value
job1_category  1.0      0.300000
               2.0      0.200000
               3.0      0.400000
               4.0      4.700000
               5.0      0.600000
               6.0      0.100000
               7.0      1.200000
               8.0      0.600000
               9.0      0.400000
               10.0     0.300000
job2_category  1.0      0.400000
               2.0      0.100000
               3.0      0.300000
               4.0      4.100000
               5.0      1.000000
               6.0      0.400000
               7.0      1.400000
               8.0      0.400000
               9.0      0.300000
               10.0     0.400000
job3_category  1.0      1.000000
               3.0      0.571429
               4.0      3.571429
               5.0      0.714286
               7.0      1.714286
               8.0      1.000000
               10.0     0.428571
job4_category  3.0      0.166667
               4.0      1.500000
               5.0      0.500000
               7.0      0.333333
               8.0      1.000000
               10.0     0.333333
Name: pct, dtype: float64
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2楼 · 编辑于 2024-09-24 06:35:15

如果column中的一个或多个条目是nan，则sum(column)将返回nan。要避免此问题，请改用NumPy的np.sum()。如果原始数据中缺少的值被编码为np.nan，这将按预期工作：

import numpy as np
import pandas as pd

data = "{'job1_category': {0: nan, 1: 4.0, 2: 5.0, 3: 5.0, 4: 4.0, 5: 4.0, 6: 5.0, 7: 4.0, 8: 4.0, 9: 4.0, 10: 4.0, 11: 4.0, 12: 4.0, 13: 4.0, 14: nan, 15: 4.0, 16: 3.0, 17: 7.0, 18: 4.0, 19: 4.0, 20: nan, 21: nan, 22: 4.0, 23: 1.0, 24: 4.0, 25: 1.0, 26: 4.0, 27: 2.0, 28: 5.0, 29: 3.0, 30: 4.0, 31: 5.0, 32: 4.0, 33: 4.0, 34: 4.0, 35: 4.0, 36: 3.0, 37: 4.0, 38: 4.0, 39: 4.0, 40: 9.0, 41: 4.0, 42: 4.0, 43: 3.0, 44: 4.0, 45: 9.0, 46: 10.0, 47: nan, 48: 10.0, 49: 4.0, 50: 8.0, 51: nan, 52: 5.0, 53: 8.0, 54: 4.0, 55: nan, 56: 4.0, 57: 8.0, 58: 4.0, 59: 4.0, 60: 4.0, 61: 4.0, 62: 8.0, 63: 4.0, 64: 8.0, 65: 7.0, 66: 4.0, 67: 7.0, 68: 8.0, 69: 7.0, 70: nan, 71: 7.0, 72: nan, 73: 10.0, 74: 7.0, 75: 6.0, 76: 7.0, 77: 4.0, 78: 7.0, 79: 7.0, 80: 7.0, 81: 7.0, 82: 4.0, 83: nan, 84: 4.0, 85: nan, 86: 4.0, 87: 9.0, 88: 4.0, 89: 4.0, 90: 4.0, 91: 4.0, 92: 4.0, 93: 7.0, 94: 2.0, 95: 4.0, 96: 4.0, 97: nan, 98: 1.0, 99: 9.0}, 'job2_category': {0: 6.0, 1: 5.0, 2: 4.0, 3: 5.0, 4: 6.0, 5: 4.0, 6: 5.0, 7: 5.0, 8: 2.0, 9: 4.0, 10: 4.0, 11: nan, 12: 5.0, 13: 4.0, 14: nan, 15: 1.0, 16: 4.0, 17: 4.0, 18: 4.0, 19: 4.0, 20: 4.0, 21: 3.0, 22: 4.0, 23: 4.0, 24: 4.0, 25: 4.0, 26: 8.0, 27: 6.0, 28: 6.0, 29: 3.0, 30: 4.0, 31: 5.0, 32: 7.0, 33: 4.0, 34: 4.0, 35: 4.0, 36: 4.0, 37: 4.0, 38: 4.0, 39: 4.0, 40: 4.0, 41: 5.0, 42: 5.0, 43: 4.0, 44: 4.0, 45: 1.0, 46: 10.0, 47: 10.0, 48: 1.0, 49: 4.0, 50: 4.0, 51: nan, 52: 7.0, 53: 4.0, 54: 4.0, 55: 9.0, 56: 9.0, 57: 4.0, 58: 3.0, 59: 4.0, 60: 4.0, 61: nan, 62: 7.0, 63: nan, 64: 7.0, 65: 7.0, 66: 7.0, 67: 7.0, 68: 8.0, 69: 7.0, 70: 7.0, 71: 7.0, 72: nan, 73: 10.0, 74: 8.0, 75: 7.0, 76: 8.0, 77: 5.0, 78: nan, 79: 7.0, 80: nan, 81: 4.0, 82: 7.0, 83: nan, 84: 4.0, 85: nan, 86: 4.0, 87: 4.0, 88: 4.0, 89: 4.0, 90: 10.0, 91: 5.0, 92: nan, 93: 7.0, 94: 4.0, 95: 4.0, 96: 4.0, 97: nan, 98: 1.0, 99: 9.0}, 'job3_category': {0: 5.0, 1: 5.0, 2: 4.0, 3: nan, 4: 10.0, 5: nan, 6: nan, 7: nan, 8: nan, 9: nan, 10: 4.0, 11: 4.0, 12: 5.0, 13: nan, 14: 4.0, 15: 1.0, 16: 4.0, 17: 4.0, 18: 4.0, 19: 4.0, 20: nan, 21: nan, 22: nan, 23: nan, 24: nan, 25: 1.0, 26: 4.0, 27: nan, 28: nan, 29: 3.0, 30: 4.0, 31: 3.0, 32: 4.0, 33: nan, 34: nan, 35: nan, 36: 4.0, 37: nan, 38: nan, 39: 4.0, 40: 7.0, 41: 5.0, 42: nan, 43: 4.0, 44: 4.0, 45: 1.0, 46: 7.0, 47: 1.0, 48: 7.0, 49: 7.0, 50: 4.0, 51: 8.0, 52: nan, 53: 4.0, 54: 7.0, 55: nan, 56: 8.0, 57: nan, 58: nan, 59: 3.0, 60: nan, 61: nan, 62: nan, 63: nan, 64: nan, 65: 7.0, 66: 7.0, 67: 7.0, 68: 7.0, 69: 8.0, 70: 7.0, 71: 4.0, 72: 8.0, 73: nan, 74: 3.0, 75: 10.0, 76: 4.0, 77: 8.0, 78: 8.0, 79: nan, 80: nan, 81: 4.0, 82: 7.0, 83: 4.0, 84: nan, 85: 8.0, 86: 4.0, 87: 4.0, 88: 4.0, 89: nan, 90: 5.0, 91: 7.0, 92: 4.0, 93: nan, 94: 1.0, 95: nan, 96: nan, 97: 10.0, 98: 1.0, 99: 1.0}, 'job4_category': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan, 5: nan, 6: nan, 7: nan, 8: nan, 9: nan, 10: 4.0, 11: nan, 12: nan, 13: nan, 14: 4.0, 15: 8.0, 16: nan, 17: 4.0, 18: 3.0, 19: nan, 20: nan, 21: nan, 22: nan, 23: nan, 24: nan, 25: nan, 26: nan, 27: nan, 28: nan, 29: nan, 30: nan, 31: nan, 32: 4.0, 33: nan, 34: nan, 35: nan, 36: 4.0, 37: nan, 38: nan, 39: nan, 40: 4.0, 41: nan, 42: nan, 43: nan, 44: nan, 45: nan, 46: nan, 47: 7.0, 48: nan, 49: nan, 50: 5.0, 51: 8.0, 52: nan, 53: 4.0, 54: nan, 55: nan, 56: 8.0, 57: nan, 58: nan, 59: nan, 60: nan, 61: nan, 62: nan, 63: nan, 64: nan, 65: 8.0, 66: 7.0, 67: nan, 68: nan, 69: nan, 70: 8.0, 71: 5.0, 72: nan, 73: nan, 74: nan, 75: 10.0, 76: nan, 77: 4.0, 78: nan, 79: nan, 80: nan, 81: nan, 82: nan, 83: nan, 84: nan, 85: nan, 86: 10.0, 87: nan, 88: 8.0, 89: nan, 90: nan, 91: nan, 92: nan, 93: nan, 94: 4.0, 95: nan, 96: nan, 97: nan, 98: nan, 99: 5.0}}"
data = eval(data.replace('nan', 'np.nan'))
data_rel1 = pd.DataFrame(data)

job1_freq = data_rel1.groupby('job1_category').size()
job2_freq = data_rel1.groupby('job2_category').size()
job3_freq = data_rel1.groupby('job3_category').size()
job4_freq = data_rel1.groupby('job4_category').size()

data_freq = pd.concat([job1_freq, job2_freq, job3_freq, job4_freq], axis=1)

data_freq.columns = [1,2,3,4]

data_freq["prob_1"] = data_freq[1] / np.sum(data_freq[1])
data_freq["prob_2"] = data_freq[2] / np.sum(data_freq[2])
data_freq["prob_3"] = data_freq[3] / np.sum(data_freq[3])
data_freq["prob_4"] = data_freq[4] / np.sum(data_freq[4])

data_freq

        1   2   3       4       prob_1      prob_2      prob_3      prob_4
1.0     3   4   7.0     NaN     0.034091    0.045455    0.111111    NaN
2.0     2   1   NaN     NaN     0.022727    0.011364    NaN         NaN
3.0     4   3   4.0     1.0     0.045455    0.034091    0.063492    0.043478
4.0     47  41  25.0    9.0     0.534091    0.465909    0.396825    0.391304
5.0     6   10  5.0     3.0     0.068182    0.113636    0.079365    0.130435
6.0     1   4   NaN     NaN     0.011364    0.045455    NaN         NaN
7.0     12  14  12.0    2.0     0.136364    0.159091    0.190476    0.086957
8.0     6   4   7.0     6.0     0.068182    0.045455    0.111111    0.260870
9.0     4   3   NaN     NaN     0.045455    0.034091    NaN         NaN
10.0    3   4   3.0     2.0     0.034091    0.045455    0.047619    0.086957
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3楼 · 编辑于 2024-09-24 06:35:15

尝试用0.0替换空值，然后尝试您的代码

data_freq.fillna(0.0)

我觉得这也不会影响你的计算。作为(0.0)/sum([.....])=0.0

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