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堆积条形图 X 轴顺序错误 Python Plotly

pythonpandasdataframeplotlyplotly-python
3

你好，我使用Python的Plotly创建了一个堆叠条形图。但是X轴的顺序不正确。

DF:

Day-Shift   State          seconds
Day 01-05   A              7439
Day 01-05   STOPPED        0
Day 01-05   B              10
Day 01-05   C              35751
Night 01-05 C              43200
Day 01-06   STOPPED        7198
Day 01-06   F              18
Day 01-06   A              14
Day 01-06   A              29301
Day 01-06   STOPPED        6
Day 01-06   A              6663
Night 01-06 A              43200

在 df 中，Day-Shift 表示班次和日期，它的顺序是 Day 01-05、Night 01-05、Day 01-06、Night 01-06 等等。但在图表中，X 轴的顺序却错误了。例如，在 Day 01-05 后，图表显示 Night 01-08 而不是 Night 01-05。
以下附有示例 df 和我的代码：
import plotly.express as px
fig = px.bar(df, x="Day-Shift", y="seconds", color="State")
fig.show()

Df和Dict：

import pandas as pd
import plotly.express as px


df = pd.DataFrame({'Day-Shift': {0: 'Day 01-05',
  1: 'Day 01-05',
  2: 'Day 01-05',
  3: 'Day 01-05',
  4: 'Night 01-05',
  5: 'Day 01-06',
  6: 'Day 01-06',
  7: 'Day 01-06',
  8: 'Day 01-06',
  9: 'Day 01-06',
  10: 'Day 01-06',
  11: 'Night 01-06',
  12: 'Day 01-07',
  13: 'Night 01-07',
  14: 'Night 01-07',
  15: 'Night 01-07',
  16: 'Night 01-07',
  17: 'Night 01-07',
  18: 'Night 01-08',
  19: 'Night 01-08',
  20: 'Night 01-08',
  21: 'Night 01-08',
  22: 'Day 01-08',
  23: 'Day 01-08',
  24: 'Day 01-08',
  25: 'Night 01-09',
  26: 'Night 01-09',
  27: 'Night 01-09',
  28: 'Day 01-09',
  29: 'Day 01-09',
  30: 'Day 01-09',
  31: 'Day 01-09',
  32: 'Day 01-10',
  33: 'Night 01-10',
  34: 'Day 01-11',
  35: 'Day 01-11',
  36: 'Day 01-11',
  37: 'Day 01-11',
  38: 'Day 01-11',
  39: 'Night 01-11',
  40: 'Day 01-12',
  41: 'Night 01-12',
  42: 'Day 01-13',
  43: 'Day 01-13',
  44: 'Day 01-13',
  45: 'Day 01-13',
  46: 'Day 01-13',
  47: 'Day 01-13',
  48: 'Day 01-13',
  49: 'Night 01-13',
  50: 'Day 01-14',
  51: 'Day 01-14',
  52: 'Day 01-14',
  53: 'Day 01-14',
  54: 'Day 01-14',
  55: 'Day 01-14',
  56: 'Day 01-14',
  57: 'Day 01-14',
  58: 'Day 01-14',
  59: 'Night 01-14'},
 'State': {0: 'D',
  1: 'STOPPED',
  2: 'B',
  3: 'A',
  4: 'A',
  5: 'A',
  6: 'A1',
  7: 'A2',
  8: 'A3',
  9: 'A4',
  10: 'B1',
  11: 'B1',
  12: 'B1',
  13: 'B1',
  14: 'B2',
  15: 'STOPPED',
  16: 'RUNNING',
  17: 'B',
  18: 'STOPPED',
  19: 'B',
  20: 'RUNNING',
  21: 'D',
  22: 'STOPPED',
  23: 'B',
  24: 'RUNNING',
  25: 'STOPPED',
  26: 'RUNNING',
  27: 'B',
  28: 'RUNNING',
  29: 'STOPPED',
  30: 'B',
  31: 'D',
  32: 'B',
  33: 'B',
  34: 'B',
  35: 'RUNNING',
  36: 'STOPPED',
  37: 'D',
  38: 'A',
  39: 'A',
  40: 'A',
  41: 'A',
  42: 'A',
  43: 'A1',
  44: 'A2',
  45: 'A3',
  46: 'A4',
  47: 'B1',
  48: 'B2',
  49: 'B2',
  50: 'B2',
  51: 'B',
  52: 'STOPPED',
  53: 'A',
  54: 'A1',
  55: 'A2',
  56: 'A3',
  57: 'A4',
  58: 'B1',
  59: 'B1'},
 'seconds': {0: 7439,
  1: 0,
  2: 10,
  3: 35751,
  4: 43200,
  5: 7198,
  6: 18,
  7: 14,
  8: 29301,
  9: 6,
  10: 6663,
  11: 43200,
  12: 43200,
  13: 5339,
  14: 8217,
  15: 0,
  16: 4147,
  17: 1040,
  18: 24787,
  19: 1500,
  20: 14966,
  21: 1410,
  22: 2499,
  23: 1310,
  24: 39391,
  25: 3570,
  26: 17234,
  27: 47390,
  28: 36068,
  29: 270,
  30: 6842,
  31: 20,
  32: 43200,
  33: 43200,
  34: 2486,
  35: 8420,
  36: 870,
  37: 30,
  38: 31394,
  39: 43200,
  40: 43200,
  41: 43200,
  42: 36733,
  43: 23,
  44: 6,
  45: 4,
  46: 4,
  47: 3,
  48: 6427,
  49: 43200,
  50: 620,
  51: 0,
  52: 4,
  53: 41336,
  54: 4,
  55: 4,
  56: 4,
  57: 23,
  58: 1205,
  59: 43200}})

非常感谢您的支持！！！
1
目前无法测试Plotly，但可以尝试将值分配为分类以保留原始列的顺序，然后进行绘图。df['Day-Shift'] = pd.Categorical(df['Day-Shift'], df['Day-Shift'].unique(), ordered=True) - anky
非常感谢你的回答，但是它没有改变图表。 - johnson
@domahc 你能具体说明这个问题与这个问题不同吗？ - vestland
@vestland，你提到的问题是我问的，即我的代码出现了一个意外的图形。在这里，我正确地进行了可视化（使用了你对之前问题的建议），但是X轴的顺序与原始数据框不同。 - johnson
@domahc 没问题！这就是正确的做法；一个问题对应一个回答，然后可以继续提出后续问题。你是否考虑将我在你之前的问题中的建议标记为被采纳的答案？ - vestland
显示剩余2条评论
2个回答
4
您可以使用category_orders来设置数值的顺序：
import pandas as pd 
import plotly.express as px 
df = pd.DataFrame({'Day-Shift': {0: 'Day 01-05', 1: 'Day 01-05', 2: 'Day 01-05', 3: 'Day 01-05', 4: 'Night 01-05', 5: 'Day 01-06', 6: 'Day 01-06', 7: 'Day 01-06', 8: 'Day 01-06', 9: 'Day 01-06', 10: 'Day 01-06', 11: 'Night 01-06', 12: 'Day 01-07', 13: 'Night 01-07', 14: 'Night 01-07', 15: 'Night 01-07', 16: 'Night 01-07', 17: 'Night 01-07', 18: 'Night 01-08', 19: 'Night 01-08', 20: 'Night 01-08', 21: 'Night 01-08', 22: 'Day 01-08', 23: 'Day 01-08', 24: 'Day 01-08', 25: 'Night 01-09', 26: 'Night 01-09', 27: 'Night 01-09', 28: 'Day 01-09', 29: 'Day 01-09', 30: 'Day 01-09', 31: 'Day 01-09', 32: 'Day 01-10', 33: 'Night 01-10', 34: 'Day 01-11', 35: 'Day 01-11', 36: 'Day 01-11', 37: 'Day 01-11', 38: 'Day 01-11', 39: 'Night 01-11', 40: 'Day 01-12', 41: 'Night 01-12', 42: 'Day 01-13', 43: 'Day 01-13', 44: 'Day 01-13', 45: 'Day 01-13', 46: 'Day 01-13', 47: 'Day 01-13', 48: 'Day 01-13', 49: 'Night 01-13', 50: 'Day 01-14', 51: 'Day 01-14', 52: 'Day 01-14', 53: 'Day 01-14', 54: 'Day 01-14', 55: 'Day 01-14', 56: 'Day 01-14', 57: 'Day 01-14', 58: 'Day 01-14', 59: 'Night 01-14'}, 'State': {0: 'D', 1: 'STOPPED', 2: 'B', 3: 'A', 4: 'A', 5: 'A', 6: 'A1', 7: 'A2', 8: 'A3', 9: 'A4', 10: 'B1', 11: 'B1', 12: 'B1', 13: 'B1', 14: 'B2', 15: 'STOPPED', 16: 'RUNNING', 17: 'B', 18: 'STOPPED', 19: 'B', 20: 'RUNNING', 21: 'D', 22: 'STOPPED', 23: 'B', 24: 'RUNNING', 25: 'STOPPED', 26: 'RUNNING', 27: 'B', 28: 'RUNNING', 29: 'STOPPED', 30: 'B', 31: 'D', 32: 'B', 33: 'B', 34: 'B', 35: 'RUNNING', 36: 'STOPPED', 37: 'D', 38: 'A', 39: 'A', 40: 'A', 41: 'A', 42: 'A', 43: 'A1', 44: 'A2', 45: 'A3', 46: 'A4', 47: 'B1', 48: 'B2', 49: 'B2', 50: 'B2', 51: 'B', 52: 'STOPPED', 53: 'A', 54: 'A1', 55: 'A2', 56: 'A3', 57: 'A4', 58: 'B1', 59: 'B1'}, 'seconds': {0: 7439, 1: 0, 2: 10, 3: 35751, 4: 43200, 5: 7198, 6: 18, 7: 14, 8: 29301, 9: 6, 10: 6663, 11: 43200, 12: 43200, 13: 5339, 14: 8217, 15: 0, 16: 4147, 17: 1040, 18: 24787, 19: 1500, 20: 14966, 21: 1410, 22: 2499, 23: 1310, 24: 39391, 25: 3570, 26: 17234, 27: 47390, 28: 36068, 29: 270, 30: 6842, 31: 20, 32: 43200, 33: 43200, 34: 2486, 35: 8420, 36: 870, 37: 30, 38: 31394, 39: 43200, 40: 43200, 41: 43200, 42: 36733, 43: 23, 44: 6, 45: 4, 46: 4, 47: 3, 48: 6427, 49: 43200, 50: 620, 51: 0, 52: 4, 53: 41336, 54: 4, 55: 4, 56: 4, 57: 23, 58: 1205, 59: 43200}})

fig = px.bar(df, x="Day-Shift", y="seconds", category_orders={'Day-Shift': df['Day-Shift'].to_list()},color="State")
fig.show()

输出： 在此输入图片描述

有没有可能在堆叠柱状图中按照相同的数据顺序显示？例如：对于01-09日，原始数据顺序是Running（36068秒）和状态B（6842秒），但在堆叠柱状图中，第一个是状态B，然后是Running状态。我们能否在堆叠柱状图中按照相同的数据顺序显示？ - johnson
2

设置 category_orders = {"Day-Shift":df['Day-Shift'].unique()} 可以工作，但前提是您的数据集一开始就有正确的顺序。另一个条件是您只有一个唯一年份的数据。为了保证正确的顺序，无论原始顺序如何，并使其可能将 december 2020 的数据与 january 2021 组合起来，我建议您：

  1. 将 "Day-Shift" 拆分为两个单独的列；time of day == tod 和 day of month = date，
  2. 在日期后添加 year，例如 dfs['date2'] = dfs['date'] + '-2021'，
  3. 使用 dfs['date2'] = pd.to_datetime(dfs['date2']) 将 'date2' 转换为日期时间格式，
  4. 按时间顺序排序，
  5. 使用 new_order = list(df['Day-Shift'].unique()) 检索现在正确的顺序中的 "Day-Shift"，然后
  6. 通过 category_orders = {'Day-Shift': new_order} 应用正确的时间顺序

图表

enter image description here

完整代码：

import pandas as pd
import plotly.express as px

df = pd.DataFrame({'Day-Shift': {0: 'Day 01-05',
  1: 'Day 01-05',
  2: 'Day 01-05',
  3: 'Day 01-05',
  4: 'Night 01-05',
  5: 'Day 01-06',
  6: 'Day 01-06',
  7: 'Day 01-06',
  8: 'Day 01-06',
  9: 'Day 01-06',
  10: 'Day 01-06',
  11: 'Night 01-06',
  12: 'Day 01-07',
  13: 'Night 01-07',
  14: 'Night 01-07',
  15: 'Night 01-07',
  16: 'Night 01-07',
  17: 'Night 01-07',
  18: 'Night 01-08',
  19: 'Night 01-08',
  20: 'Night 01-08',
  21: 'Night 01-08',
  22: 'Day 01-08',
  23: 'Day 01-08',
  24: 'Day 01-08',
  25: 'Night 01-09',
  26: 'Night 01-09',
  27: 'Night 01-09',
  28: 'Day 01-09',
  29: 'Day 01-09',
  30: 'Day 01-09',
  31: 'Day 01-09',
  32: 'Day 01-10',
  33: 'Night 01-10',
  34: 'Day 01-11',
  35: 'Day 01-11',
  36: 'Day 01-11',
  37: 'Day 01-11',
  38: 'Day 01-11',
  39: 'Night 01-11',
  40: 'Day 01-12',
  41: 'Night 01-12',
  42: 'Day 01-13',
  43: 'Day 01-13',
  44: 'Day 01-13',
  45: 'Day 01-13',
  46: 'Day 01-13',
  47: 'Day 01-13',
  48: 'Day 01-13',
  49: 'Night 01-13',
  50: 'Day 01-14',
  51: 'Day 01-14',
  52: 'Day 01-14',
  53: 'Day 01-14',
  54: 'Day 01-14',
  55: 'Day 01-14',
  56: 'Day 01-14',
  57: 'Day 01-14',
  58: 'Day 01-14',
  59: 'Night 01-14'},
 'State': {0: 'D',
  1: 'STOPPED',
  2: 'B',
  3: 'A',
  4: 'A',
  5: 'A',
  6: 'A1',
  7: 'A2',
  8: 'A3',
  9: 'A4',
  10: 'B1',
  11: 'B1',
  12: 'B1',
  13: 'B1',
  14: 'B2',
  15: 'STOPPED',
  16: 'RUNNING',
  17: 'B',
  18: 'STOPPED',
  19: 'B',
  20: 'RUNNING',
  21: 'D',
  22: 'STOPPED',
  23: 'B',
  24: 'RUNNING',
  25: 'STOPPED',
  26: 'RUNNING',
  27: 'B',
  28: 'RUNNING',
  29: 'STOPPED',
  30: 'B',
  31: 'D',
  32: 'B',
  33: 'B',
  34: 'B',
  35: 'RUNNING',
  36: 'STOPPED',
  37: 'D',
  38: 'A',
  39: 'A',
  40: 'A',
  41: 'A',
  42: 'A',
  43: 'A1',
  44: 'A2',
  45: 'A3',
  46: 'A4',
  47: 'B1',
  48: 'B2',
  49: 'B2',
  50: 'B2',
  51: 'B',
  52: 'STOPPED',
  53: 'A',
  54: 'A1',
  55: 'A2',
  56: 'A3',
  57: 'A4',
  58: 'B1',
  59: 'B1'},
 'seconds': {0: 7439,
  1: 0,
  2: 10,
  3: 35751,
  4: 43200,
  5: 7198,
  6: 18,
  7: 14,
  8: 29301,
  9: 6,
  10: 6663,
  11: 43200,
  12: 43200,
  13: 5339,
  14: 8217,
  15: 0,
  16: 4147,
  17: 1040,
  18: 24787,
  19: 1500,
  20: 14966,
  21: 1410,
  22: 2499,
  23: 1310,
  24: 39391,
  25: 3570,
  26: 17234,
  27: 47390,
  28: 36068,
  29: 270,
  30: 6842,
  31: 20,
  32: 43200,
  33: 43200,
  34: 2486,
  35: 8420,
  36: 870,
  37: 30,
  38: 31394,
  39: 43200,
  40: 43200,
  41: 43200,
  42: 36733,
  43: 23,
  44: 6,
  45: 4,
  46: 4,
  47: 3,
  48: 6427,
  49: 43200,
  50: 620,
  51: 0,
  52: 4,
  53: 41336,
  54: 4,
  55: 4,
  56: 4,
  57: 23,
  58: 1205,
  59: 43200}})

dfs = df['Day-Shift'].str.extract('([a-zA-Z]+)([^a-zA-Z]+)', expand=True)
dfs.columns = ['tod', 'date']
dfs['date2'] = dfs['date'] + '-2021'
dfs['date2'] = pd.to_datetime(dfs['date2'])

df = pd.concat([df, dfs], axis = 1)
df = df.sort_values(['date2', 'tod'], ascending = [True, True])

new_order = list(df['Day-Shift'].unique())
# df['Day-Shift'] = pd.Categorical(df['Day-Shift'], categories=new_order, ordered=True)

fig = px.bar(df, x="Day-Shift", y="seconds", color="State",
            category_orders = {'Day-Shift': new_order})
fig.update_xaxes(type='category')
fig.show()
这真的很棒，不知道我们能否在堆叠柱状图中按照相同的数据顺序排列呢？例如：对于01-09日，原始数据顺序是Running（36068秒）和状态B（6842秒），但在堆叠柱状图中，第一个是状态B，然后是Running状态。我们能否在堆叠柱状图中按照相同的数据顺序排列呢？ - johnson
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