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堆积条形图 X 轴给出错误顺序 python plotly

[英]Stacked bar chart X axis gives wrong order python plotly

 原文  2021-02-12 04:50:09  3   2    python/ pandas/ dataframe/ plotly/ plotly-python

问题描述

Hi created a stack bar chart using python plotly.您好使用 python plotly 创建了一个堆栈条形图。 But gives the wrong X-axis order.但给出了错误的 X 轴顺序。

DF:东风：

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

In df Day-Shift represent shift and Date, it goes Day 01-05, Night 01-05, Day 01-06, Night 01-06, and so on.在 df Day-Shift 中表示班次和日期，它是 Day 01-05、Night 01-05、Day 01-06、Night 01-06，依此类推。 But in the graph, gives the wrong order on X-axis.但在图中，在 X 轴上给出了错误的顺序。 Ex: After the Day 01-05 graph shows Night 01-08 instead of Night 01-05.例如：Day 01-05 之后图表显示 Night 01-08 而不是 Night 01-05。

在此处输入图像描述

Sample df and my code attached below:示例 df 和我的代码附在下面：

import plotly.express as px
fig = px.bar(df, x="Day-Shift", y="seconds", color="State")
fig.show()

Df ad Dict: Df ad 字典：

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}})

Really appreciate your support !!!真的很感谢你的支持！！！

2 个解决方案

解决方案1
 2  2021-02-12 09:32:53

You can use category_orders to set the order of values:您可以使用category_orders设置值的顺序：

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()

Output: Output： 在此处输入图像描述

解决方案2
 2 已采纳  2021-02-12 10:31:14

Setting category_orders = {"Day-Shift":df['Day-Shift'].unique()} will work, but only reliably if your dataset has the correct order to begin with.设置category_orders = {"Day-Shift":df['Day-Shift'].unique()}将起作用，但只有在您的数据集具有正确的开头顺序时才可靠。 Another condition is that you only have data for one unique year.另一个条件是您只有一个唯一年份的数据。 In order to guarantee the correct order regardless of original order, and to make it possible to have data for december 2020 combinde with january 2021 I would suggest you to:为了保证正确的订单，无论原始订单如何，并且可以将december 2020的数据与january 2021年 1 月的数据结合起来，我建议您：

  1. split "Day-Shift" into two separate columns;将"Day-Shift"拆分为两个单独的列； time of day == tod and day of month = date , time of day == tod和day of month = date ，
  2. append year to youe dates, like dfs['date2'] = dfs['date'] + '-2021' , append year到你的日期，如dfs['date2'] = dfs['date'] + '-2021' ，
  3. turn 'date2' into datetime using dfs['date2'] = pd.to_datetime(dfs['date2']) ,使用dfs['date2'] = pd.to_datetime(dfs['date2'])将'date2'转换为日期时间，
  4. sort your values chronologically, and按时间顺序对您的值进行排序，并且
  5. retrive "Day-Shift" in the now correct order with new_order = list(df['Day-Shift'].unique()) , and then使用new_order = list(df['Day-Shift'].unique())以现在正确的顺序检索"Day-Shift" ，然后
  6. apply the chronologially correct order through category_orders = {'Day-Shift': new_order}通过category_orders = {'Day-Shift': new_order}应用按时间顺序正确的顺序

Plot Plot

在此处输入图像描述

Complete code: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()

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