带有标记目标的线的动态直方图子图

Question

我一直在尝试发布一些类似的解决方案，但运气不好。

我正在尝试获取制造过程中所有 Step No 的 Cost 的直方图。每个部分都有不同数量的步骤，所以我想为每个部分在一个 plot/image 上有一组直方图。

在我的真实数据中有很多部分，所以如果这可以遍历许多部分并保存图表，那将是理想的。

此外，我们有一个目标成本，我想覆盖在直方图上的每个步骤。这在一个单独的数据框中表示。我卡在了子图的循环中，所以我还没有尝试这个。

下面是我能找到的每一步直方图应该是什么样子的最接近的内容：

到目前为止，这是我的代码：

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_excel('Dist_Example.xlsx')
df1 = df[~df['Cost Type'].isin(['Material'])]
number_of_subplots = len(df1['Step No'].unique())
steps = df1['Step No'].unique()
fig, axs = plt.subplots(1, number_of_subplots, sharey = True, tight_layout=True)
for step in steps:
    df2 = df1[df1['Step No'].isin([step])]
    axs[step].hist(df2['Cost'])
plt.show()

在此先感谢您对我的帮助！

这里是 Target Cost 我想在直方图上显示为垂直线：

PartNo  StepNo  TargetCost
ABC     10      12
ABC     20      20
ABC     30     13

下面是一些样本历史数据，它们应该在直方图中的 bin 中：

PartNo  SerialNo    StepNo  CostType    Cost
ABC      123        10      Labor       11
ABC      123        10      Material    16
ABC      456        10      Labor       21
ABC      456        10      Material    26
ABC      789        10      Labor       21
ABC      789        10      Material    16
ABC      1011       10      Labor       11
ABC      1011       10      Material    6
ABC      1112       10      Labor       1
ABC      1112       10      Material    -4
ABC      123        20      Labor       11
ABC      123        20      Material    19
ABC      456        20      Labor       24
ABC      456        20      Material    29
ABC      789        20      Labor       24
ABC      789        20      Material    19
ABC      1011       20      Labor       14
ABC      1011       20      Material    9
ABC      1112       20      Labor       4
ABC      1112       20      Material    -1
ABC      123        30      Labor       11
ABC      123        30      Material    13
ABC      456        30      Labor       18
ABC      456        30      Material    23
ABC      789        30      Labor       18
ABC      789        30      Material    13
ABC      1011       30      Labor       8
ABC      1011       30      Material    3
ABC      1112       30      Labor       -2
ABC      1112       30      Material    -7

第二个样本数据集：

PartNo  SerialNo    StepNo  CostType    Cost
DEF     Aplha       10  Labor   2
DEF     Zed         10  Labor   3
DEF     Kelly       10  Labor   4
DEF     Aplha       20  Labor   3
DEF     Zed         20  Labor   2
DEF     Kelly       20  Labor   5
DEF     Aplha       30  Labor   6
DEF     Zed         30  Labor   7
DEF     Kelly       30  Labor   5
DEF     Aplha       40  Labor   3
DEF     Zed         40  Labor   4
DEF     Kelly       40  Labor   2
DEF     Aplha       50  Labor   8
DEF     Zed         50  Labor   9
DEF     Kelly       50  Labor   7

Answer 1

您找不到可以直接为您的数据集解决此问题的直方图函数。您需要以适合您需要的方式聚合数据，然后用条形图表示您的发现。

我发现您的 objective 和数据有点令人困惑，但我想我已经根据这些假设弄清楚了您的目标：

您想汇总每个 StepNo 的成本
成本类型无关
必须计算 Target 总成本，因为您要汇总每个 StepNo 中的所有成本。

剧情

编辑

这不是 OP 想要的。经过一番反复之后，我们找到了一个似乎可行的解决方案

(from the question) I am trying to get histograms for Cost for all the Step No

(from a comment) I actually want to have a historgram for the sum of the cost per serial no in each step.

由于您必须在直方图中的 y 轴上具有 count 或频率，因此您必须以某种有意义的方式聚合数据。下面您将看到每个 SerialNO 在每个步骤的总成本的选择箱数的计数。

结果：

代码：

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
import pylab


# Load data in two steps:
# df1 = pd.read_clipboard(sep='\s+')
# Part No Serial No   Step No Cost Type   Cost
# ABC      123        10      Labor       11
# ABC      123        10      Material    16
# ABC      456        10      Labor       21
# ABC      456        10      Material    26
# ...

# df2 = pd.read_clipboard(sep='\s+')
# Part No Step No Target Cost
# ABC     10      12
# ABC     20      20
# ABC     30     13

# Cost type and SerialNo irrelevant
df11 = df1.drop(['CostType'] , axis = 1)

# Aggregate by StepNo, find total cost and count
##df12 = df11.groupby(['PartNo', 'StepNo']).agg(['sum', 'count']).reset_index()
df12 = df11.groupby(['PartNo', 'StepNo', 'SerialNo']).agg(['sum', 'count']).reset_index()

df12.columns = ['PartNo', 'StepNo', 'SerialNo', 'Cost', 'Count']
df3 = pd.merge(df2, df12, how = 'left', on = ['PartNo', 'StepNo'])

# Calculate total target cost
df3['TargetTotal'] = df3['TargetCost']*df3['Count']

# pylab.rcParams['figure.figsize'] = (2, 1)

def multiHist(x_data, x_label, bins):

    # Hisrogram setup
    fig, ax = plt.subplots()
    ax.hist(x_data, bins=bins, color='blue', alpha=0.5, histtype='stepfilled')

    # Horizontal line
    x0 = dfs['TargetTotal'].iloc[0]
    ax.axvline(x0, color='red', linewidth=2)

    # Annotation
    ax.annotate('Target: {:0.2f}'.format(x0), xy=(x0, 1), xytext=(-15, 15),
            xycoords=('data', 'axes fraction'), textcoords='offset points',
            horizontalalignment='left', verticalalignment='center',
            arrowprops=dict(arrowstyle='-|>', fc='white', shrinkA=0, shrinkB=0,
                            connectionstyle='angle,angleA=0,angleB=90,rad=10'),)

    # Labels
    ax.set_xlabel(x_label, color = 'grey')
    ax.legend(loc='upper left')
    plt.show()

# Identify and plot  data for each StepNo
for step in df3['StepNo'].unique():
    dfs = df3[df3['StepNo']==step]

    # Data to plot
    cost = dfs['Cost']
    labels = 'Part: ' + dfs['PartNo'].iloc[0] + ', ' 'Step:' + str(dfs['StepNo'].iloc[0])

    # Plot
    multiHist(x_data = cost, x_label = labels, bins = 4)

带有标记目标的线的动态直方图子图

Dynamic histogram subplots with line to mark target

matplotlib

python-3.x

pandas

subplot