如何删除 x 轴子图上的重复值和空值或未标记值
How to remove repeating and empty or unmarked values on subplot of x-axis
我正在开发一组图表来绘制一些 Pandas DataFrame 值。为此，我使用以下代码使用各种 pandas、numpy 和 matplotlib 模块和函数：
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.ticker as ticker
data = {'Name': ['immoControlCmd', 'BrkTerrMde', 'GlblClkYr', 'HsaStat', 'TesterPhysicalResGWM', 'FapLc','FirstRowBuckleDriver', 'GlblClkDay'],
'Value': [0, 5, 0, 4, 0, 1, 1, 1],
'Id_Par': [0, 0, 3, 3, 3, 3, 0, 0]
}
signals_df = pd.DataFrame(data)
def plot_signals(signals_df):
# Count signals by par
signals_df['Count'] = signals_df.groupby('Id_Par').cumcount().add(1).mask(signals_df['Id_Par'].eq(0), 0)
# Subtract Par values from the index column
signals_df['Sub'] = signals_df.index - signals_df['Count']
id_par_prev = signals_df['Id_Par'].unique()
id_par = np.delete(id_par_prev, 0)
signals_df['Prev'] = [1 if x in id_par else 0 for x in signals_df['Id_Par']]
signals_df['Final'] = signals_df['Prev'] + signals_df['Sub']
# signals_df['Finall'] = signals_df['Final'].unique()
# print(signals_df['Finall'])
# Convert and set Subtract to index
signals_df.set_index('Final', inplace=True)
# pos_x = len(signals_df.index.unique()) - 1
# print(pos_x)
# Get individual names and variables for the chart
names_list = [name for name in signals_df['Name'].unique()]
num_names_list = len(names_list)
num_axis_x = len(signals_df["Name"])
# Creation Graphics
fig, ax = plt.subplots(nrows=num_names_list, figsize=(10, 10), sharex=True)
plt.xticks(np.arange(0, num_axis_x), color='SteelBlue', fontweight='bold')
for pos, (a_, name) in enumerate(zip(ax, names_list)):
# Get data
data = signals_df[signals_df["Name"] == name]["Value"]
# Get values axis-x and axis-y
x_ = np.hstack([-1, data.index.values, len(signals_df) - 1])
# print(data.index.values)
y_ = np.hstack([0, data.values, data.iloc[-1]])
# Plotting the data by position
ax[pos].plot(x_, y_, drawstyle='steps-post', marker='*', markersize=8, color='k', linewidth=2)
ax[pos].set_ylabel(name, fontsize=8, fontweight='bold', color='SteelBlue', rotation=30, labelpad=35)
ax[pos].yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
ax[pos].yaxis.set_tick_params(labelsize=6)
ax[pos].grid(alpha=0.4, color='SteelBlue')
plt.show()
plot_signals(signals_df)
我想要的是去掉x轴上没有画的或者没有在图上标注的点或者位置，但是最后保留图片中的值和名称；从 Pandas 来看，这将是“最终”列，在绘制子图之前，将其分配为索引，这是该列中某些值重复的地方；将是从图中删除红色框内的值，但保留最后图像中的值和名称：
Name Value Id_Par Count Sub Prev
Final
0 immoControlCmd 0 0 0 0 0
1 BrkTerrMde 5 0 0 1 0
2 GlblClkYr 0 3 1 1 1
2 HsaStat 4 3 2 1 1
2 TesterPhysicalResGWM 0 3 3 1 1
2 FapLc 1 3 4 1 1
6 FirstRowBuckleDriver 1 0 0 6 0
7 GlblClkDay 1 0 0 7 0
我一直试图带上最后一列的唯一值，这将是 x 轴应该是的值，但由于数据框是另一个大小或维度，我得到一个错误：ValueError: Length of values (5) does not match length of index (8)，然后我必须调整图表的大小，但在这种情况下我不知道该怎么做：
signals_df['Final'] = signals_df['Prev'] + signals_df['Sub']
signals_df['Finall'] = signals_df['Final'].unique()
print(signals_df['Finall'])
我也试过带上唯一索引的大小，之前分配给变量 x_ 的 data.index.values 应用减法，但它没有带我想要的，因为它是收集所有值并批量减去它们而不是单独减去，就像 data.index.values:
signals_df.set_index('Final', inplace=True)
pos_x = len(signals_df.index.unique()) - 1
...
..
.
x_ = np.hstack([-1, data.index.values-pos-x, len(signals_df) - 1])
是否有 Pandas and/or Matplotlib 函数允许我使用？或者有人可以给我一个建议，帮助我更好地理解如何去做吗？我希望实现的是下面的情节:
非常感谢您的帮助，任何评论都有帮助。
我有 Python 版本：3.6.5，Pandas 版本：1.1.5 和 Matplotlib 版本：3.3.2
一种可能的方法是将 x 轴值转换为字符串，这意味着 matplotlib 将绘制“分类”图。 See examples of that here.
对于您的情况，因为您的子图可能具有不同的值，并且它们的顺序并不总是正确的，所以我们需要先做一些小技巧来确保刻度以正确的顺序出现。为此，我们可以使用 方法，他们绘制一些以正确顺序使用所有 x 值的东西，然后将其删除。
要将所有 xtick 值收集在一起，您可以这样做，在其中创建值列表，使用 set 将其缩减为唯一值，然后对这些值进行排序，然后转换使用列表理解和 str():
到字符串
# First make a list of all the xticks we want
xvals = [-1,]
for name in names_list:
xvals.append(signals_df[signals_df["Name"] == name]["Value"].index.values[0])
xvals.append(len(signals_df)-1)
# Reduce to only unique values, sorted, and then convert to strings
xvals = [str(i) for i in sorted(set(xvals))]
一旦你有了这些，你就可以制作一个虚拟图，然后将其删除，就像这样（这是为了以正确的顺序修复刻度位置）。 请注意，对于 matplotlib 版本 3.3.4 和更早版本，这需要在您的绘图循环中:
# To get the ticks in the right order on all subplots, we need to make
# a dummy plot here and then remove it
dummy, = ax[0].plot(xvals, np.zeros_like(xvals))
dummy.remove()
最后，当你在循环中实际绘制真实数据时，你只需要在绘制它们时将 x_ 转换为字符串：
ax[pos].plot(x_.astype('str'), y_, drawstyle='steps-post', marker='*', markersize=8, color='k', linewidth=2)
请注意，我所做的唯一其他更改是未明确设置 xtick 位置（您使用 plt.xticks 进行了设置），但您仍然可以使用该命令设置字体颜色和粗细
plt.xticks(color='SteelBlue', fontweight='bold')
这是输出：
为了完整起见，我已将所有内容放在您的脚本中：
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.ticker as ticker
import matplotlib
print(matplotlib.__version__)
data = {'Name': ['immoControlCmd', 'BrkTerrMde', 'GlblClkYr', 'HsaStat', 'TesterPhysicalResGWM', 'FapLc',
'FirstRowBuckleDriver', 'GlblClkDay'],
'Value': [0, 5, 0, 4, 0, 1, 1, 1],
'Id_Par': [0, 0, 3, 3, 3, 3, 0, 0]
}
signals_df = pd.DataFrame(data)
def plot_signals(signals_df):
# Count signals by par
signals_df['Count'] = signals_df.groupby('Id_Par').cumcount().add(1).mask(signals_df['Id_Par'].eq(0), 0)
# Subtract Par values from the index column
signals_df['Sub'] = signals_df.index - signals_df['Count']
id_par_prev = signals_df['Id_Par'].unique()
id_par = np.delete(id_par_prev, 0)
signals_df['Prev'] = [1 if x in id_par else 0 for x in signals_df['Id_Par']]
signals_df['Final'] = signals_df['Prev'] + signals_df['Sub']
# signals_df['Finall'] = signals_df['Final'].unique()
# print(signals_df['Finall'])
# Convert and set Subtract to index
signals_df.set_index('Final', inplace=True)
# pos_x = len(signals_df.index.unique()) - 1
# print(pos_x)
# Get individual names and variables for the chart
names_list = [name for name in signals_df['Name'].unique()]
num_names_list = len(names_list)
num_axis_x = len(signals_df["Name"])
# Creation Graphics
fig, ax = plt.subplots(nrows=num_names_list, figsize=(10, 10), sharex=True)
# No longer any need to define where the ticks go, but still set the colour and weight here
plt.xticks(color='SteelBlue', fontweight='bold')
# First make a list of all the xticks we want
xvals = [-1, ]
for name in names_list:
xvals.append(signals_df[signals_df["Name"] == name]["Value"].index.values[0])
xvals.append(len(signals_df) - 1)
# Reduce to only unique values, sorted, and then convert to strings
xvals = [str(i) for i in sorted(set(xvals))]
for pos, (a_, name) in enumerate(zip(ax, names_list)):
# To get the ticks in the right order on all subplots,
# we need to make a dummy plot here and then remove it
dummy, = ax[pos].plot(xvals, np.zeros_like(xvals))
dummy.remove()
# Get data
data = signals_df[signals_df["Name"] == name]["Value"]
# Get values axis-x and axis-y
x_ = np.hstack([-1, data.index.values, len(signals_df) - 1])
y_ = np.hstack([0, data.values, data.iloc[-1]])
# Plotting the data by position
# NOTE: here we convert x_ to strings as we plot, to make sure they are plotted as catagorical values
ax[pos].plot(x_.astype('str'), y_, drawstyle='steps-post', marker='*', markersize=8, color='k', linewidth=2)
ax[pos].set_ylabel(name, fontsize=8, fontweight='bold', color='SteelBlue', rotation=30, labelpad=35)
ax[pos].yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
ax[pos].yaxis.set_tick_params(labelsize=6)
ax[pos].grid(alpha=0.4, color='SteelBlue')
plt.show()
plot_signals(signals_df)
我正在开发一组图表来绘制一些 Pandas DataFrame 值。为此，我使用以下代码使用各种 pandas、numpy 和 matplotlib 模块和函数：
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.ticker as ticker
data = {'Name': ['immoControlCmd', 'BrkTerrMde', 'GlblClkYr', 'HsaStat', 'TesterPhysicalResGWM', 'FapLc','FirstRowBuckleDriver', 'GlblClkDay'],
'Value': [0, 5, 0, 4, 0, 1, 1, 1],
'Id_Par': [0, 0, 3, 3, 3, 3, 0, 0]
}
signals_df = pd.DataFrame(data)
def plot_signals(signals_df):
# Count signals by par
signals_df['Count'] = signals_df.groupby('Id_Par').cumcount().add(1).mask(signals_df['Id_Par'].eq(0), 0)
# Subtract Par values from the index column
signals_df['Sub'] = signals_df.index - signals_df['Count']
id_par_prev = signals_df['Id_Par'].unique()
id_par = np.delete(id_par_prev, 0)
signals_df['Prev'] = [1 if x in id_par else 0 for x in signals_df['Id_Par']]
signals_df['Final'] = signals_df['Prev'] + signals_df['Sub']
# signals_df['Finall'] = signals_df['Final'].unique()
# print(signals_df['Finall'])
# Convert and set Subtract to index
signals_df.set_index('Final', inplace=True)
# pos_x = len(signals_df.index.unique()) - 1
# print(pos_x)
# Get individual names and variables for the chart
names_list = [name for name in signals_df['Name'].unique()]
num_names_list = len(names_list)
num_axis_x = len(signals_df["Name"])
# Creation Graphics
fig, ax = plt.subplots(nrows=num_names_list, figsize=(10, 10), sharex=True)
plt.xticks(np.arange(0, num_axis_x), color='SteelBlue', fontweight='bold')
for pos, (a_, name) in enumerate(zip(ax, names_list)):
# Get data
data = signals_df[signals_df["Name"] == name]["Value"]
# Get values axis-x and axis-y
x_ = np.hstack([-1, data.index.values, len(signals_df) - 1])
# print(data.index.values)
y_ = np.hstack([0, data.values, data.iloc[-1]])
# Plotting the data by position
ax[pos].plot(x_, y_, drawstyle='steps-post', marker='*', markersize=8, color='k', linewidth=2)
ax[pos].set_ylabel(name, fontsize=8, fontweight='bold', color='SteelBlue', rotation=30, labelpad=35)
ax[pos].yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
ax[pos].yaxis.set_tick_params(labelsize=6)
ax[pos].grid(alpha=0.4, color='SteelBlue')
plt.show()
plot_signals(signals_df)
我想要的是去掉x轴上没有画的或者没有在图上标注的点或者位置，但是最后保留图片中的值和名称；从 Pandas 来看，这将是“最终”列，在绘制子图之前，将其分配为索引，这是该列中某些值重复的地方；将是从图中删除红色框内的值，但保留最后图像中的值和名称：
Name Value Id_Par Count Sub Prev
Final
0 immoControlCmd 0 0 0 0 0
1 BrkTerrMde 5 0 0 1 0
2 GlblClkYr 0 3 1 1 1
2 HsaStat 4 3 2 1 1
2 TesterPhysicalResGWM 0 3 3 1 1
2 FapLc 1 3 4 1 1
6 FirstRowBuckleDriver 1 0 0 6 0
7 GlblClkDay 1 0 0 7 0
我一直试图带上最后一列的唯一值，这将是 x 轴应该是的值，但由于数据框是另一个大小或维度，我得到一个错误：ValueError: Length of values (5) does not match length of index (8)，然后我必须调整图表的大小，但在这种情况下我不知道该怎么做：
signals_df['Final'] = signals_df['Prev'] + signals_df['Sub']
signals_df['Finall'] = signals_df['Final'].unique()
print(signals_df['Finall'])
我也试过带上唯一索引的大小，之前分配给变量 x_ 的 data.index.values 应用减法，但它没有带我想要的，因为它是收集所有值并批量减去它们而不是单独减去，就像 data.index.values:
signals_df.set_index('Final', inplace=True)
pos_x = len(signals_df.index.unique()) - 1
...
..
.
x_ = np.hstack([-1, data.index.values-pos-x, len(signals_df) - 1])
是否有 Pandas and/or Matplotlib 函数允许我使用？或者有人可以给我一个建议，帮助我更好地理解如何去做吗？我希望实现的是下面的情节:
非常感谢您的帮助，任何评论都有帮助。 我有 Python 版本：3.6.5，Pandas 版本：1.1.5 和 Matplotlib 版本：3.3.2
一种可能的方法是将 x 轴值转换为字符串，这意味着 matplotlib 将绘制“分类”图。 See examples of that here.
对于您的情况，因为您的子图可能具有不同的值，并且它们的顺序并不总是正确的，所以我们需要先做一些小技巧来确保刻度以正确的顺序出现。为此，我们可以使用
要将所有 xtick 值收集在一起，您可以这样做，在其中创建值列表，使用 set 将其缩减为唯一值，然后对这些值进行排序，然后转换使用列表理解和 str():
# First make a list of all the xticks we want
xvals = [-1,]
for name in names_list:
xvals.append(signals_df[signals_df["Name"] == name]["Value"].index.values[0])
xvals.append(len(signals_df)-1)
# Reduce to only unique values, sorted, and then convert to strings
xvals = [str(i) for i in sorted(set(xvals))]
一旦你有了这些，你就可以制作一个虚拟图，然后将其删除，就像这样（这是为了以正确的顺序修复刻度位置）。 请注意，对于 matplotlib 版本 3.3.4 和更早版本，这需要在您的绘图循环中:
# To get the ticks in the right order on all subplots, we need to make
# a dummy plot here and then remove it
dummy, = ax[0].plot(xvals, np.zeros_like(xvals))
dummy.remove()
最后，当你在循环中实际绘制真实数据时，你只需要在绘制它们时将 x_ 转换为字符串：
ax[pos].plot(x_.astype('str'), y_, drawstyle='steps-post', marker='*', markersize=8, color='k', linewidth=2)
请注意，我所做的唯一其他更改是未明确设置 xtick 位置（您使用 plt.xticks 进行了设置），但您仍然可以使用该命令设置字体颜色和粗细
plt.xticks(color='SteelBlue', fontweight='bold')
这是输出：
为了完整起见，我已将所有内容放在您的脚本中：
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.ticker as ticker
import matplotlib
print(matplotlib.__version__)
data = {'Name': ['immoControlCmd', 'BrkTerrMde', 'GlblClkYr', 'HsaStat', 'TesterPhysicalResGWM', 'FapLc',
'FirstRowBuckleDriver', 'GlblClkDay'],
'Value': [0, 5, 0, 4, 0, 1, 1, 1],
'Id_Par': [0, 0, 3, 3, 3, 3, 0, 0]
}
signals_df = pd.DataFrame(data)
def plot_signals(signals_df):
# Count signals by par
signals_df['Count'] = signals_df.groupby('Id_Par').cumcount().add(1).mask(signals_df['Id_Par'].eq(0), 0)
# Subtract Par values from the index column
signals_df['Sub'] = signals_df.index - signals_df['Count']
id_par_prev = signals_df['Id_Par'].unique()
id_par = np.delete(id_par_prev, 0)
signals_df['Prev'] = [1 if x in id_par else 0 for x in signals_df['Id_Par']]
signals_df['Final'] = signals_df['Prev'] + signals_df['Sub']
# signals_df['Finall'] = signals_df['Final'].unique()
# print(signals_df['Finall'])
# Convert and set Subtract to index
signals_df.set_index('Final', inplace=True)
# pos_x = len(signals_df.index.unique()) - 1
# print(pos_x)
# Get individual names and variables for the chart
names_list = [name for name in signals_df['Name'].unique()]
num_names_list = len(names_list)
num_axis_x = len(signals_df["Name"])
# Creation Graphics
fig, ax = plt.subplots(nrows=num_names_list, figsize=(10, 10), sharex=True)
# No longer any need to define where the ticks go, but still set the colour and weight here
plt.xticks(color='SteelBlue', fontweight='bold')
# First make a list of all the xticks we want
xvals = [-1, ]
for name in names_list:
xvals.append(signals_df[signals_df["Name"] == name]["Value"].index.values[0])
xvals.append(len(signals_df) - 1)
# Reduce to only unique values, sorted, and then convert to strings
xvals = [str(i) for i in sorted(set(xvals))]
for pos, (a_, name) in enumerate(zip(ax, names_list)):
# To get the ticks in the right order on all subplots,
# we need to make a dummy plot here and then remove it
dummy, = ax[pos].plot(xvals, np.zeros_like(xvals))
dummy.remove()
# Get data
data = signals_df[signals_df["Name"] == name]["Value"]
# Get values axis-x and axis-y
x_ = np.hstack([-1, data.index.values, len(signals_df) - 1])
y_ = np.hstack([0, data.values, data.iloc[-1]])
# Plotting the data by position
# NOTE: here we convert x_ to strings as we plot, to make sure they are plotted as catagorical values
ax[pos].plot(x_.astype('str'), y_, drawstyle='steps-post', marker='*', markersize=8, color='k', linewidth=2)
ax[pos].set_ylabel(name, fontsize=8, fontweight='bold', color='SteelBlue', rotation=30, labelpad=35)
ax[pos].yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
ax[pos].yaxis.set_tick_params(labelsize=6)
ax[pos].grid(alpha=0.4, color='SteelBlue')
plt.show()
plot_signals(signals_df)