根据过滤数据的直方图网格
Grid of histograms according to filtered data
考虑这种数据文件：
数据-file.txt
75,15,1,57.5,9.9,5
75,15,1,58.1,10.0,5
75,15,2,37.9,8.3,5
75,15,2,18.2,7.3,5
150,15,1,26.4,8.3,10
150,15,1,31.6,7.9,10
150,15,2,30.6,7.5,10
150,15,2,25.1,7.1,10
观察第 3 列值仅为 1,2.
我想生成 3x2- 直方图网格。下面的子图看起来是正确的，但是每一行应该包含来自不同数据集的 2 个直方图，我的意思是，我根据最后一列过滤数据。
重要的代码是 ax.hist(X[ (y==grp) & (X[:,2]==1), cols], 过滤器出现的地方。
我想要每行 2 个直方图：
- 第一行
(X[:,2]== * ) 其中 * 是第 3 列（1 或 2）中的任何值，
- 第 2 行
(X[:,2]==1) 和
- 第 3 行
(X[:,2]==2)。
在简历中，我希望得到过滤后数据的第 2、3 行直方图：
第 3 列值 = 1
75,15,1,57.5,9.9,5
75,15,1,58.1,10.0,5
150,15,1,26.4,8.3,10
150,15,1,31.6,7.9,10
第 3 列值 = 2
75,15,2,37.9,8.3,5
75,15,2,18.2,7.3,5
150,15,2,30.6,7.5,10
150,15,2,25.1,7.1,10
代码：
#!/usr/bin/python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import math
from matplotlib import pyplot as plt
from itertools import combinations
data_file='data-file.txt'
df = pd.io.parsers.read_csv(
filepath_or_buffer=data_file,
delim_whitespace=False,
)
M, N = df.shape[0], df.shape[1]
feature_dict = {i+1:label for i,label in zip(
range(N),
('L',
'A',
'G',
'P',
'T',
'PP',
))}
df.columns = [l for i,l in sorted(feature_dict.items())]
X = df[range(N-1)].values
y = df['PP'].values
label_dict = dict(enumerate(sorted(list(set(y)))))
label_dict = {x+1:y for x,y in label_dict.iteritems()}
num_grupos = len(label_dict.keys())
grps_to_hist_list = [[j for j in i] for i in combinations(label_dict.keys(), 2)]
grps_to_hist_list_values = [[j for j in i] for i in combinations(label_dict.values(), 2)]
cols_to_hist = [3, 4]
for grps_to_hist in grps_to_hist_list:
grps_str = [ label_dict[grps_to_hist[0]], label_dict[grps_to_hist[1]] ]
print 'creating histogram for groups %s from data file %s' % (grps_str , data_file)
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(18,8))
for ax,cols in zip(axes.ravel(), cols_to_hist):
# set bin sizes
min_b = math.floor(np.min(X[:,cols]))
max_b = math.ceil(np.max(X[:,cols]))
bins = np.linspace(min_b, max_b, 40)
# ploting the histograms
#"""
for grp,color in zip( grps_str, ('blue', 'red')):
ax.hist(X[ (y==grp) & (X[:,2]==1), cols],
color=color,
label='%s' % grp,
bins=bins,
alpha=0.3,)
ylims = ax.get_ylim()
# plot annotation
leg = ax.legend(loc='upper right', fancybox=True, fontsize=8)
leg.get_frame().set_alpha(0.5)
ax.set_ylim([0, max(ylims)+2])
ax.set_xlabel(feature_dict[cols+1])
ax.set_title('%s' % str(data_file))
# hide axis ticks
ax.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="off", right="off", labelleft="on")
# remove axis spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["left"].set_visible(False)
#"""
fig.tight_layout()
plt.show()
这是上面代码的屏幕截图，其中包含过滤器 (y==grp) & (X[:,2]==1)（应该在第二行）。
我的逻辑是使用您选择的相应掩码遍历行，[(X[:,2]==1) | (X[:,2]==2), X[:,2]==1, X[:,2]==2]。希望这就是您想要的：
#!/usr/bin/python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import math
from matplotlib import pyplot as plt
from itertools import combinations
data_file='data-file.txt'
df = pd.io.parsers.read_csv(
filepath_or_buffer=data_file,
delim_whitespace=False,
)
M, N = df.shape[0], df.shape[1]
feature_dict = {i+1:label for i,label in zip(
range(N),
('L',
'A',
'G',
'P',
'T',
'PP',
))}
df.columns = [l for i,l in sorted(feature_dict.items())]
X = df[range(N-1)].values
y = df['PP'].values
label_dict = dict(enumerate(sorted(list(set(y)))))
label_dict = {x+1:y for x,y in label_dict.iteritems()}
num_grupos = len(label_dict.keys())
grps_to_hist_list = [[j for j in i] for i in combinations(label_dict.keys(), 2)]
grps_to_hist_list_values = [[j for j in i] for i in combinations(label_dict.values(), 2)]
cols_to_hist = [3, 4]
for grps_to_hist in grps_to_hist_list:
grps_str = [ label_dict[grps_to_hist[0]], label_dict[grps_to_hist[1]] ]
print 'creating histogram for groups %s from data file %s' % (grps_str , data_file)
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(18,8))
for row_ax, row_mask in zip(axes, [(X[:,2]==1) | (X[:,2]==2), X[:,2]==1, X[:,2]==2]):
for ax,cols in zip(row_ax, cols_to_hist):
# set bin sizes
min_b = math.floor(np.min(X[:,cols]))
max_b = math.ceil(np.max(X[:,cols]))
bins = np.linspace(min_b, max_b, 40)
# ploting the histograms
#"""
for grp,color in zip( grps_str, ('blue', 'red')):
ax.hist(X[ (y==grp) & row_mask, cols],
color=color,
label='%s' % grp,
bins=bins,
alpha=0.3,)
ylims = ax.get_ylim()
# plot annotation
leg = ax.legend(loc='upper right', fancybox=True, fontsize=8)
leg.get_frame().set_alpha(0.5)
ax.set_ylim([0, max(ylims)+2])
ax.set_xlabel(feature_dict[cols+1])
ax.set_title('%s' % str(data_file))
# hide axis ticks
ax.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="off", right="off", labelleft="on")
# remove axis spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["left"].set_visible(False)
#"""
fig.tight_layout()
plt.show()
考虑这种数据文件：
数据-file.txt
75,15,1,57.5,9.9,5
75,15,1,58.1,10.0,5
75,15,2,37.9,8.3,5
75,15,2,18.2,7.3,5
150,15,1,26.4,8.3,10
150,15,1,31.6,7.9,10
150,15,2,30.6,7.5,10
150,15,2,25.1,7.1,10
观察第 3 列值仅为 1,2.
我想生成 3x2- 直方图网格。下面的子图看起来是正确的，但是每一行应该包含来自不同数据集的 2 个直方图，我的意思是，我根据最后一列过滤数据。
重要的代码是 ax.hist(X[ (y==grp) & (X[:,2]==1), cols], 过滤器出现的地方。
我想要每行 2 个直方图：
- 第一行
(X[:,2]== * )其中*是第 3 列（1 或 2）中的任何值， - 第 2 行
(X[:,2]==1)和 - 第 3 行
(X[:,2]==2)。
在简历中，我希望得到过滤后数据的第 2、3 行直方图：
第 3 列值 = 1
75,15,1,57.5,9.9,5
75,15,1,58.1,10.0,5
150,15,1,26.4,8.3,10
150,15,1,31.6,7.9,10
第 3 列值 = 2
75,15,2,37.9,8.3,5
75,15,2,18.2,7.3,5
150,15,2,30.6,7.5,10
150,15,2,25.1,7.1,10
代码：
#!/usr/bin/python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import math
from matplotlib import pyplot as plt
from itertools import combinations
data_file='data-file.txt'
df = pd.io.parsers.read_csv(
filepath_or_buffer=data_file,
delim_whitespace=False,
)
M, N = df.shape[0], df.shape[1]
feature_dict = {i+1:label for i,label in zip(
range(N),
('L',
'A',
'G',
'P',
'T',
'PP',
))}
df.columns = [l for i,l in sorted(feature_dict.items())]
X = df[range(N-1)].values
y = df['PP'].values
label_dict = dict(enumerate(sorted(list(set(y)))))
label_dict = {x+1:y for x,y in label_dict.iteritems()}
num_grupos = len(label_dict.keys())
grps_to_hist_list = [[j for j in i] for i in combinations(label_dict.keys(), 2)]
grps_to_hist_list_values = [[j for j in i] for i in combinations(label_dict.values(), 2)]
cols_to_hist = [3, 4]
for grps_to_hist in grps_to_hist_list:
grps_str = [ label_dict[grps_to_hist[0]], label_dict[grps_to_hist[1]] ]
print 'creating histogram for groups %s from data file %s' % (grps_str , data_file)
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(18,8))
for ax,cols in zip(axes.ravel(), cols_to_hist):
# set bin sizes
min_b = math.floor(np.min(X[:,cols]))
max_b = math.ceil(np.max(X[:,cols]))
bins = np.linspace(min_b, max_b, 40)
# ploting the histograms
#"""
for grp,color in zip( grps_str, ('blue', 'red')):
ax.hist(X[ (y==grp) & (X[:,2]==1), cols],
color=color,
label='%s' % grp,
bins=bins,
alpha=0.3,)
ylims = ax.get_ylim()
# plot annotation
leg = ax.legend(loc='upper right', fancybox=True, fontsize=8)
leg.get_frame().set_alpha(0.5)
ax.set_ylim([0, max(ylims)+2])
ax.set_xlabel(feature_dict[cols+1])
ax.set_title('%s' % str(data_file))
# hide axis ticks
ax.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="off", right="off", labelleft="on")
# remove axis spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["left"].set_visible(False)
#"""
fig.tight_layout()
plt.show()
这是上面代码的屏幕截图，其中包含过滤器 (y==grp) & (X[:,2]==1)（应该在第二行）。
我的逻辑是使用您选择的相应掩码遍历行，[(X[:,2]==1) | (X[:,2]==2), X[:,2]==1, X[:,2]==2]。希望这就是您想要的：
#!/usr/bin/python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import math
from matplotlib import pyplot as plt
from itertools import combinations
data_file='data-file.txt'
df = pd.io.parsers.read_csv(
filepath_or_buffer=data_file,
delim_whitespace=False,
)
M, N = df.shape[0], df.shape[1]
feature_dict = {i+1:label for i,label in zip(
range(N),
('L',
'A',
'G',
'P',
'T',
'PP',
))}
df.columns = [l for i,l in sorted(feature_dict.items())]
X = df[range(N-1)].values
y = df['PP'].values
label_dict = dict(enumerate(sorted(list(set(y)))))
label_dict = {x+1:y for x,y in label_dict.iteritems()}
num_grupos = len(label_dict.keys())
grps_to_hist_list = [[j for j in i] for i in combinations(label_dict.keys(), 2)]
grps_to_hist_list_values = [[j for j in i] for i in combinations(label_dict.values(), 2)]
cols_to_hist = [3, 4]
for grps_to_hist in grps_to_hist_list:
grps_str = [ label_dict[grps_to_hist[0]], label_dict[grps_to_hist[1]] ]
print 'creating histogram for groups %s from data file %s' % (grps_str , data_file)
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(18,8))
for row_ax, row_mask in zip(axes, [(X[:,2]==1) | (X[:,2]==2), X[:,2]==1, X[:,2]==2]):
for ax,cols in zip(row_ax, cols_to_hist):
# set bin sizes
min_b = math.floor(np.min(X[:,cols]))
max_b = math.ceil(np.max(X[:,cols]))
bins = np.linspace(min_b, max_b, 40)
# ploting the histograms
#"""
for grp,color in zip( grps_str, ('blue', 'red')):
ax.hist(X[ (y==grp) & row_mask, cols],
color=color,
label='%s' % grp,
bins=bins,
alpha=0.3,)
ylims = ax.get_ylim()
# plot annotation
leg = ax.legend(loc='upper right', fancybox=True, fontsize=8)
leg.get_frame().set_alpha(0.5)
ax.set_ylim([0, max(ylims)+2])
ax.set_xlabel(feature_dict[cols+1])
ax.set_title('%s' % str(data_file))
# hide axis ticks
ax.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="off", right="off", labelleft="on")
# remove axis spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["left"].set_visible(False)
#"""
fig.tight_layout()
plt.show()