使用seaborn可视化缺失数据出现频率
Visualisation of missing-data occurrence frequency by using seaborn
我想创建一个 24x20 矩阵（8 个部分，每个部分有 60 个单元格或 6x10），用于通过循环（=每个 480-values) 通过 panda dataframe 在数据集中并为每一列绘制它 'A','B','C'.
到目前为止，我可以映射创建的 csv 文件并在矩阵中以正确的方式映射值，并在更改缺失数据后通过 sns.heatmap(df.isnull()) 绘制它（nan & inf) 到 0 或类似 0.01234 的东西，它对数据的影响最小，另一方面可以绘制。
以下是我目前的脚本：
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as plt
def mkdf(ListOf480Numbers):
normalMatrix = np.array_split(ListOf480Numbers,8)
fixMatrix = []
for i in range(8):
lines = np.array_split(normalMatrix[i],6)
newMatrix = [0,0,0,0,0,0]
for j in (1,3,5):
newMatrix[j] = lines[j]
for j in (0,2,4):
newMatrix[j] = lines[j][::-1]
fixMatrix.append(newMatrix)
return fixMatrix
def print_df(fixMatrix):
values = []
for i in range(6):
values.append([*fixMatrix[6][i], *fixMatrix[7][i]])
for i in range(6):
values.append([*fixMatrix[4][i], *fixMatrix[5][i]])
for i in range(6):
values.append([*fixMatrix[2][i], *fixMatrix[3][i]])
for i in range(6):
values.append([*fixMatrix[0][i], *fixMatrix[1][i]])
df = pd.DataFrame(values)
return (df)
dft = pd.read_csv('D:\Feryan.TXT', header=None)
id_set = dft[dft.index % 4 == 0].astype('int').values
A = dft[dft.index % 4 == 1].values
B = dft[dft.index % 4 == 2].values
C = dft[dft.index % 4 == 3].values
data = {'A': A[:,0], 'B': B[:,0], 'C': C[:,0]}
df = pd.DataFrame(data, columns=['A','B','C'], index = id_set[:,0])
nan = np.array(df.isnull())
inf = np.array(df.isnull())
df = df.replace([np.inf, -np.inf], np.nan)
df[np.isinf(df)] = np.nan # convert inf to nan
#dff = df[df.isnull().any(axis=1)] # extract sub data frame
#df = df.fillna(0)
#df = df.replace(0,np.nan)
#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for cycle in range(3):
count = '{:04}'.format(cycle)
j = cycle * 480
new_value1 = df['A'].iloc[j:j+480]
new_value2 = df['B'].iloc[j:j+480]
new_value3 = df['C'].iloc[j:j+480]
df1 = print_df(mkdf(new_value1))
df2 = print_df(mkdf(new_value2))
df3 = print_df(mkdf(new_value3))
for i in df:
try:
os.mkdir(i)
except:
pass
df1.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
df2.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
df3.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
#plotting all columns ['A','B','C'] in-one-window side by side
fig, ax = plt.subplots(nrows=1, ncols=3 , figsize=(20,10))
plt.subplot(131)
ax = sns.heatmap(df1.isnull(), cbar=False)
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in A', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.subplot(132)
ax = sns.heatmap(df2.isnull(), cbar=False)
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in B', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.subplot(133)
ax = sns.heatmap(df3.isnull(), cbar=False)
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in C', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.suptitle(f'Missing-data visualization', color='yellow', backgroundcolor='black', fontsize=15, fontweight='bold')
plt.subplots_adjust(top=0.92, bottom=0.02, left=0.05, right=0.96, hspace=0.2, wspace=0.2)
fig.text(0.035, 0.93, 'dataset1' , fontsize=19, fontweight='bold', rotation=42., ha='center', va='center',bbox=dict(boxstyle="round",ec=(1., 0.5, 0.5),fc=(1., 0.8, 0.8)))
#fig.tight_layout()
plt.savefig(f'{i}/result{count}.png')
#plt.show()
问题 是我不知道如何正确绘制缺失数据出现的频率以了解它在哪些部分和单元格中频繁发生。
Note1更多的缺失值颜色应该更亮，100%的缺失数据通过循环应该用white颜色和纯黑色 颜色表示非缺失值。可能会有从黑色 0% 到 100% 白色开始的条形图。
Note2 我也提供了3个周期数据集的示例文本文件，包括一些缺失数据，但可以手动修改和增加：dataset
预期结果 应该如下所示：
您可以将 nan/inf 数据存储在一个单独的数组中，您可以在每个 nan/inf 的循环中将其相加。
您的数组似乎总是具有相同的大小，因此我将它们定义为固定大小。您可以更改它以匹配您的数据：
df1MissingDataFrequency = np.zeros((24,20))
然后您可以将它们相加，得到 nan 值（您已经在代码中将 inf 替换为 nan）：
df1MissingDataFrequency = df1MissingDataFrequency + np.isnan(df1).astype(int)
在你所有的周期中。
您的缩进似乎有问题。我不知道这是否只是您在此处发布的代码的情况，或者您的实际代码是否相同，但目前您每个周期都会制作一个新图 and您为每个 i.
重新定义 df1, df2, df3
由于缺少频率数据，您的代码应如下所示：
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as plt
def mkdf(ListOf480Numbers):
normalMatrix = np.array_split(ListOf480Numbers,8)
fixMatrix = []
for i in range(8):
lines = np.array_split(normalMatrix[i],6)
newMatrix = [0,0,0,0,0,0]
for j in (1,3,5):
newMatrix[j] = lines[j]
for j in (0,2,4):
newMatrix[j] = lines[j][::-1]
fixMatrix.append(newMatrix)
return fixMatrix
def print_df(fixMatrix):
values = []
for i in range(6):
values.append([*fixMatrix[6][i], *fixMatrix[7][i]])
for i in range(6):
values.append([*fixMatrix[4][i], *fixMatrix[5][i]])
for i in range(6):
values.append([*fixMatrix[2][i], *fixMatrix[3][i]])
for i in range(6):
values.append([*fixMatrix[0][i], *fixMatrix[1][i]])
df = pd.DataFrame(values)
return (df)
dft = pd.read_csv('D:/Feryan2.txt', header=None)
id_set = dft[dft.index % 4 == 0].astype('int').values
A = dft[dft.index % 4 == 1].values
B = dft[dft.index % 4 == 2].values
C = dft[dft.index % 4 == 3].values
data = {'A': A[:,0], 'B': B[:,0], 'C': C[:,0]}
df = pd.DataFrame(data, columns=['A','B','C'], index = id_set[:,0])
nan = np.array(df.isnull())
inf = np.array(df.isnull())
df = df.replace([np.inf, -np.inf], np.nan)
df[np.isinf(df)] = np.nan # convert inf to nan
df1MissingDataFrequency = np.zeros((24,20))
df2MissingDataFrequency = np.zeros((24,20))
df3MissingDataFrequency = np.zeros((24,20))
#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for cycle in range(3):
count = '{:04}'.format(cycle)
j = cycle * 480
new_value1 = df['A'].iloc[j:j+480]
new_value2 = df['B'].iloc[j:j+480]
new_value3 = df['C'].iloc[j:j+480]
df1 = print_df(mkdf(new_value1))
df2 = print_df(mkdf(new_value2))
df3 = print_df(mkdf(new_value3))
for i in df:
try:
os.mkdir(i)
except:
pass
df1.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
df2.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
df3.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
df1MissingDataFrequency = df1MissingDataFrequency + np.isnan(df1).astype(int)
df2MissingDataFrequency = df2MissingDataFrequency + np.isnan(df2).astype(int)
df3MissingDataFrequency = df3MissingDataFrequency + np.isnan(df3).astype(int)
#plotting all columns ['A','B','C'] in-one-window side by side
fig, ax = plt.subplots(nrows=1, ncols=3 , figsize=(10,7))
plt.subplot(131)
ax = sns.heatmap(df1MissingDataFrequency, cbar=False, cmap="gray")
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in A', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.subplot(132)
ax = sns.heatmap(df2MissingDataFrequency, cbar=False, cmap="gray")
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in B', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.subplot(133)
ax = sns.heatmap(df3MissingDataFrequency, cbar=False, cmap="gray")
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in C', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.suptitle(f'Missing-data visualization', color='yellow', backgroundcolor='black', fontsize=15, fontweight='bold')
plt.subplots_adjust(top=0.92, bottom=0.02, left=0.05, right=0.96, hspace=0.2, wspace=0.2)
fig.text(0.035, 0.93, 'dataset1' , fontsize=19, fontweight='bold', rotation=42., ha='center', va='center',bbox=dict(boxstyle="round",ec=(1., 0.5, 0.5),fc=(1., 0.8, 0.8)))
#fig.tight_layout()
plt.savefig(f'{i}/result{count}.png')
#plt.show()
哪个给你你想要的输出：
编辑
本着 DRY 的精神，我编辑了您的代码，因此您没有 df1、df2、df3、new_values1，...并且您到处复制和粘贴相同的内容.您已经遍历 i，因此您应该使用它来实际处理数据框中的三个不同列：
dft = pd.read_csv('C:/Users/frefra/Downloads/Feryan2.txt', header=None).replace([np.inf, -np.inf], np.nan)
id_set = dft[dft.index % 4 == 0].astype('int').values
A = dft[dft.index % 4 == 1].values
B = dft[dft.index % 4 == 2].values
C = dft[dft.index % 4 == 3].values
data = {'A': A[:,0], 'B': B[:,0], 'C': C[:,0]}
df = pd.DataFrame(data, columns=['A','B','C'], index = id_set[:,0])
new_values = []
dfs = []
nan_frequencies = np.zeros((3,24,20))
#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for cycle in range(cycles):
count = '{:04}'.format(cycle)
j = cycle * 480
for idx,i in enumerate(df):
try:
os.mkdir(i)
except:
pass
new_value = df[i].iloc[j:j+480]
new_values.append(new_value)
dfi = print_df(mkdf(new_value))
dfs.append(dfi)
dfi.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
nan_frequencies[idx] = nan_frequencies[idx] + np.isnan(dfi).astype(int)
#plotting all columns ['A','B','C'] in-one-window side by side
fig, ax = plt.subplots(nrows=1, ncols=3 , figsize=(10,7))
for idx,i in enumerate(df):
plt.subplot(1,3,idx+1)
ax = sns.heatmap(nan_frequencies[idx], cbar=False, cmap="gray")
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in ' + i, fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
我想创建一个 24x20 矩阵（8 个部分，每个部分有 60 个单元格或 6x10），用于通过循环（=每个 480-values) 通过 panda dataframe 在数据集中并为每一列绘制它 'A','B','C'.
到目前为止，我可以映射创建的 csv 文件并在矩阵中以正确的方式映射值，并在更改缺失数据后通过 sns.heatmap(df.isnull()) 绘制它（nan & inf) 到 0 或类似 0.01234 的东西，它对数据的影响最小，另一方面可以绘制。
以下是我目前的脚本：
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as plt
def mkdf(ListOf480Numbers):
normalMatrix = np.array_split(ListOf480Numbers,8)
fixMatrix = []
for i in range(8):
lines = np.array_split(normalMatrix[i],6)
newMatrix = [0,0,0,0,0,0]
for j in (1,3,5):
newMatrix[j] = lines[j]
for j in (0,2,4):
newMatrix[j] = lines[j][::-1]
fixMatrix.append(newMatrix)
return fixMatrix
def print_df(fixMatrix):
values = []
for i in range(6):
values.append([*fixMatrix[6][i], *fixMatrix[7][i]])
for i in range(6):
values.append([*fixMatrix[4][i], *fixMatrix[5][i]])
for i in range(6):
values.append([*fixMatrix[2][i], *fixMatrix[3][i]])
for i in range(6):
values.append([*fixMatrix[0][i], *fixMatrix[1][i]])
df = pd.DataFrame(values)
return (df)
dft = pd.read_csv('D:\Feryan.TXT', header=None)
id_set = dft[dft.index % 4 == 0].astype('int').values
A = dft[dft.index % 4 == 1].values
B = dft[dft.index % 4 == 2].values
C = dft[dft.index % 4 == 3].values
data = {'A': A[:,0], 'B': B[:,0], 'C': C[:,0]}
df = pd.DataFrame(data, columns=['A','B','C'], index = id_set[:,0])
nan = np.array(df.isnull())
inf = np.array(df.isnull())
df = df.replace([np.inf, -np.inf], np.nan)
df[np.isinf(df)] = np.nan # convert inf to nan
#dff = df[df.isnull().any(axis=1)] # extract sub data frame
#df = df.fillna(0)
#df = df.replace(0,np.nan)
#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for cycle in range(3):
count = '{:04}'.format(cycle)
j = cycle * 480
new_value1 = df['A'].iloc[j:j+480]
new_value2 = df['B'].iloc[j:j+480]
new_value3 = df['C'].iloc[j:j+480]
df1 = print_df(mkdf(new_value1))
df2 = print_df(mkdf(new_value2))
df3 = print_df(mkdf(new_value3))
for i in df:
try:
os.mkdir(i)
except:
pass
df1.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
df2.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
df3.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
#plotting all columns ['A','B','C'] in-one-window side by side
fig, ax = plt.subplots(nrows=1, ncols=3 , figsize=(20,10))
plt.subplot(131)
ax = sns.heatmap(df1.isnull(), cbar=False)
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in A', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.subplot(132)
ax = sns.heatmap(df2.isnull(), cbar=False)
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in B', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.subplot(133)
ax = sns.heatmap(df3.isnull(), cbar=False)
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in C', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.suptitle(f'Missing-data visualization', color='yellow', backgroundcolor='black', fontsize=15, fontweight='bold')
plt.subplots_adjust(top=0.92, bottom=0.02, left=0.05, right=0.96, hspace=0.2, wspace=0.2)
fig.text(0.035, 0.93, 'dataset1' , fontsize=19, fontweight='bold', rotation=42., ha='center', va='center',bbox=dict(boxstyle="round",ec=(1., 0.5, 0.5),fc=(1., 0.8, 0.8)))
#fig.tight_layout()
plt.savefig(f'{i}/result{count}.png')
#plt.show()
问题 是我不知道如何正确绘制缺失数据出现的频率以了解它在哪些部分和单元格中频繁发生。
Note1更多的缺失值颜色应该更亮，100%的缺失数据通过循环应该用white颜色和纯黑色 颜色表示非缺失值。可能会有从黑色 0% 到 100% 白色开始的条形图。
Note2 我也提供了3个周期数据集的示例文本文件，包括一些缺失数据，但可以手动修改和增加：dataset
预期结果 应该如下所示：
您可以将 nan/inf 数据存储在一个单独的数组中，您可以在每个 nan/inf 的循环中将其相加。
您的数组似乎总是具有相同的大小，因此我将它们定义为固定大小。您可以更改它以匹配您的数据：
df1MissingDataFrequency = np.zeros((24,20))
然后您可以将它们相加，得到 nan 值（您已经在代码中将 inf 替换为 nan）：
df1MissingDataFrequency = df1MissingDataFrequency + np.isnan(df1).astype(int)
在你所有的周期中。
您的缩进似乎有问题。我不知道这是否只是您在此处发布的代码的情况，或者您的实际代码是否相同，但目前您每个周期都会制作一个新图 and您为每个 i.
df1, df2, df3
由于缺少频率数据，您的代码应如下所示：
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as plt
def mkdf(ListOf480Numbers):
normalMatrix = np.array_split(ListOf480Numbers,8)
fixMatrix = []
for i in range(8):
lines = np.array_split(normalMatrix[i],6)
newMatrix = [0,0,0,0,0,0]
for j in (1,3,5):
newMatrix[j] = lines[j]
for j in (0,2,4):
newMatrix[j] = lines[j][::-1]
fixMatrix.append(newMatrix)
return fixMatrix
def print_df(fixMatrix):
values = []
for i in range(6):
values.append([*fixMatrix[6][i], *fixMatrix[7][i]])
for i in range(6):
values.append([*fixMatrix[4][i], *fixMatrix[5][i]])
for i in range(6):
values.append([*fixMatrix[2][i], *fixMatrix[3][i]])
for i in range(6):
values.append([*fixMatrix[0][i], *fixMatrix[1][i]])
df = pd.DataFrame(values)
return (df)
dft = pd.read_csv('D:/Feryan2.txt', header=None)
id_set = dft[dft.index % 4 == 0].astype('int').values
A = dft[dft.index % 4 == 1].values
B = dft[dft.index % 4 == 2].values
C = dft[dft.index % 4 == 3].values
data = {'A': A[:,0], 'B': B[:,0], 'C': C[:,0]}
df = pd.DataFrame(data, columns=['A','B','C'], index = id_set[:,0])
nan = np.array(df.isnull())
inf = np.array(df.isnull())
df = df.replace([np.inf, -np.inf], np.nan)
df[np.isinf(df)] = np.nan # convert inf to nan
df1MissingDataFrequency = np.zeros((24,20))
df2MissingDataFrequency = np.zeros((24,20))
df3MissingDataFrequency = np.zeros((24,20))
#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for cycle in range(3):
count = '{:04}'.format(cycle)
j = cycle * 480
new_value1 = df['A'].iloc[j:j+480]
new_value2 = df['B'].iloc[j:j+480]
new_value3 = df['C'].iloc[j:j+480]
df1 = print_df(mkdf(new_value1))
df2 = print_df(mkdf(new_value2))
df3 = print_df(mkdf(new_value3))
for i in df:
try:
os.mkdir(i)
except:
pass
df1.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
df2.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
df3.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
df1MissingDataFrequency = df1MissingDataFrequency + np.isnan(df1).astype(int)
df2MissingDataFrequency = df2MissingDataFrequency + np.isnan(df2).astype(int)
df3MissingDataFrequency = df3MissingDataFrequency + np.isnan(df3).astype(int)
#plotting all columns ['A','B','C'] in-one-window side by side
fig, ax = plt.subplots(nrows=1, ncols=3 , figsize=(10,7))
plt.subplot(131)
ax = sns.heatmap(df1MissingDataFrequency, cbar=False, cmap="gray")
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in A', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.subplot(132)
ax = sns.heatmap(df2MissingDataFrequency, cbar=False, cmap="gray")
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in B', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.subplot(133)
ax = sns.heatmap(df3MissingDataFrequency, cbar=False, cmap="gray")
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in C', fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')
plt.suptitle(f'Missing-data visualization', color='yellow', backgroundcolor='black', fontsize=15, fontweight='bold')
plt.subplots_adjust(top=0.92, bottom=0.02, left=0.05, right=0.96, hspace=0.2, wspace=0.2)
fig.text(0.035, 0.93, 'dataset1' , fontsize=19, fontweight='bold', rotation=42., ha='center', va='center',bbox=dict(boxstyle="round",ec=(1., 0.5, 0.5),fc=(1., 0.8, 0.8)))
#fig.tight_layout()
plt.savefig(f'{i}/result{count}.png')
#plt.show()
哪个给你你想要的输出：
编辑
本着 DRY 的精神，我编辑了您的代码，因此您没有 df1、df2、df3、new_values1，...并且您到处复制和粘贴相同的内容.您已经遍历 i，因此您应该使用它来实际处理数据框中的三个不同列：
dft = pd.read_csv('C:/Users/frefra/Downloads/Feryan2.txt', header=None).replace([np.inf, -np.inf], np.nan)
id_set = dft[dft.index % 4 == 0].astype('int').values
A = dft[dft.index % 4 == 1].values
B = dft[dft.index % 4 == 2].values
C = dft[dft.index % 4 == 3].values
data = {'A': A[:,0], 'B': B[:,0], 'C': C[:,0]}
df = pd.DataFrame(data, columns=['A','B','C'], index = id_set[:,0])
new_values = []
dfs = []
nan_frequencies = np.zeros((3,24,20))
#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for cycle in range(cycles):
count = '{:04}'.format(cycle)
j = cycle * 480
for idx,i in enumerate(df):
try:
os.mkdir(i)
except:
pass
new_value = df[i].iloc[j:j+480]
new_values.append(new_value)
dfi = print_df(mkdf(new_value))
dfs.append(dfi)
dfi.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
nan_frequencies[idx] = nan_frequencies[idx] + np.isnan(dfi).astype(int)
#plotting all columns ['A','B','C'] in-one-window side by side
fig, ax = plt.subplots(nrows=1, ncols=3 , figsize=(10,7))
for idx,i in enumerate(df):
plt.subplot(1,3,idx+1)
ax = sns.heatmap(nan_frequencies[idx], cbar=False, cmap="gray")
ax.axhline(y=6, color='w',linewidth=1.5)
ax.axhline(y=12, color='w',linewidth=1.5)
ax.axhline(y=18, color='w',linewidth=1.5)
ax.axvline(x=10, color='w',linewidth=1.5)
plt.title('Missing-data frequency in ' + i, fontsize=20 , fontweight='bold', color='black', loc='center', style='italic')
plt.axis('off')