import matplotlib as mp
时间: 2023-08-14 18:16:21 浏览: 57
在Python中,导入Matplotlib库的标准方式为:
```python
import matplotlib.pyplot as plt
```
它将Matplotlib库中的pyplot模块导入,并将其命名为plt。这样,就可以使用plt来调用Matplotlib中的各种函数和工具。
Matplotlib是一个Python 2D绘图库,它可以用于生成各种类型的图形,包括线图、散点图、条形图、直方图、饼图等等。Matplotlib的pyplot模块提供了类似于MATLAB的绘图接口,使得用户可以快速绘制各种类型的图形。
例如,要绘制一个简单的线图,可以使用以下代码:
```python
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.plot(x, y)
plt.show()
```
这会生成一个包含x轴和y轴的简单线图。如果要绘制其他类型的图形,可以查看Matplotlib文档中的示例和教程。
相关问题
在Python中想运行from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random怎么操作
要在Python中运行这些代码,你需要确保已经安装了相应的包。大多数包都可以使用`pip`进行安装。
首先,在命令行中运行以下命令来安装所需的包:
```bash
pip install scikit-learn pandas matplotlib numpy
```
然后,在你的Python脚本中导入所需的库:
```python
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import pandas as pd
from sklearn import svm
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import colors
from sklearn.model_selection import train_test_split
from sklearn import datasets
from matplotlib.colors import ListedColormap
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
import datetime
import multiprocessing as mp
from sklearn.ensemble import StackingClassifier
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC
import random
```
请确保在运行这些代码之前,已经安装了所需的Python库。如果遇到任何问题,请确保已正确安装这些库,并且版本与代码兼容。
import numpy as np import pandas as pd import matplotlib.pyplot as mp df=pd.read_excel('C:/Users/86147/OneDrive/文档/66666.xlsx',header=0,usecols=(1,)) mylist1=df.values.tolist() room=[] for i in mylist1: room.append(i[0]) df=pd.read_excel('C:/Users/86147/OneDrive/文档/66666.xlsx',header=0,usecols=(2,)) mylist1=df.values.tolist() tomp=[] for i in mylist1: tomp.append(i[0]) room_tomp=zip(room,tomp) avge=[] ls=[] for i in range(1,185): ls.clear() for k,v in room_tomp: if k==i: ls.append(v) s=sum(ls) avg=s/2928 avge.append(avg) print(avge)修改这个代码让avge输出都为正常值
可以在每次遍历时重新生成room_tomp,避免其被清空的问题。修改后的代码如下:
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as mp
df=pd.read_excel('C:/Users/86147/OneDrive/文档/66666.xlsx',header=0,usecols=(1,))
mylist1=df.values.tolist()
room=[]
for i in mylist1:
room.append(i[0])
df=pd.read_excel('C:/Users/86147/OneDrive/文档/66666.xlsx',header=0,usecols=(2,))
mylist1=df.values.tolist()
tomp=[]
for i in mylist1:
tomp.append(i[0])
avge=[]
for i in range(1,185):
ls=[]
room_tomp=zip(room,tomp)
for k,v in room_tomp:
if k==i:
ls.append(v)
s=sum(ls)
avg=s/2928
avge.append(avg)
print(avge)
```
这样就可以得到每个房间的平均温度了。
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