# ML问题总结

## matplotlib总结

#### 显示中文需要添加：

```python
import matplotlib as plt
plt.rcParams['font.sas-serig']=['SimHei'] # 用来正确显示中文标签
plt.rcParams['axes.unicode_minus']=False # 用来争取显示正负号
```

## Numpy总结

np.r是按列连接两个矩阵，就是把两矩阵上下相加，要求列数相等，类似于pandas中的concat()。 np.c是按行连接两个矩阵，就是把两矩阵左右相加，要求行数相等，类似于pandas中的merge()。

### stack, vstack, hstack

ref: <https://cloud.tencent.com/developer/article/1378491>

### range和np.arange的区别

ref: <https://blog.csdn.net/lanchunhui/article/details/49493633>

## Pandas

使用read\_csv读入文件的时候，访问的方式不同得到的变量的类型不同

```python
import pandas as pd
data = pd.read_csv('train.csv')
data['occupation']     # 返回的格式是：pandas.core.series.Series
data[['occupation']]   # 返回的格式是：pandas.core.frame.DataFrame
```

判断一个数据内容是否为空

```python
import pandas as pd
import numpy as np
x = np.nan
# x = pd.NA
pd.isnull(x)
```

创建一个新的DataFrame变量，增加新的一行数据

```python
import pandas as pd
df = pd.DataFrame([],columns=['one','two'])  
# df = df.append才可以生效，直接是df.append不可以
# ignore_index=True，表示的是让index从0开始依次递增
df = df.append([{'one':10.0, 'two': 90}],ignore_index = True)
df.append([{'one':"11.0", 'two': 90}], ignore_index = True)
```

Series变量如何reshape

```python
# data['China'] # 假定该变量为Series类型
# reshape的方式如下, .values得到的是一个ndarray
data['China'].values.reshape(-1, 1)
```

## 打印juypter运行时间

magic函数 magic有行魔法%time 和单元魔法%%time


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