# 取数据

## ipython 查看加载变量

```bash
%who_ls
```

## 获取数据框中数据

当使用`loc`获得一个数据框的时候，要想遍历这个数据集中的内容

* `iterrows`按照行获取数据，每次获得一行数据

> `iterrows`是一个生成器，每个返回的生成器中包含两个内容：
>
> * 改行的索引
> * 改行的数据list，并且list的下标是从0开始的

```python
>>>df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>>row = next(df.iterrows())[1]
>>>row
int      1.0
float    1.5
Name: 0, dtype: float64
>>>print(row['int'].dtype)
float64
>>>print(df['int'].dtype)
int64
```

### 遍历一行中的列

* 使用索引进行遍历`iloc[:0]`
* 使用列名进行遍历`loc['chr']`

```python
##获取pandas中的某一行
df=d.loc[(d['chr']=="Ghir_A01") & (d['start']==8076) & (d['end']==9496)]
#获取该行对应的第3列及以后列的数据
df.iloc[:,3:]
df.loc[['列名3','列名4'...]]
```

### 使用映射函数，批量处理

对数据库进行批量处理

* map
* apply

```python
##使用apply对数据框进行批量处理
#没列数加上10
df.apply(lambda x:x+10,axis=1)
```

### 多条件筛选

筛选符合多个条件的行

> 筛选`chr`列为"Ghir\_A01"同时"start"列为8076的行

```python
>>>d.loc[(d['chr']=="Ghir_A01") & (d['start']==8076)]
    chr  start    end  0DPA_Sample001
0   Ghir_A01   8076   9496               0
```

### 获取多行多列

```python
df.loc['one':'two',['a','c']]#one到two行，ac列
```

### 去除行名中的重复

```bash
## 行名重复 index
def filterData(myDF):
    ...:     myDF['index'] = myDF.index
    ...:     myDF= myDF.drop_duplicates('index')
    ...:     myDF.set_index = myDF['index']
    ...:     myDF= myDF.drop('index', axis =1)
    ...:     return myDF
```

### 合并两个数据框

```bash
#横向连接两个数据框，按行合并
pd.concat([data1,data2],axis=0)
#纵向连接两个数据框,按列合并
pd.concat([data1,data2],axis=1)
#merge 合并
```

### 修改行名与列名

```bash
#修改列名
mergeData.columns=['HuanGangRep1', 'HuanGangRep2', 'HuanGangRep3', 'mean']
#修改行名
mergeData.index=['HuanGangRep1', 'HuanGangRep2', 'HuanGangRep3', 'mean']
```

### 调整列的顺序

```bash
```

### 按照指定列进行排序

```bash
 #! 按照第18列进行降序排序后，提取对应的p-value
pt=tmpData.sort_values(by=[18],axis=0,ascending=False).iloc[0,17]
```


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