mirror of
https://github.com/apachecn/ailearning.git
synced 2026-02-12 06:46:14 +08:00
250 lines
3.2 KiB
Markdown
250 lines
3.2 KiB
Markdown
# CSV 文件和 csv 模块
|
||
|
||
标准库中有自带的 `csv` (逗号分隔值) 模块处理 `csv` 格式的文件：
|
||
|
||
In [1]:
|
||
|
||
```py
|
||
import csv
|
||
|
||
```
|
||
|
||
## 读 csv 文件
|
||
|
||
假设我们有这样的一个文件：
|
||
|
||
In [2]:
|
||
|
||
```py
|
||
%%file data.csv
|
||
"alpha 1", 100, -1.443
|
||
"beat 3", 12, -0.0934
|
||
"gamma 3a", 192, -0.6621
|
||
"delta 2a", 15, -4.515
|
||
|
||
```
|
||
|
||
```py
|
||
Writing data.csv
|
||
|
||
```
|
||
|
||
打开这个文件，并产生一个文件 reader：
|
||
|
||
In [3]:
|
||
|
||
```py
|
||
fp = open("data.csv")
|
||
r = csv.reader(fp)
|
||
|
||
```
|
||
|
||
可以按行迭代数据：
|
||
|
||
In [4]:
|
||
|
||
```py
|
||
for row in r:
|
||
print row
|
||
|
||
fp.close()
|
||
|
||
```
|
||
|
||
```py
|
||
['alpha 1', ' 100', ' -1.443']
|
||
['beat 3', ' 12', ' -0.0934']
|
||
['gamma 3a', ' 192', ' -0.6621']
|
||
['delta 2a', ' 15', ' -4.515']
|
||
|
||
```
|
||
|
||
默认数据内容都被当作字符串处理，不过可以自己进行处理：
|
||
|
||
In [5]:
|
||
|
||
```py
|
||
data = []
|
||
|
||
with open('data.csv') as fp:
|
||
r = csv.reader(fp)
|
||
for row in r:
|
||
data.append([row[0], int(row[1]), float(row[2])])
|
||
|
||
data
|
||
|
||
```
|
||
|
||
Out[5]:
|
||
|
||
```py
|
||
[['alpha 1', 100, -1.443],
|
||
['beat 3', 12, -0.0934],
|
||
['gamma 3a', 192, -0.6621],
|
||
['delta 2a', 15, -4.515]]
|
||
```
|
||
|
||
In [6]:
|
||
|
||
```py
|
||
import os
|
||
os.remove('data.csv')
|
||
|
||
```
|
||
|
||
## 写 csv 文件
|
||
|
||
可以使用 `csv.writer` 写入文件，不过相应地，传入的应该是以写方式打开的文件，不过一般要用 `'wb'` 即二进制写入方式，防止出现换行不正确的问题：
|
||
|
||
In [7]:
|
||
|
||
```py
|
||
data = [('one', 1, 1.5), ('two', 2, 8.0)]
|
||
with open('out.csv', 'wb') as fp:
|
||
w = csv.writer(fp)
|
||
w.writerows(data)
|
||
|
||
```
|
||
|
||
显示结果：
|
||
|
||
In [8]:
|
||
|
||
```py
|
||
!cat 'out.csv'
|
||
|
||
```
|
||
|
||
```py
|
||
one,1,1.5
|
||
two,2,8.0
|
||
|
||
```
|
||
|
||
## 更换分隔符
|
||
|
||
默认情况下，`csv` 模块默认 `csv` 文件都是由 `excel` 产生的，实际中可能会遇到这样的问题：
|
||
|
||
In [9]:
|
||
|
||
```py
|
||
data = [('one, \"real\" string', 1, 1.5), ('two', 2, 8.0)]
|
||
with open('out.csv', 'wb') as fp:
|
||
w = csv.writer(fp)
|
||
w.writerows(data)
|
||
|
||
```
|
||
|
||
In [10]:
|
||
|
||
```py
|
||
!cat 'out.csv'
|
||
|
||
```
|
||
|
||
```py
|
||
"one, ""real"" string",1,1.5
|
||
two,2,8.0
|
||
|
||
```
|
||
|
||
可以修改分隔符来处理这组数据：
|
||
|
||
In [11]:
|
||
|
||
```py
|
||
data = [('one, \"real\" string', 1, 1.5), ('two', 2, 8.0)]
|
||
with open('out.psv', 'wb') as fp:
|
||
w = csv.writer(fp, delimiter="|")
|
||
w.writerows(data)
|
||
|
||
```
|
||
|
||
In [12]:
|
||
|
||
```py
|
||
!cat 'out.psv'
|
||
|
||
```
|
||
|
||
```py
|
||
"one, ""real"" string"|1|1.5
|
||
two|2|8.0
|
||
|
||
```
|
||
|
||
In [13]:
|
||
|
||
```py
|
||
import os
|
||
os.remove('out.psv')
|
||
os.remove('out.csv')
|
||
|
||
```
|
||
|
||
## 其他选项
|
||
|
||
`numpy.loadtxt()` 和 `pandas.read_csv()` 可以用来读写包含很多数值数据的 `csv` 文件：
|
||
|
||
In [14]:
|
||
|
||
```py
|
||
%%file trades.csv
|
||
Order,Date,Stock,Quantity,Price
|
||
A0001,2013-12-01,AAPL,1000,203.4
|
||
A0002,2013-12-01,MSFT,1500,167.5
|
||
A0003,2013-12-02,GOOG,1500,167.5
|
||
|
||
```
|
||
|
||
```py
|
||
Writing trades.csv
|
||
|
||
```
|
||
|
||
使用 `pandas` 进行处理，生成一个 `DataFrame` 对象：
|
||
|
||
In [15]:
|
||
|
||
```py
|
||
import pandas
|
||
df = pandas.read_csv('trades.csv', index_col=0)
|
||
print df
|
||
|
||
```
|
||
|
||
```py
|
||
Date Stock Quantity Price
|
||
Order
|
||
A0001 2013-12-01 AAPL 1000 203.4
|
||
A0002 2013-12-01 MSFT 1500 167.5
|
||
A0003 2013-12-02 GOOG 1500 167.5
|
||
|
||
```
|
||
|
||
通过名字进行索引：
|
||
|
||
In [16]:
|
||
|
||
```py
|
||
df['Quantity'] * df['Price']
|
||
|
||
```
|
||
|
||
Out[16]:
|
||
|
||
```py
|
||
Order
|
||
A0001 203400
|
||
A0002 251250
|
||
A0003 251250
|
||
dtype: float64
|
||
```
|
||
|
||
In [17]:
|
||
|
||
```py
|
||
import os
|
||
os.remove('trades.csv')
|
||
|
||
``` |