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48 changes: 24 additions & 24 deletions docs/user/basics/io.md
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@ In a nutshell, ``genfromtxt`` runs two main loops. 第一个循环以字符串

```python
>>> import numpy as np
>>> from io import BytesIO
>>> from io import StringIO
```

### 定义输入
Expand All @@ -29,7 +29,7 @@ In a nutshell, ``genfromtxt`` runs two main loops. 第一个循环以字符串

```python
>>> data = "1, 2, 3\n4, 5, 6"
>>> np.genfromtxt(BytesIO(data), delimiter=",")
>>> np.genfromtxt(StringIO(data), delimiter=",")
array([[ 1., 2., 3.],
[ 4., 5., 6.]])
```
Expand All @@ -40,12 +40,12 @@ array([[ 1., 2., 3.],

```python
>>> data = " 1 2 3\n 4 5 67\n890123 4"
>>> np.genfromtxt(BytesIO(data), delimiter=3)
>>> np.genfromtxt(StringIO(data), delimiter=3)
array([[ 1., 2., 3.],
[ 4., 5., 67.],
[ 890., 123., 4.]])
>>> data = "123456789\n 4 7 9\n 4567 9"
>>> np.genfromtxt(BytesIO(data), delimiter=(4, 3, 2))
>>> np.genfromtxt(StringIO(data), delimiter=(4, 3, 2))
array([[ 1234., 567., 89.],
[ 4., 7., 9.],
[ 4., 567., 9.]])
Expand All @@ -58,12 +58,12 @@ array([[ 1234., 567., 89.],
```python
>>> data = "1, abc , 2\n 3, xxx, 4"
>>> # Without autostrip
>>> np.genfromtxt(BytesIO(data), delimiter=",", dtype="|S5")
>>> np.genfromtxt(StringIO(data), delimiter=",", dtype="|S5")
array([['1', ' abc ', ' 2'],
['3', ' xxx', ' 4']],
dtype='|S5')
>>> # With autostrip
>>> np.genfromtxt(BytesIO(data), delimiter=",", dtype="|S5", autostrip=True)
>>> np.genfromtxt(StringIO(data), delimiter=",", dtype="|S5", autostrip=True)
array([['1', 'abc', '2'],
['3', 'xxx', '4']],
dtype='|S5')
Expand All @@ -84,7 +84,7 @@ array([['1', 'abc', '2'],
... # And here comes the last line
... 9, 0
... """
>>> np.genfromtxt(BytesIO(data), comments="#", delimiter=",")
>>> np.genfromtxt(StringIO(data), comments="#", delimiter=",")
[[ 1. 2.]
[ 3. 4.]
[ 5. 6.]
Expand All @@ -106,9 +106,9 @@ array([['1', 'abc', '2'],

```python
>>> data = "\n".join(str(i) for i in range(10))
>>> np.genfromtxt(BytesIO(data),)
>>> np.genfromtxt(StringIO(data),)
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
>>> np.genfromtxt(BytesIO(data),
>>> np.genfromtxt(StringIO(data),
... skip_header=3, skip_footer=5)
array([ 3., 4.])
```
Expand All @@ -123,7 +123,7 @@ array([ 3., 4.])

```python
>>> data = "1 2 3\n4 5 6"
>>> np.genfromtxt(BytesIO(data), usecols=(0, -1))
>>> np.genfromtxt(StringIO(data), usecols=(0, -1))
array([[ 1., 3.],
[ 4., 6.]])
```
Expand All @@ -132,11 +132,11 @@ array([[ 1., 3.],

```python
>>> data = "1 2 3\n4 5 6"
>>> np.genfromtxt(BytesIO(data),
>>> np.genfromtxt(StringIO(data),
... names="a, b, c", usecols=("a", "c"))
array([(1.0, 3.0), (4.0, 6.0)],
dtype=[('a', '<f8'), ('c', '<f8')])
>>> np.genfromtxt(BytesIO(data),
>>> np.genfromtxt(StringIO(data),
... names="a, b, c", usecols=("a, c"))
array([(1.0, 3.0), (4.0, 6.0)],
dtype=[('a', '<f8'), ('c', '<f8')])
Expand Down Expand Up @@ -167,7 +167,7 @@ array([(1.0, 3.0), (4.0, 6.0)],
处理表格数据时的一种自然方法是为每列分配一个名称。如前所述，第一种可能性是使用明确的结构化dtype。

```python
>>> data = BytesIO("1 2 3\n 4 5 6")
>>> data = StringIO("1 2 3\n 4 5 6")
>>> np.genfromtxt(data, dtype=[(_, int) for _ in "abc"])
array([(1, 2, 3), (4, 5, 6)],
dtype=[('a', '<i8'), ('b', '<i8'), ('c', '<i8')])
Expand All @@ -176,7 +176,7 @@ array([(1, 2, 3), (4, 5, 6)],
另一种更简单的可能性是将``names``关键字与一系列字符串或逗号分隔的字符串一起使用：

```python
>>> data = BytesIO("1 2 3\n 4 5 6")
>>> data = StringIO("1 2 3\n 4 5 6")
>>> np.genfromtxt(data, names="A, B, C")
array([(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)],
dtype=[('A', '<f8'), ('B', '<f8'), ('C', '<f8')])
Expand All @@ -187,7 +187,7 @@ array([(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)],
我们有时可能需要从数据本身定义列名。在这种情况下，我们必须使用``names``关键字的值为``True``。这些名字将从第一行（在``skip_header``之后）被读取，即使该行被注释掉：

```python
>>> data = BytesIO("So it goes\n#a b c\n1 2 3\n 4 5 6")
>>> data = StringIO("So it goes\n#a b c\n1 2 3\n 4 5 6")
>>> np.genfromtxt(data, skip_header=1, names=True)
array([(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)],
dtype=[('a', '<f8'), ('b', '<f8'), ('c', '<f8')])
Expand All @@ -196,7 +196,7 @@ array([(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)],
``names``的默认值为``None``。如果我们给关键字赋予任何其他值，新名称将覆盖我们可能用dtype定义的字段名称：

```python
>>> data = BytesIO("1 2 3\n 4 5 6")
>>> data = StringIO("1 2 3\n 4 5 6")
>>> ndtype=[('a',int), ('b', float), ('c', int)]
>>> names = ["A", "B", "C"]
>>> np.genfromtxt(data, names=names, dtype=ndtype)
Expand All @@ -209,7 +209,7 @@ array([(1, 2.0, 3), (4, 5.0, 6)],
如果 ``names=None`` 的时候，只是预计会有一个结构化的dtype，它的名称将使用标准的NumPy默认值 ``"f%i"``来定义，会产生例如``f0``，``f1``等名称：

```python
>>> data = BytesIO("1 2 3\n 4 5 6")
>>> data = StringIO("1 2 3\n 4 5 6")
>>> np.genfromtxt(data, dtype=(int, float, int))
array([(1, 2.0, 3), (4, 5.0, 6)],
dtype=[('f0', '<i8'), ('f1', '<f8'), ('f2', '<i8')])
Expand All @@ -218,7 +218,7 @@ array([(1, 2.0, 3), (4, 5.0, 6)],
同样，如果我们没有提供足够的名称来匹配dtype的长度，缺少的名称将使用此默认模板进行定义：

```python
>>> data = BytesIO("1 2 3\n 4 5 6")
>>> data = StringIO("1 2 3\n 4 5 6")
>>> np.genfromtxt(data, dtype=(int, float, int), names="a")
array([(1, 2.0, 3), (4, 5.0, 6)],
dtype=[('a', '<i8'), ('f0', '<f8'), ('f1', '<i8')])
Expand All @@ -227,7 +227,7 @@ array([(1, 2.0, 3), (4, 5.0, 6)],
我们可以使用``defaultfmt``参数覆盖此默认值，该参数采用任何格式字符串：

```python
>>> data = BytesIO("1 2 3\n 4 5 6")
>>> data = StringIO("1 2 3\n 4 5 6")
>>> np.genfromtxt(data, dtype=(int, float, int), defaultfmt="var_%02i")
array([(1, 2.0, 3), (4, 5.0, 6)],
dtype=[('var_00', '<i8'), ('var_01', '<f8'), ('var_02', '<i8')])
Expand Down Expand Up @@ -259,7 +259,7 @@ array([(1, 2.0, 3), (4, 5.0, 6)],
>>> data = "1, 2.3%, 45.\n6, 78.9%, 0"
>>> names = ("i", "p", "n")
>>> # General case .....
>>> np.genfromtxt(BytesIO(data), delimiter=",", names=names)
>>> np.genfromtxt(StringIO(data), delimiter=",", names=names)
array([(1.0, nan, 45.0), (6.0, nan, 0.0)],
dtype=[('i', '<f8'), ('p', '<f8'), ('n', '<f8')])
```
Expand All @@ -268,7 +268,7 @@ array([(1.0, nan, 45.0), (6.0, nan, 0.0)],

```python
>>> # Converted case ...
>>> np.genfromtxt(BytesIO(data), delimiter=",", names=names,
>>> np.genfromtxt(StringIO(data), delimiter=",", names=names,
... converters={1: convertfunc})
array([(1.0, 0.023, 45.0), (6.0, 0.78900000000000003, 0.0)],
dtype=[('i', '<f8'), ('p', '<f8'), ('n', '<f8')])
Expand All @@ -278,7 +278,7 @@ array([(1.0, 0.023, 45.0), (6.0, 0.78900000000000003, 0.0)],

```python
>>> # Using a name for the converter ...
>>> np.genfromtxt(BytesIO(data), delimiter=",", names=names,
>>> np.genfromtxt(StringIO(data), delimiter=",", names=names,
... converters={"p": convertfunc})
array([(1.0, 0.023, 45.0), (6.0, 0.78900000000000003, 0.0)],
dtype=[('i', '<f8'), ('p', '<f8'), ('n', '<f8')])
Expand All @@ -289,7 +289,7 @@ array([(1.0, 0.023, 45.0), (6.0, 0.78900000000000003, 0.0)],
```python
>>> data = "1, , 3\n 4, 5, 6"
>>> convert = lambda x: float(x.strip() or -999)
>>> np.genfromtxt(BytesIO(data), delimiter=",",
>>> np.genfromtxt(StringIO(data), delimiter=",",
... converters={1: convert})
array([[ 1., -999., 3.],
[ 4., 5., 6.]])
Expand Down Expand Up @@ -338,7 +338,7 @@ array([[ 1., -999., 3.],
... names="a,b,c",
... missing_values={0:"N/A", 'b':" ", 2:"???"},
... filling_values={0:0, 'b':0, 2:-999})
>>> np.genfromtxt(BytesIO(data), **kwargs)
>>> np.genfromtxt(StringIO(data), **kwargs)
array([(0, 2, 3), (4, 0, -999)],
dtype=[('a', '<i8'), ('b', '<i8'), ('c', '<i8')])
```
Expand Down