The delimiter argument¶

Once the file is defined and open for reading, genfromtxt() splits each non-empty line into a sequence of strings. Empty or commented lines are just skipped. The delimiter keyword is used to define how the splitting should take place.

Quite often, a single character marks the separation between columns. For example, comma-separated files (CSV) use a comma (,) or a semicolon (;) as delimiter:

>>> data = "1, 2, 3\n4, 5, 6"
>>> np.genfromtxt(StringIO(data), delimiter=",")
array([[ 1.,  2.,  3.],
       [ 4.,  5.,  6.]])

Another common separator is "\t", the tabulation character. However, we are not limited to a single character, any string will do. By default, genfromtxt() assumes delimiter=None, meaning that the line is split along white spaces (including tabs) and that consecutive white spaces are considered as a single white space.

Alternatively, we may be dealing with a fixed-width file, where columns are defined as a given number of characters. In that case, we need to set delimiter to a single integer (if all the columns have the same size) or to a sequence of integers (if columns can have different sizes):

>>> data = "  1  2  3\n  4  5 67\n890123  4"
>>> 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(StringIO(data), delimiter=(4, 3, 2))
array([[ 1234.,   567.,    89.],
       [    4.,     7.,     9.],
       [    4.,   567.,     9.]])

The autostrip argument¶

By default, when a line is decomposed into a series of strings, the individual entries are not stripped of leading nor trailing white spaces. This behavior can be overwritten by setting the optional argument autostrip to a value of True:

>>> data = "1, abc , 2\n 3, xxx, 4"
>>> # Without autostrip
>>> np.genfromtxt(StringIO(data), dtype="|S5")
array([['1', ' abc ', ' 2'],
       ['3', ' xxx', ' 4']],
      dtype='|S5')
>>> # With autostrip
>>> np.genfromtxt(StringIO(data), dtype="|S5", autostrip=True)
array([['1', 'abc', '2'],
       ['3', 'xxx', '4']],
      dtype='|S5')

The comments argument¶

The optional argument comments is used to define a character string that marks the beginning of a comment. By default, genfromtxt() assumes comments='#'. The comment marker may occur anywhere on the line. Any character present after the comment marker(s) is simply ignored:

>>> data = """#
... # Skip me !
... # Skip me too !
... 1, 2
... 3, 4
... 5, 6 #This is the third line of the data
... 7, 8
... # And here comes the last line
... 9, 0
... """
>>> np.genfromtxt(StringIO(data), comments="#", delimiter=",")
[[ 1.  2.]
 [ 3.  4.]
 [ 5.  6.]
 [ 7.  8.]
 [ 9.  0.]]

The skip_header and skip_footer arguments¶

The presence of a header in the file can hinder data processing. In that case, we need to use the skip_header optional argument. The values of this argument must be an integer which corresponds to the number of lines to skip at the beginning of the file, before any other action is performed. Similarly, we can skip the last n lines of the file by using the skip_footer attribute and giving it a value of n:

>>> data = "\n".join(str(i) for i in range(10))
>>> np.genfromtxt(StringIO(data),)
array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])
>>> np.genfromtxt(StringIO(data),
...               skip_header=3, skip_footer=5)
array([ 3.,  4.])

By default, skip_header=0 and skip_footer=0, meaning that no lines are skipped.

The usecols argument¶

In some cases, we are not interested in all the columns of the data but only a few of them. We can select which columns to import with the usecols argument. This argument accepts a single integer or a sequence of integers corresponding to the indices of the columns to import. Remember that by convention, the first column has an index of 0. Negative integers behave the same as regular Python negative indexes.

For example, if we want to import only the first and the last columns, we can use usecols=(0, -1):

>>> data = "1 2 3\n4 5 6"
>>> np.genfromtxt(StringIO(data), usecols=(0, -1))
array([[ 1.,  3.],
       [ 4.,  6.]])

If the columns have names, we can also select which columns to import by giving their name to the usecols argument, either as a sequence of strings or a comma-separated string:

>>> data = "1 2 3\n4 5 6"
>>> 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(StringIO(data),
...               names="a, b, c", usecols=("a, c"))
    array([(1.0, 3.0), (4.0, 6.0)],
          dtype=[('a', '<f8'), ('c', '<f8')])

The names argument¶

A natural approach when dealing with tabular data is to allocate a name to each column. A first possibility is to use an explicit structured dtype, as mentioned previously:

>>> 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')])

Another simpler possibility is to use the names keyword with a sequence of strings or a comma-separated string:

>>> 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')])

In the example above, we used the fact that by default, dtype=float. By giving a sequence of names, we are forcing the output to a structured dtype.

We may sometimes need to define the column names from the data itself. In that case, we must use the names keyword with a value of True. The names will then be read from the first line (after the skip_header ones), even if the line is commented out:

>>> 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')])

The default value of names is None. If we give any other value to the keyword, the new names will overwrite the field names we may have defined with the dtype:

>>> 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)
array([(1, 2.0, 3), (4, 5.0, 6)],
      dtype=[('A', '<i8'), ('B', '<f8'), ('C', '<i8')])

The defaultfmt argument¶

If names=None but a structured dtype is expected, names are defined with the standard NumPy default of "f%i", yielding names like f0, f1 and so forth:

>>> 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')])

In the same way, if we don’t give enough names to match the length of the dtype, the missing names will be defined with this default template:

>>> 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')])

We can overwrite this default with the defaultfmt argument, that takes any format string:

>>> 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')])

Validating names¶

Numpy arrays with a structured dtype can also be viewed as recarray, where a field can be accessed as if it were an attribute. For that reason, we may need to make sure that the field name doesn’t contain any space or invalid character, or that it does not correspond to the name of a standard attribute (like size or shape), which would confuse the interpreter. genfromtxt() accepts three optional arguments that provide a finer control on the names:

deletechars

Gives a string combining all the characters that must be deleted from the name. By default, invalid characters are ~!@#$%^&*()-=+~\|]}[{';: /?.>,<.

excludelist

Gives a list of the names to exclude, such as return, file, print... If one of the input name is part of this list, an underscore character ('_') will be appended to it.

case_sensitive

Whether the names should be case-sensitive (case_sensitive=True), converted to upper case (case_sensitive=False or case_sensitive='upper') or to lower case (case_sensitive='lower').

The converters argument¶

Usually, defining a dtype is sufficient to define how the sequence of strings must be converted. However, some additional control may sometimes be required. For example, we may want to make sure that a date in a format YYYY/MM/DD is converted to a datetime object, or that a string like xx% is properly converted to a float between 0 and 1. In such cases, we should define conversion functions with the converters arguments.

The value of this argument is typically a dictionary with column indices or column names as keys and a conversion functions as values. These conversion functions can either be actual functions or lambda functions. In any case, they should accept only a string as input and output only a single element of the wanted type.

In the following example, the second column is converted from as string representing a percentage to a float between 0 and 1:

>>> convertfunc = lambda x: float(x.strip("%"))/100.
>>> data = "1, 2.3%, 45.\n6, 78.9%, 0"
>>> names = ("i", "p", "n")
>>> # General case .....
>>> 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')])

We need to keep in mind that by default, dtype=float. A float is therefore expected for the second column. However, the strings ' 2.3%' and ' 78.9%' cannot be converted to float and we end up having np.nan instead. Let’s now use a converter:

>>> # Converted case ...
>>> 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')])

The same results can be obtained by using the name of the second column ("p") as key instead of its index (1):

>>> # Using a name for the converter ...
>>> 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')])

Converters can also be used to provide a default for missing entries. In the following example, the converter convert transforms a stripped string into the corresponding float or into -999 if the string is empty. We need to explicitly strip the string from white spaces as it is not done by default:

>>> data = "1, , 3\n 4, 5, 6"
>>> convert = lambda x: float(x.strip() or -999)
>>> np.genfromtxt(StringIO(data), delimiter=",",
...               converter={1: convert})
array([[   1., -999.,    3.],
       [   4.,    5.,    6.]])

Using missing and filling values¶

Some entries may be missing in the dataset we are trying to import. In a previous example, we used a converter to transform an empty string into a float. However, user-defined converters may rapidly become cumbersome to manage.

The genfromtxt() function provides two other complementary mechanisms: the missing_values argument is used to recognize missing data and a second argument, filling_values, is used to process these missing data.

missing_values¶

By default, any empty string is marked as missing. We can also consider more complex strings, such as "N/A" or "???" to represent missing or invalid data. The missing_values argument accepts three kind of values:

a string or a comma-separated string

This string will be used as the marker for missing data for all the columns

a sequence of strings

In that case, each item is associated to a column, in order.

a dictionary

Values of the dictionary are strings or sequence of strings. The corresponding keys can be column indices (integers) or column names (strings). In addition, the special key None can be used to define a default applicable to all columns.

filling_values¶

We know how to recognize missing data, but we still need to provide a value for these missing entries. By default, this value is determined from the expected dtype according to this table:

We can get a finer control on the conversion of missing values with the filling_values optional argument. Like missing_values, this argument accepts different kind of values:

a single value

This will be the default for all columns

a sequence of values

Each entry will be the default for the corresponding column

a dictionary

Each key can be a column index or a column name, and the corresponding value should be a single object. We can use the special key None to define a default for all columns.

In the following example, we suppose that the missing values are flagged with "N/A" in the first column and by "???" in the third column. We wish to transform these missing values to 0 if they occur in the first and second column, and to -999 if they occur in the last column:

>>> data = "N/A, 2, 3\n4, ,???"
>>> kwargs = dict(delimiter=",",
...               dtype=int,
...               names="a,b,c",
...               missing_values={0:"N/A", 'b':" ", 2:"???"},
...               filling_values={0:0, 'b':0, 2:-999})
>>> np.genfromtxt(StringIO.StringIO(data), **kwargs)
array([(0, 2, 3), (4, 0, -999)],
      dtype=[('a', '<i8'), ('b', '<i8'), ('c', '<i8')])

usemask¶

We may also want to keep track of the occurrence of missing data by constructing a boolean mask, with True entries where data was missing and False otherwise. To do that, we just have to set the optional argument usemask to True (the default is False). The output array will then be a MaskedArray.