Importing data with genfromtxt

Numpy provides several functions to create arrays from tabular data. We focus here on the genfromtxt function.

In a nutshell, genfromtxt runs two main loops. The first loop converts each line of the file in a sequence of strings. The second loop converts each string to the appropriate data type. This mechanism is slower than a single loop, but gives more flexibility. In particular, genfromtxt is able to take missing data into account, when other faster and simpler functions like loadtxt cannot


When giving examples, we will use the following conventions

>>> import numpy as np
>>> from StringIO import StringIO

Defining the input

The only mandatory argument of genfromtxt is the source of the data. It can be a string corresponding to the name of a local or remote file, or a file-like object with a read method (such as an actual file or a StringIO.StringIO object). If the argument is the URL of a remote file, this latter is automatically downloaded in the current directory.

The input file can be a text file or an archive. Currently, the function recognizes gzip and bz2 (bzip2) archives. The type of the archive is determined by examining the extension of the file: if the filename ends with '.gz', a gzip archive is expected; if it ends with 'bz2', a bzip2 archive is assumed.

Splitting the lines into columns

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=",")
       [ 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)
       [   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']],
>>> # With autostrip
>>> np.genfromtxt(StringIO(data), dtype="|S5", autostrip=True)

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.]]


There is one notable exception to this behavior: if the optional argument names=True, the first commented line will be examined for names.

Skipping lines and choosing columns

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 correspond to

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.
>>> 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"))

Choosing the data type

The main way to control how the sequences of strings we have read from the file are converted to other types is to set the dtype argument. Acceptable values for this argument are:

  • a single type, such as dtype=float. The output will be 2D with the given dtype, unless a name has been associated with each column with the use of the names argument (see below). Note that dtype=float is the default for genfromtxt.
  • a sequence of types, such as dtype=(int, float, float).
  • a comma-separated string, such as dtype="i4,f8,|S3".
  • a dictionary with two keys 'names' and 'formats'.
  • a sequence of tuples (name, type), such as dtype=[('A', int), ('B', float)].
  • an existing numpy.dtype object.
  • the special value None. In that case, the type of the columns will be determined from the data itself (see below).

In all the cases but the first one, the output will be a 1D array with a structured dtype. This dtype has as many fields as items in the sequence. The field names are defined with the names keyword.

When dtype=None, the type of each column is determined iteratively from its data. We start by checking whether a string can be converted to a boolean (that is, if the string matches true or false in lower cases); then whether it can be converted to an integer, then to a float, then to a complex and eventually to a string. This behavior may be changed by modifying the default mapper of the StringConverter class.

The option dtype=None is provided for convenience. However, it is significantly slower than setting the dtype explicitly.

Setting the names

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.
>>> 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)

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.

>>> 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.
>>> np.genfromtxt(data, dtype=(int, float, int))
      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.
>>> np.genfromtxt(data, dtype=(int, float, int), names="a")
      dtype=[('a', '<i8'), ('f0', '<f8'), ('f1', '<i8')])
We can overwrite this default with the defaultfmt argument, that takes any format string:
>>> np.genfromtxt(data, dtype=(int, float, int), defaultfmt="var_%02i")
      dtype=[('var_00', '<i8'), ('var_01', '<f8'), ('var_02', '<i8')])


We need to keep in mind that defaultfmt is used only if some names are expected but not defined.

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:

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

Tweaking the conversion

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 function 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 ...
...               converters={1: convertfunc})
array([(1.0, 0.023, 45.0), (6.0, 0.78900000000000003, 0.0)],

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 ...
...               converters={"p": convertfunc})

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)
...               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.


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.


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:

Expected type Default
bool False
int -1
float np.nan
complex np.nan+0j
string '???'

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,
...               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)],


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.

Shortcut functions

In addition to genfromtxt, the module provides several convenience functions derived from genfromtxt. These functions work the same way as the original, but they have different default values.

Always set usemask=False. The output is always a standard numpy.ndarray.
Always set usemask=True. The output is always a MaskedArray
Returns a standard numpy.recarray (if usemask=False) or a MaskedRecords array (if usemaske=True). The default dtype is dtype=None, meaning that the types of each column will be automatically determined.
Like recfromtxt, but with a default delimiter=",".