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.
Note
When giving examples, we will use the following conventions:
>>> import numpy as np
>>> from io import BytesIO
Defining the input¶
The only mandatory argument of genfromtxt
is the source of
the data. It can be a string, a list of strings, or a generator. If a
single string is provided, it is assumed to be the name of a local or
remote file, or an open file-like object with a read
method, for
example, a file or StringIO.StringIO
object. If a list of strings
or a generator returning strings is provided, each string is treated as one
line in a file. When the URL of a remote file is passed, the file is
automatically downloaded to the current directory and opened.
Recognized file types are text files and archives. Currently, the function
recognizes gzip
and bz2
(bzip2) archives. The type of
the archive is determined from 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(BytesIO(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(BytesIO(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))
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(BytesIO(data), delimiter=",", dtype="|U5")
array([['1', ' abc ', ' 2'],
['3', ' xxx', ' 4']],
dtype='|U5')
>>> # With autostrip
>>> np.genfromtxt(BytesIO(data), delimiter=",", dtype="|U5", autostrip=True)
array([['1', 'abc', '2'],
['3', 'xxx', '4']],
dtype='|U5')
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(BytesIO(data), comments="#", delimiter=",")
[[ 1. 2.]
[ 3. 4.]
[ 5. 6.]
[ 7. 8.]
[ 9. 0.]]
Note
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 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(BytesIO(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(BytesIO(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),
... names="a, b, c", usecols=("a, c"))
array([(1.0, 3.0), (4.0, 6.0)],
dtype=[('a', '<f8'), ('c', '<f8')])
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 thenames
argument (see below). Note thatdtype=float
is the default forgenfromtxt
. - a sequence of types, such as
dtype=(int, float, float)
. - a comma-separated string, such as
dtype="i4,f8,|U3"
. - a dictionary with two keys
'names'
and'formats'
. - a sequence of tuples
(name, type)
, such asdtype=[('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 = BytesIO("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 = BytesIO("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 = BytesIO("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 = BytesIO("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 = BytesIO("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 = BytesIO("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 = BytesIO("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')])
Note
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:
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
,'_'
) will be appended to it.case_sensitive
- Whether the names should be case-sensitive (
case_sensitive=True
), converted to upper case (case_sensitive=False
orcase_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 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(BytesIO(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(BytesIO(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(BytesIO(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(BytesIO(data), delimiter=",",
... converters={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:
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,
... names="a,b,c",
... missing_values={0:"N/A", 'b':" ", 2:"???"},
... filling_values={0:0, 'b':0, 2:-999})
>>> np.genfromtxt(BytesIO(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
.
Shortcut functions¶
In addition to genfromtxt
, the numpy.lib.io
module
provides several convenience functions derived from
genfromtxt
. These functions work the same way as the
original, but they have different default values.
ndfromtxt
- Always set
usemask=False
. The output is always a standardnumpy.ndarray
. mafromtxt
- Always set
usemask=True
. The output is always aMaskedArray
recfromtxt
- Returns a standard
numpy.recarray
(ifusemask=False
) or aMaskedRecords
array (ifusemaske=True
). The default dtype isdtype=None
, meaning that the types of each column will be automatically determined. recfromcsv
- Like
recfromtxt
, but with a defaultdelimiter=","
.