Numpy genfromtxt() | How to use Numpy genfromtxt()
Numpy is the name that came from ‘Numerical Python.’ It is a library in python which performs n-dimensional operations on arrays. But have you ever thought about loading the data into numpy from the text files? We can do this with two function i.e. numpy.genfromtxt() and numpy.loadtxt(). In this tutorial, we will be studying numpy genfromtxt().
What is numpy genfromtxt() ?
We use Numpy genfromtxt() to load the data from the text files, with missing values handled as specified.
Syntax
Parameters
- fname: It is the file, filename, list, string, list of string, or generator to read. If the filename is with the extension gz or bz2, then the file is decompressed. Note: that generator should always return byte strings. Strings in the list are treated as lines.
- dtype: It is the data type of the resulting array, which is the optional input. If we set type to None, then the dtypes will be determined by each column’s contents individually.
- comment: optional. We use this character to indicate the start of the comment. The characters occurring in a line after a comment is discarded.
- delimiter: optional. This is the string used to separate the values by default, any consecutive whitespace that occurs acts as a delimiter.
- skip_header: optional. This is the number of lines that we have to skip from the beginning of the file.
- skip_footer: optional. This is the number of lines that we have to skip from the end of the file.
- skip_rows: optional. It was removed in numpy 1.10. So. instead of it, we can use skip_header.
- converters: optional. It is the set function that converts the data of a column to a value. The converters are also be used to provide a default value for missing data: converters = <3: lambda s: float(s or 0)>.
- missing: optional. It was removed in numpy 1.10. So. instead of it, we can use missing_values.
- missing_values: optional. These are the set of strings corresponding to missing data.
- filling_values: optional. These are the set of values to be used as default when the data are missing.
- usecols: optional. This tells us about which column to be read; 0 is being the first.
- names: optional. If names are set to True, then the field names are read from the first line after the first skip_header lines. A comment delimiter can optionally begin this line. If names are None, the names of the dtype fields will be used.
- excludelist: optional. It is a sequence of lists of names to exclude. This list gets appended to the default list.
- deletechars: optional. If a string is combining invalid characters, that must be deleted from the names.
- replace_space: optional. These are the Characters that are used in the replacement of white spaces in the variables names. By default, it uses a ‘_.’
- autostrip: optional. This tells whether to strip white spaces from the variables automatically.
- defaultfmt: optional. This format defines default field names.
- unpack: optional.
- case_sensitive: optional. If set to True, field names are case sensitive, and if False or ‘upper,’ field names are converted to uppercase. If ‘lower,’ they are converted to lowercase.
- usemask: optional. It is a boolean value. If set to True, it returns a masked array. Else it will return a regular array.
- loose: optional. It is a boolean value. If set to True, it does not raise an error for invalid values.
- invalid_raise: optional. If we set it to True, then an exception is raised if an inconsistency is detected in the number of columns. Otherwise, a warning is emitted, and the offending lines are skipped.
- max_rows: optional. It tells about the maximum number of rows to be read. we cannot use it with the skip_footer parameter at the same time. By default, it reads the entire file.
- encoding: optional. This is used to decode the input file. It does not allow when the filename is the file object.
- like: This is the reference object to allow the creation of arrays that are not NumPy arrays.
Return value of numpy genfromtxt()
The function gives the return value as an array. In this, data is read from the text file. If we have set usemask to True, then it is a masked array.
Examples of numpy genfromtxt()
Let us understand numpy genfromtxt() with all the parameters with the help of examples:
1. Using str, dtype, encoding and delimiter as a parameter
In this example, we will be importing 2 libraries from python, i.e., numpy and StringIO. Then, we will take an input string in the form of a list and apply it with the given parameter and see the output.
Output:
Explanation:
Firstly, we have imported two libraries, i.e., numpy with an alias name as np and from io import StringIO. Secondly, we have taken an input string in str. Finally, we have applied the genfromtxt() function in which we have given some str, dtype, encoding, and delimiter and printed the output. Hence, you can see the output.
2. Using skip_header and skip_footer
In this example, we will write a file. We will import the numpy library as an alias name np. then, we will apply the function and write the file name in the function with the other parameters.
Output:
Explanation:
Here, we have taken a text file with the name Latra.txt in which we have written some content. Then, we have imported the numpy library. We have then applied the genfromtxt() function in which we have given the text filename, dtype, encoding, skip header, and skip footer, which will skip the first and last line from and print the lines containing in the file. Hence, you can see the output.
3. showing comments in numpy genfromtxt()
In this example, we will be showing how comments work in the file. For this, we will import two libraries, numpy, and StringIO, and then taken input. After that, we will apply the numpy genfromtxt() function and see the output.
Output:
Explanation:
Here, we will be showing that how comments work in the files. For this, we will import the numpy and StringIO library. Then, we will take an input string f in which we comment with the # symbol. Then, we will apply the genfromtxt() function. Finally, we will print the output and see after the # symbol line gets removed and not printed in the output array.
4. Using autostrip in numpy genfromtxt()
In this example, we will import numpy and StringIO library. Then we will take the input as data and apply the function with the parameters and print the output with and without the autostrip parameter. Hence, we can see the difference with and without the autostrip parameter.
Output:
Explanation:
Here, we have imported two libraries, numpy, and StringIO. We have taken input in the data string. Then, we will apply the genfromtxt() function with its parameters. In this, firstly, we will print the output without the ‘autostrip’ parameter, and after that, we will print the output with the ‘autostrip’ parameter. Hence, we can see the output.
Difference between Genfromtxt() and loadtxt()
Numpy Genfromtxt()
We use Numpy genfromtxt() to load the data from the text files, with missing values handled as specified.
Numpy Loadtxt()
We use Numpy loadtxt() to load the data from the text files, with the aim to be a fast reader for simple text files.
Example of genfromtxt() and loadtxt()
In this example, we will be using both the function simultaneously and observe the difference between them just by seeing the output and their definition.
Output:
Explanation:
Here firstly, we have imported the numpy library as np and also imported the StringIO library. Secondly, we have taken input and applied the loadtxt() function, and printed the output. Thirdly, we have taken an input as d and applied a genfromtxt() function and printed the output. Hence, you can see the output.
Conclusion
In this tutorial, we have learned about how to use the numpy genfromtxt() function. We have explained the concept in detail by taking all its parameters in the example. We have explained all the examples in detail so that you understand every parameter in deep. Hence, you can use the function and its parameters according to your need.
NumPy Input and Output: genfromtxt() function
The genfromtxt() used to load data from a text file, with missing values handled as specified.
Each line past the first skip_header lines is split at the delimiter character, and characters following the comments character are discarded.
Syntax:
Version: 1.15.0
Parameter:
Name | Description | Required / Optional |
---|---|---|
fname | File, filename, list, or generator to read. If the filename extension is gz or bz2, the file is first decompressed. Note that generators must return byte strings in Python 3k. The strings in a list or produced by a generator are treated as lines. file, str, pathlib.Path, list of str, generator |
Required |
dtype | Data type of the resulting array. If None, the dtypes will be determined by the contents of each column, individually. dtype |
Optional |
comments | The character used to indicate the start of a comment. All the characters occurring on a line after a comment are discarded str |
Optional |
delimiter | The string used to separate values. By default, any consecutive whitespaces act as delimiter. An integer or sequence of integers can also be provided as width(s) of each field. str, int, or sequence |
Optional |
skiprows | skiprows was removed in numpy 1.10. Please use skip_header instead. int |
Optional |
skip_header | The number of lines to skip at the beginning of the file. int |
Optional |
skip_footer | The number of lines to skip at the end of the file. int |
Optional |
converters | The set of functions that convert the data of a column to a value. The converters can also be used to provide a default value for missing data: converters = <3: lambda s: float(s or 0)>. variable |
Optional |
missing | missing was removed in numpy 1.10. Please use missing_values instead. variable |
Optional |
missing_values | The set of strings corresponding to missing data. variable |
Optional |
filling_values | The set of values to be used as default when the data are missing. variable |
Optional |
usecols | Which columns to read, with 0 being the first. For example, usecols = (1, 4, 5) will extract the 2nd, 5th and 6th columns. sequence |
Optional |
names | If names is True, the field names are read from the first valid line after the first skip_header lines. If names is a sequence or a single-string of comma-separated names, the names will be used to define the field names in a structured dtype. If names is None, the names of the dtype fields will be used, if any. | Optional |
excludelist | A list of names to exclude. This list is appended to the default list [‘return’,’file’,’print’]. Excluded names are appended an underscore: for example, file would become file_. sequence |
Optional |
deletechars | A string combining invalid characters that must be deleted from the names. str |
Optional |
defaultfmt | A format used to define default field names, such as «f%i» or «f_%02i». str |
Optional |
autostrip | Whether to automatically strip white spaces from the variables. bool |
Optional |
replace_space | Character(s) used in replacement of white spaces in the variables names. By default, use a ‘_’. char |
Optional |
case_sensitive | If True, field names are case sensitive. If False or ‘upper’, field names are converted to upper case. If ‘lower’, field names are converted to lower case. | Optional |
unpack | If True, the returned array is transposed, so that arguments may be unpacked using x, y, z = loadtxt(. ) bool |
Optional |
usemask | If True, return a masked array. If False, return a regular array. bool |
Optional |
loose | If True, do not raise errors for invalid values. bool |
Optional |
invalid_raise | If True, an exception is raised if an inconsistency is detected in the number of columns. If False, a warning is emitted and the offending lines are skipped. bool |
Optional |
max_rows | The maximum number of rows to read. Must not be used with skip_footer at the same time. If given, the value must be at least 1. Default is to read the entire file. int |
Optional |
encoding | Encoding used to decode the inputfile. Does not apply when fname is a file object. The special value ‘bytes’ enables backward compatibility workarounds that ensure that you receive byte arrays when possible and passes latin1 encoded strings to converters. Override this value to receive unicode arrays and pass strings as input to converters. If set to None the system default is used. The default value is ‘bytes’. str |
Optional |
Returns: out : ndarray
Data read from the text file. If usemask is True, this is a masked array.
Notes:
- When spaces are used as delimiters, or when no delimiter has been given as input, there should not be any missing data between two fields.
- When the variables are named (either by a flexible dtype or with names, there must not be any header in the file (else a ValueError exception is raised).
- Individual values are not stripped of spaces by default. When using a custom converter, make sure the function does remove spaces.
NumPy.genfromtxt() method Example-1:
Comma delimited file with mixed dtype
NumPy.genfromtxt() method Example-2:
Using dtype = None
NumPy.genfromtxt() method Example-3:
Specifying dtype and names
NumPy.genfromtxt() method Example-4:
An example with fixed-width columns
Python — NumPy Code Editor:
Follow us on Facebook and Twitter for latest update.
- Weekly Trends
We are closing our Disqus commenting system for some maintenanace issues. You may write to us at reach[at]yahoo[dot]com or visit us at Facebook