kopia lustrzana https://github.com/animator/learn-python
68 wiersze
3.1 KiB
Markdown
68 wiersze
3.1 KiB
Markdown
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# Loading Arrays From Files
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The ability to load data from various file formats is a critical feature for scientific computing and data analysis.
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NumPy provides several functions to read data from different file types and convert them into ndarrays.
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This section will cover how to load ndarrays from common file formats, including CSV, TSV, and binary files.
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### Here are the methods available:
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`numpy.loadtxt`: The loadtxt function allows you to load data from a text file.You can specify various parameters such as the file name, data type, delimiter,
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and more. It reads the file line by line, splits it at the specified delimiter, and converts the values into an array.
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- **Syntax:**
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```python
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numpy.loadtxt(fname, dtype = float, delimiter=None, converters=None, skiprows=0, usecols=None)
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```
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`fname` : Name of the file <br>
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`dtype` : Data type of the resulting array. (By default is float) <br>
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`delimiter`: String or character separating columns; default is any whitespace. <br>
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`converters`: Dictionary mapping column number to a function to convert that column's string to a float. <br>
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`skiprows`: Number of lines to skip at the beginning of the file. <br>
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`usecols`: Which columns to read starting from 0.
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- **Example for `loadtxt`:**
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**example.txt** <br>
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**Code** <br>
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```python
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import numpy as np
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arr = np.loadtxt("loadtxt.txt", dtype=int)
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print(arr)
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```
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**Output**<br>
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```python
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[1 2 3 4 5]
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```
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`numpy.genfromtxt`: The genfromtxt function is similar to loadtxt but provides more flexibility. It handles missing values (such as NaNs), allows custom converters
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for data parsing, and can handle different data types within the same file. It’s particularly useful for handling complex data formats.
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- **Syntax:**
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```python
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numpy.genfromtxt(fname, dtype=float, delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None)
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```
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`fname` : Name of the file <br>
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`dtype` : Data type of the resulting array. (By default is float) <br>
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`delimiter`: String or character separating columns; default is any whitespace. <br>
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`skip_header`: Number of lines to skip at the beginning of the file.<br>
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`skip_footer`: Number of lines to skip at the end of the file.<br>
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`converters`: Dictionary mapping column number to a function to convert that column's string to a float. <br>
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`missing_values`: Set of strings corresponding to missing data.<br>
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`filling_values`: Value used to fill in missing data. Default is NaN.<br>
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`usecols`: Which columns to read starting from 0.
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- **Examples for `genfromtxt`:**
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`numpy.fromfile`: The fromfile function reads binary data directly from a file into a NumPy array. It doesn’t assume any specific format or delimiter;
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instead, it interprets the raw binary data according to the specified data type.
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`numpy.load`: Load arrays saved in NumPy’s native binary format (.npy or .npz). These files preserve the array structure, data types, and metadata.
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It’s an efficient way to store and load large arrays.
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