如何使用 numpy 在Python读取数字数据或文件？

先决条件： Numpy

NumPy 是一个通用的数组处理包。它提供了一个高性能的多维数组对象和用于处理这些数组的工具。本文描述了如何使用 Numpy 从文件中读取数字数据。

数字数据可以以不同格式的文件存在：

• 数据可以保存在 txt 文件中，其中每行都有一个新的数据点。
• 数据可以存储在 CSV（逗号分隔值）文件中。
• 数据也可以存储在 TSV（制表符分隔值）文件中。

有多种在文件中存储数据的方式，以上是一些最常用的存储数值数据的格式。为了实现我们所需的函数，将使用 numpy 的 loadtxt()函数。

Syntax: numpy.loadtxt(fname, dtype=’float’, comments=’#’, delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)

Parameters:
fname : File, filename, or generator to read. If the filename extension is .gz or .bz2, the file is first decompressed. Note that generators should return byte strings for Python 3k.
dtype : Data-type of the resulting array; default: float. If this is a structured data-type, the resulting array will be 1-dimensional, and each row will be interpreted as an element of the array.
delimiter : The string used to separate values. By default, this is any whitespace.
converters : A dictionary mapping column number to a function that will convert that column to a float. E.g., if column 0 is a date string: converters = {0: datestr2num}. Default: None.
skiprows : Skip the first skiprows lines; default: 0.

Returns: ndarray

方法

• 导入模块
• 加载文件
• 读取数值数据
• 检索到的打印数据。

下面给出了各种文件格式的一些实现：

所用数据文件的下载链接：

• 链接1 ：gfg_example1.txt
• 链接2 ：gfg_example2.csv
• 链接3 ：gfg_example3.tsv
• 链接4 ：gfg_example4.csv

示例 1：从文本文件中读取数值数据

Python3

# Importing libraries that will be used
import numpy as np
 
# Setting name of the file that the data is to be extracted from in python
filename = 'gfg_example1.txt'
 
# Loading file data into numpy array and storing it in variable called data_collected
data_collected = np.loadtxt(filename)
 
# Printing data stored
print(data_collected)
 
 
# Type of data
print(
    f'Stored in : {type(data_collected)} and data type is : {data_collected.dtype}')

输出 ：

示例 1 的输出

示例 2：从 CSV 文件中读取数值数据。

蟒蛇3

# Importing libraries that will be used
import numpy as np
 
# Setting name of the file that the data is to be extracted from in python
# This is a comma separated values file
filename = 'gfg_example2.csv'
 
# Loading file data into numpy array and storing it in variable.
# We use a delimiter that basically tells the code that at every ',' we encounter,
# we need to treat it as a new data point.
# The data type of the variables is set to be int using dtype parameter.
data_collected = np.loadtxt(filename, delimiter=',', dtype=int)
 
# Printing data stored
print(data_collected)
 
 
# Type of data
print(
    f'Stored in : {type(data_collected)} and data type is : {data_collected.dtype}')

输出 ：

示例 2 的输出

示例 3：从 tsv 文件中读取

蟒蛇3

# Importing libraries that will be used
import numpy as np
 
# Setting name of the file that the data is to be extracted from in python
# This
filename = 'gfg_example3.tsv'
 
# Loading file data into numpy array and storing it in variable called data_collected
# We use a delimiter that basically tells the code that at every ',' we encounter,
# we need to treat it as a new data point.
data_collected = np.loadtxt(filename, delimiter='\t')
 
# Printing data stored
print(data_collected)
 
 
# Type of data
print(
    f'Stored in : {type(data_collected)} and data type is : {data_collected.dtype}')

输出 ：

示例 3 的输出

示例 4：仅选择特定行并跳过某些行

蟒蛇3

# Importing libraries that will be used
import numpy as np
 
# Setting name of the file that the data is to be extracted from in python
filename = 'gfg_example4.csv'
 
# Loading file data into numpy array and storing it in variable called data_collected
data_collected = np.loadtxt(
    filename, skiprows=1, usecols=[0, 1], delimiter=',')
 
# Printing data stored
print(data_collected)
 
 
# Type of data
print(
    f'Stored in : {type(data_collected)} and data type is : {data_collected.dtype}')

输出 ：

示例 4 的输出