如何用numpy在Python中读取数字数据或文件

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。

数字数据可以存在于不同格式的文件中。

• 数据可以保存在一个txt文件中，每一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（tab separated values）文件中。

在文件中存储数据有多种方式，以上是一些最常用的存储数字数据的格式。为了实现我们需要的功能，将使用numpy的loadtxt()函数。

语法:

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

参数:
fname : 要读取的文件、文件名或生成器。如果文件名的扩展名是.gz或.bz2，则首先对文件进行解压缩。注意，对于Python 3k，生成器应该返回字节字符串。
dtype :结果数组的数据类型；默认：浮点。如果这是一个结构化的数据类型，产生的数组将是一维的，每一行将被解释为数组的一个元素。
delimiter : 用来分隔数值的字符串。默认情况下，它是任何空白。_
converters : 一个字典，将列号映射到一个将该列转换为浮点数的函数。例如，如果第0列是一个日期字符串：converters = {0: datestr2num}。默认值。无。
skiprows :跳过第一个skiprows行；默认：0。

返回： ndarray

步骤

• Import module
• Load file
• 读取数字数据
• 打印检索到的数据。

下面是对各种文件格式的一些实现。

例子1：从文本文件中读取数字数据

# 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 :

例子2：从CSV文件中读取数字数据。

# 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 :

例子3：从tsv文件中读取信息

# 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 :

例子4：只选择特定的行并跳过一些行

# 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 :