10 examples of 'python read file line by line into array' in Python

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def readLines(in_file):
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    """Returns a list of lines from a input markdown file."""
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    with open(in_file, 'r') as inf:
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        in_contents = inf.read().split('\n')
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    return in_contents

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def read_lines(filepath):
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    """
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    :param filepath: (string) path to a file, each line will be an item.
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    :return: (list).
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    """
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    ls = []
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    with open(filepath) as f:
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        for line in f:
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            line = line.strip()
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            ls.append(line)
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    return ls

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def getLineList(self):
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    if self.__bufferSize:
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        raise NotImplementedError
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    return self.__lineList

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def _read_file_lines(file_):
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    """Read lines of file
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       file_: either path to file or a file (like) object.
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       Return list of lines read or 'None' if file does not exist.
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    """
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    cont_str = _read_file(file_)
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    if cont_str is None:
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        return None
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    return [url_str.rstrip() for url_str in cont_str.splitlines()]

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def _read_line1(line):
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    """Reads the first line of a thermdat specie
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    Parameters
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    ----------
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        line : str
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            Line 1 of thermdat specie
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    Returns
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    -------
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        nasa_data : dict
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            Nasa input fields
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    """
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    nasa_data = {}
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    ref_pos = 24
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    ref_offset = 5
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    max_elements = 4
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    phase_pos = 44
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    # Store the name
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    blank_pos = line.find(' ')
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    nasa_data['name'] = line[:blank_pos]
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    # Store the notes if any
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    notes = line[blank_pos:ref_pos].strip()
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    if len(notes) > 0:
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        nasa_data['notes'] = notes
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    # Store the elements
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    nasa_data['elements'] = {}
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    for i in range(max_elements):
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        blank_pos = line.find(' ', ref_pos)
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        # All the elements have been assigned
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        if blank_pos == ref_pos:
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            break
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        element = line[ref_pos:blank_pos]
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        ref_pos += ref_offset
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        coeff = int(line[blank_pos:ref_pos])
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        nasa_data['elements'][element] = coeff
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    # Store the phase
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    nasa_data['phase'] = line[phase_pos]
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    # Store the temperatures
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    fields = _get_fields(line[phase_pos + 1:])
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    nasa_data['T_low'] = float(fields[0])
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    nasa_data['T_high'] = float(fields[1])
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    nasa_data['T_mid'] = float(fields[2])
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    return nasa_data

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def read_input(file):
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    for line in file:
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        yield line.rstrip()

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def read_file():
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    # Text file containing words for training
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    training_file = 'belling_the_cat.txt'
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    content=[]
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    with open(training_file,'r') as f:
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        for line in f.readlines():
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            # line 表示读到数据的每一行，linelist是按照空格切分成一个list
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            linelist=line.strip().split()
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            for i in linelist:
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                content.append(i.strip())
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    content=np.array(content)
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    content=np.reshape(content,[-1,])  #shape (204,1)
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    return content

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def read_line(filename):
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    """help function to read a single line from a file. returns none"""
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    try:
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        f = open(filename)
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        line = f.readline().strip()
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        f.close()
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        return line
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    except IOError:
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        return None

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def read_file(f):
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    """ 
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    Open the text file containing deep learning classification results for files. The decision variables is automatically opened from utils folder
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    :param f: text filename          
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    :returns:                                                                                                         
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      - sounds: sounds that were analyzed
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      - lc: labels for each sound
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    """
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    with open(f, 'r') as i_file:
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        files = i_file.read().split('\n')
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    sounds = np.array([i.split(" ")[0].split(".json")[0] for i in np.array(files)])
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    labels = np.array([i.split(" ") for i in np.array(files)])
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    lc = []
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    for lb in labels:
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        try: 
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            lc.append(lb[1])
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        except:
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            continue
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    decisions = np.loadtxt('utils/data.txt')
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    return sounds, np.array(lc), decisions

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def load_lines(input_file):
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    with open(input_file, 'r') as read:
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        lines = read.read()
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    return lines