如何将字符串解析为datafram

Question

为了演示，我正在尝试构建一个自包含的Jupyter笔记本，它将一个长地址字符串解析成pandas数据帧。目前，我必须突出显示整个字符串并使用pd.read_clipboard：

data = pd.read_clipboard(f,
                  comment='#', 
                  header=None, 
                  names=['address']).values.reshape(-1, 2)

matched_address = pd.DataFrame(data, columns=['addr_zagat', 'addr_fodor'])

我想知道是否有一种更简单的方法来直接读取字符串，而不是依赖于将内容复制到剪贴板。以下是字符串的前几行供参考：

f = """###################################################################################################
# 
#   There are 112 matches between the tuples.  The Zagat tuple is listed first, 
#   and then its Fodors pair.
#
###################################################################################################

Arnie Morton's of Chicago 435 S. La Cienega Blvd. Los Angeles 90048 310-246-1501 Steakhouses

Arnie Morton's of Chicago 435 S. La Cienega Blvd. Los Angeles 90048 310/246-1501 American
########################

Art's Deli 12224 Ventura Blvd. Studio City 91604 818-762-1221 Delis

Art's Delicatessen 12224 Ventura Blvd. Studio City 91604 818/762-1221 American
########################

Bel-Air Hotel 701 Stone Canyon Rd. Bel Air 90077 310-472-1211 Californian

Hotel Bel-Air 701 Stone Canyon Rd. Bel Air 90077 310/472-1211 Californian
########################

Cafe Bizou 14016 Ventura Blvd. Sherman Oaks 91423 818-788-3536 French Bistro

Cafe Bizou 14016 Ventura Blvd. Sherman Oaks 91423 818/788-3536 French
########################
h Bistro

Cafe Bizou 14016 Ventura Blvd. Sherman Oaks 91423 818/788-3536 French
########################"""

有人知道如何将这个字符串直接解析成pandas数据帧吗？你知道吗

我意识到这里还有另一个问题可以解决：Create Pandas DataFrame from a string但是字符串由分号分隔，与我的示例中使用的格式完全不同。你知道吗

Answer 1

您应该添加一个示例，说明您的输出应该是什么样子的，但一般来说，我建议如下：

import pandas as pd
import numpy as np
# read file, split into lines
f = open("./your_file.txt", "r").read().split('\n')
accumulator = []
# loop through lines
for line in f:
    # define criteria for selecting lines
    if len(line) > 1 and line[0].isupper():
        # define criteria for splitting the line
        # get name
        first_num_char = [c for c in line if c.isdigit()][0]
        name = line.split(first_num_char, 1)[0]
        line = line.replace(name, '')
        # get restaurant type
        rest_type = line.split()[-1]
        line = line.replace(rest_type, '')
        # get phone number
        number = line.split()[-1]
        line = line.replace(number, '')
        # remainder should be the address
        address = line
        accumulator.append([name, rest_type, number, address])
# turn accumulator into numpy array, pass with column index to DataFrame constructor
df = pd.DataFrame(np.asarray(accumulator), columns=['name', 'restaurant_type', 'phone_number', 'address'])