带有if / else和append函数的Python for循环

Question

根据下面的列表，我必须创建一个带有“状态”和“区域”列的数据框：

原始数据：

 Alabama[edit]
 Auburn (Auburn University)[1]
 Florence (University of North Alabama)
 Jacksonville (Jacksonville State University)[2]
 Livingston (University of West Alabama)[2]
 Montevallo (University of Montevallo)[2]
 Troy (Troy University)[2]
 Tuscaloosa (University of Alabama, Stillman College, Shelton State)[3][4]
 Tuskegee (Tuskegee University)[5]
 Alaska[edit]
 Fairbanks (University of Alaska Fairbanks)[2]
 Arizona[edit]
 Flagstaff (Northern Arizona University)[6]
 Tempe (Arizona State University)

（此处为数据link。）

所需的输出：

State   Region
Alabama Auburn
Alabama Florence
Alabama Jacksonville
Alabama Livingston
Alabama Montevallo
Alabama Troy
Alabama Tuscaloosa
Alabama Tuskegee
Alaska  Fairbanks
Arizona Flagstaff
Arizona Tempe

代码：

    df = pd.DataFrame(columns=['State', 'RegionName'])
    with open('university_towns.txt', 'r') as UniversityList:
            content = UniversityList.readlines()
            state_row = []
            region_row = []
            for row in content:
                if '[edit]' in row:
                    state_row.append(row)
                    region_row.append('region_to_be_repeated')
                else:
                    region_row.append(row)
                    state_row.append('state_to_be_repeated')

在“ if”为True的情况下，如何将'state_to_be_reapeted'替换为附加内容？

Answer 1

如果我理解您的问题并希望输出正确，则可以执行以下操作：

univeristylist = []
with open('university_towns.txt', 'r') as file:
    for line in file:
        if '[edit]' in line:
            state = row
        else:
            universitylist.append([state, row])

df = pd.DataFrame(universitylist, columns=['State', 'RegionName'])

如果您不想使用'[edit]'和'[1]'部分，则可以将代码更改为：

univeristylist = []
with open('university_towns.txt', 'r') as file:
    for line in file:
        if '[edit]' in line:
            state = row.split(' [')[0]
        else:
            universitylist.append([state, row.split(' [')[0]])

df = pd.DataFrame(columns=['State', 'RegionName'])

Answer 2

您可以在教程Pythonic Data Cleaning With NumPy and Pandas中找到清理此数据集的示例。

选项1：在“纯Python”中执行字符串处理

您可以在文件的各行上使用贪婪的for循环，并在O（n）时间内加载：

import pandas as pd

university_towns = []

with open('input/university_towns.txt') as file:
    for line in file:
        edit_pos = line.find('[edit]')
        if edit_pos != -1:
            # Remember this `state` until the next is found
            state = line[:edit_pos]
        else:
            # Otherwise, we have a city; keep `state` as last-seen
            parens = line.find(' (')
            town = line[:parens] if parens != -1 else line
            university_towns.append((state, town))

towns_df = pd.DataFrame(university_towns,
                        columns=['State', 'RegionName'])

选项2：通过Pandas API进行字符串处理

或者，您可以使用Pandas的.str访问器进行字符串处理：

import re

import pandas as pd

university_towns = []

with open('input/university_towns.txt') as file:
    for line in file:
        if '[edit]' in line:
            # Remember this `state` until the next is found
            state = line
        else:
            # Otherwise, we have a city; keep `state` as last-seen
            university_towns.append((state, line))

towns_df = pd.DataFrame(university_towns,
                        columns=['State', 'RegionName'])

towns_df['State'] = towns_df.State.str.replace(r'\[edit\]\n', '')
towns_df['RegionName'] = towns_df.RegionName\
    .str.strip()\
    .str.replace(r' \(.*', '')\
    .str.replace(r'\[.*', '')

输出：

>>> towns_df.head()
     State    RegionName
0  Alabama        Auburn
1  Alabama      Florence
2  Alabama  Jacksonville
3  Alabama    Livingston
4  Alabama    Montevallo

Answer 3

我能想到的最短版本：

import pandas as pd

lst = list()

with open('university_towns.txt', 'r', newline='\n') as infile:
    for line in infile.readlines():
        if '[edit]' in line:
            state = line.split('[')[0]
        else:
            lst.append([state, line.split(' ')[0]])

df = pd.DataFrame(lst, columns=['State', 'RegionName'])
print(df)

在我的机器上生成（Python 3.6）：

      State    RegionName
0   Alabama        Auburn
1   Alabama      Florence
2   Alabama  Jacksonville
3   Alabama    Livingston
4   Alabama    Montevallo
5   Alabama          Troy
6   Alabama    Tuscaloosa
7   Alabama      Tuskegee
8    Alaska     Fairbanks
9   Arizona     Flagstaff
10  Arizona         Tempe