Pandas - 读取存储在内存中的字符串到数据帧的csv

Question

用逗号分隔的文本存储在如下 var 中

data = """
Class,Name,Long,Lat
A,ABC11,139.6295542,35.61144069
A,ABC20,139.630596,35.61045559
A,ABC03,139.6300307,35.61327781
B,ABC54,139.7787818,35.68847945
B,ABC05,139.7814447,35.6816882
B,ABC06,139.7788191,35.681865
B,ABC24,139.7790396,35.67781697
"""

有没有一种快速的方法可以将它读入 pandas 数据帧，而不必存储到文件中并使用pd.read_csv()。我来自R，它提供了一种很好的方法来做到这一点，如下所示。

text <- "
State,District,County,Num Voters,Total Votes in State,Votes for None,Candidate Name,Party,Votes Scored
CA,San Diego,Delmar,190962,48026634,2511,A1,IND,949
CA,San Diego,Delmar,190962,48026634,2511,A2,RP(K),44815
"
df <- read.table(textConnection(text), sep = ",", header = TRUE)

Answer 1

使用io.StringIO 对象（用于文本 I/O 的内存流）：

import pandas as pd
from io import StringIO

data = """
Class,Name,Long,Lat
A,ABC11,139.6295542,35.61144069
A,ABC20,139.630596,35.61045559
A,ABC03,139.6300307,35.61327781
B,ABC54,139.7787818,35.68847945
B,ABC05,139.7814447,35.6816882
B,ABC06,139.7788191,35.681865
B,ABC24,139.7790396,35.67781697
"""

df = pd.read_csv(StringIO(data))
print(df)

输出：

  Class   Name        Long        Lat
0     A  ABC11  139.629554  35.611441
1     A  ABC20  139.630596  35.610456
2     A  ABC03  139.630031  35.613278
3     B  ABC54  139.778782  35.688479
4     B  ABC05  139.781445  35.681688
5     B  ABC06  139.778819  35.681865
6     B  ABC24  139.779040  35.677817

Answer 2

from prettytable import PrettyTable

def create_table(data):
    data = data.strip().split('\n')
    pt = PrettyTable()
    pt.field_names = data[0].split(',')

    for row in data[1:]:
        pt.add_row(row.split(','))

    return pt

data = """
Class,Name,Long,Lat
A,ABC11,139.6295542,35.61144069
A,ABC20,139.630596,35.61045559
A,ABC03,139.6300307,35.61327781
B,ABC54,139.7787818,35.68847945
B,ABC05,139.7814447,35.6816882
B,ABC06,139.7788191,35.681865
B,ABC24,139.7790396,35.67781697
"""

table = create_table(data)

print(table)
+-------+-------+-------------+-------------+
| Class |  Name |     Long    |     Lat     |
+-------+-------+-------------+-------------+
|   A   | ABC11 | 139.6295542 | 35.61144069 |
|   A   | ABC20 |  139.630596 | 35.61045559 |
|   A   | ABC03 | 139.6300307 | 35.61327781 |
|   B   | ABC54 | 139.7787818 | 35.68847945 |
|   B   | ABC05 | 139.7814447 |  35.6816882 |
|   B   | ABC06 | 139.7788191 |  35.681865  |
|   B   | ABC24 | 139.7790396 | 35.67781697 |
+-------+-------+-------------+-------------+

或

from prettytable import from_csv
from io import StringIO

table = from_csv(StringIO(data))

print(table)