Python将逗号分隔列表转换为pandas数据帧

Question

我正在努力将逗号分隔列表转换为多列(7)数据帧.

print (type(mylist))

<type 'list'>
Print(mylist)


['AN,2__AAS000,26,20150826113000,-283.000,20150826120000,-283.000',         'AN,2__AE000,26,20150826113000,0.000,20150826120000,0.000',.........

以下内容创建单个列的框架:

df = pd.DataFrame(mylist)

我已经回顾了Pandas的内置csv功能,但我的csv数据保存在列表中.我怎样才能简单地将列表转换为7列数据帧.

提前致谢.

Answer 1

您需要拆分列表中的每个字符串:

import  pandas as pd

df = pd.DataFrame([sub.split(",") for sub in l])
print(df)

输出:

   0         1   2               3         4               5         6
0  AN  2__AS000  26  20150826113000  -283.000  20150826120000  -283.000
1  AN   2__A000  26  20150826113000     0.000  20150826120000     0.000
2  AN  2__AE000  26  20150826113000  -269.000  20150826120000  -269.000
3  AN  2__AE000  26  20150826113000  -255.000  20150826120000  -255.000
4  AN   2__AE00  26  20150826113000  -254.000  20150826120000  -254.000

如果您知道在csv中要跳过多少行,则可以使用read_csv执行以下操作skiprows=lines_of_metadata:

import  pandas as pd

df = pd.read_csv("in.csv",skiprows=3,header=None)
print(df)

或者,如果元数据的每一行都以某个字符开头,您可以使用注释:

df = pd.read_csv("in.csv",header=None,comment="#")  

如果你需要指定多个字符,你可以将itertools.takewhile它们组合起来,这将删除从以下开始的行xxx:

import pandas as pd
from itertools import dropwhile
import csv
with open("in.csv") as f:
    f = dropwhile(lambda x: x.startswith("#!!"), f)
    r = csv.reader(f)
    df = pd.DataFrame().from_records(r)

使用输入数据添加一些以#!!开头的行:

#!! various
#!! metadata
#!! lines
AN,2__AS000,26,20150826113000,-283.000,20150826120000,-283.000
AN,2__A000,26,20150826113000,0.000,20150826120000,0.000
AN,2__AE000,26,20150826113000,-269.000,20150826120000,-269.000
AN,2__AE000,26,20150826113000,-255.000,20150826120000,-255.000
AN,2__AE00,26,20150826113000,-254.000,20150826120000,-254.000

输出:

    0         1   2               3         4               5         6
0  AN  2__AS000  26  20150826113000  -283.000  20150826120000  -283.000
1  AN   2__A000  26  20150826113000     0.000  20150826120000     0.000
2  AN  2__AE000  26  20150826113000  -269.000  20150826120000  -269.000
3  AN  2__AE000  26  20150826113000  -255.000  20150826120000  -255.000
4  AN   2__AE00  26  20150826113000  -254.000  20150826120000  -254.000