Python将逗号分隔的列表转换为 pandas 数据框 [英] Python convert comma separated list to pandas dataframe

问题描述

我正在努力将以逗号分隔的列表转换为多列(7)数据帧.

I am struggling to convert a comma separated list into a multi column (7) data-frame.

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',.........

以下内容创建了一个单列框架:

The following creates a frame of a single column:

df = pd.DataFrame(mylist)

我已经查看了Pandas内置的csv功能，但是我的csv数据保存在一个列表中.我怎样才能简单地将列表转换成7列数据框.

I have reviewed the inbuilt csv functionality for Pandas, however my csv data is held in a list. How can I simply covert the list into a 7 column data-frame.

谢谢.

推荐答案

您需要拆分列表中的每个字符串:

You need to split each string in your list:

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

如果您知道要在csv中跳过多少行，则可以使用skiprows=lines_of_metadata使用read_csv完成所有操作:

If you know how many lines to skip in your csv you can do it all with read_csv using 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特定字符开头，则可以使用注释:

Or if each line of the metadata starts with a certain character you can use comment:

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

如果需要指定多个字符，则可以将itertools.takewhile组合在一起，这将删除以xxx开头的行:

If you need to specify more then one character you can combine itertools.takewhile which will drop lines starting with 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)

使用您的输入数据，添加以#!!开头的几行:

Using your input data adding some lines starting with #!!:

#!! 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

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