导入CSV文件作为pandas DataFrame [英] Import CSV file as a pandas DataFrame

问题描述

将CSV文件读入 pandas DataFrame的Python方法是什么? (然后可以将其用于统计操作，可以使用其他类型的列等)?

What's the Python way to read in a CSV file into a pandas DataFrame (which I can then use for statistical operations, can have differently-typed columns, etc.)?

我的CSV文件"value.txt"具有以下内容:

My CSV file "value.txt" has the following content:

Date,"price","factor_1","factor_2"
2012-06-11,1600.20,1.255,1.548
2012-06-12,1610.02,1.258,1.554
2012-06-13,1618.07,1.249,1.552
2012-06-14,1624.40,1.253,1.556
2012-06-15,1626.15,1.258,1.552
2012-06-16,1626.15,1.263,1.558
2012-06-17,1626.15,1.264,1.572

在R中，我们将使用读取此文件:

In R we would read this file in using:

price <- read.csv("value.txt")  

这将返回R data.frame:

and that would return an R data.frame:

> price <- read.csv("value.txt")
> price
     Date   price factor_1 factor_2
1  2012-06-11 1600.20    1.255    1.548
2  2012-06-12 1610.02    1.258    1.554
3  2012-06-13 1618.07    1.249    1.552
4  2012-06-14 1624.40    1.253    1.556
5  2012-06-15 1626.15    1.258    1.552
6  2012-06-16 1626.15    1.263    1.558
7  2012-06-17 1626.15    1.264    1.572

是否有Python方式来获得相同的功能?

Is there a Pythonic way to get the same functionality?

推荐答案

熊猫进行救援:

pandas to the rescue:

import pandas as pd
print pd.read_csv('value.txt')

        Date    price  factor_1  factor_2
0  2012-06-11  1600.20     1.255     1.548
1  2012-06-12  1610.02     1.258     1.554
2  2012-06-13  1618.07     1.249     1.552
3  2012-06-14  1624.40     1.253     1.556
4  2012-06-15  1626.15     1.258     1.552
5  2012-06-16  1626.15     1.263     1.558
6  2012-06-17  1626.15     1.264     1.572

这将返回类似于 DataFrame >.

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