大 pandas 可以自动从 CSV 文件中读取日期吗? [英] Can pandas automatically read dates from a CSV file?

问题描述

今天，当从数据文件(例如)读取数据时，pandas 能够识别值的类型这一事实让我感到非常惊讶:

Today I was positively surprised by the fact that while reading data from a data file (for example) pandas is able to recognize types of values:

df = pandas.read_csv('test.dat', delimiter=r"s+", names=['col1','col2','col3'])

比如可以这样检查:

for i, r in df.iterrows():
    print type(r['col1']), type(r['col2']), type(r['col3'])

特别是整数、浮点数和字符串被正确识别.但是，我有一列日期格式如下:2013-6-4.这些日期被识别为字符串(而不是 python 日期对象).有没有办法让熊猫学习"识别日期?

In particular integer, floats and strings were recognized correctly. However, I have a column that has dates in the following format: 2013-6-4. These dates were recognized as strings (not as python date-objects). Is there a way to "learn" pandas to recognized dates?

推荐答案

阅读时应添加parse_dates=True，或parse_dates=['column name']，这通常足以神奇地解析它.但总有一些奇怪的格式需要手动定义.在这种情况下，您还可以添加日期解析器功能，这是最灵活的方式.

You should add parse_dates=True, or parse_dates=['column name'] when reading, thats usually enough to magically parse it. But there are always weird formats which need to be defined manually. In such a case you can also add a date parser function, which is the most flexible way possible.

假设您有一个包含字符串的datetime"列，那么:

Suppose you have a column 'datetime' with your string, then:

from datetime import datetime
dateparse = lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S')

df = pd.read_csv(infile, parse_dates=['datetime'], date_parser=dateparse)

通过这种方式，您甚至可以将多列合并为一个日期时间列，从而将日期"和时间"列合并为一个日期时间"列:

This way you can even combine multiple columns into a single datetime column, this merges a 'date' and a 'time' column into a single 'datetime' column:

dateparse = lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S')

df = pd.read_csv(infile, parse_dates={'datetime': ['date', 'time']}, date_parser=dateparse)

您可以找到 strptime 和 strftime 的指令(即用于不同格式的字母)在本页.

You can find directives (i.e. the letters to be used for different formats) for strptime and strftime in this page.

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