pandas 可以自动识别日期吗？ [英] Can pandas automatically recognize dates?

问题描述

今天，我非常惊讶的是，从数据文件（例如）读取数据可以识别值的类型：

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

例如，可以这样检查：

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

特别是正确识别整数，浮点数和字符串。但是，我有一列具有以下格式的日期： 2013-6-4 。这些日期被识别为字符串（不是python日期对象）。有没有办法认识大熊猫到认可的日期？

解决方案

你应该添加 parse_dates = True 或 parse_dates = ['column name'] 在阅读时，通常足以神奇地解析它。但是总是有怪异的格式需要手动定义。在这种情况下，您还可以添加一个日期解析器函数，这是最灵活的方式。

假设您的字符串具有datetime列，则：

  dateparse = lambda x：pd.datetime.strptime（x，'％Y-％m-％d％H：％ M：％S'）
 
 df = pd.read_csv（infile，parse_dates = ['datetime']，date_parser = dateparse）
  

这样，你甚至可以将多个列组合成一个单一的datetime列，它将'date'和'time'列合并成一个'datetime'列：

  dateparse = lambda x：pd.datetime.strptime（x，'％Y-％m-％d％H：％M：％ S'）
 
 df = pd.read_csv（infile，parse_dates = {'datetime'：['date'，'time']}，date_parser = dateparse）
  

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'])

For example it can be checked in this way:

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

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?

解决方案

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.

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

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

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

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

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

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