如何将期间字符串转换为实际的期间类型

问题描述

我有一列数据，例如'1971q1'，'1972q2'等(年份，后跟季度).

I have a column of data such as '1971q1', '1972q2', etc. (year followed by quarter) When I do:

print(type(df.Quarterly))

答案是Series

我需要做的是将此列转换"/转换为纯正的pd.Period类型，以便对其进行简单的时间代数处理.谢谢

What I need is to "cast"/convert this column to a genuine pd.Period type so I can do simple time algebra with it. Thank you

推荐答案

，您可以使用 pd.PeriodIndex()方法.

假设您具有以下DF:

In [517]: x
Out[517]:
  str_col
0  1971q1
1  1971q2
2  1971q3
3  1971q4
4  1972q1
5  1972q2
6  1972q3
7  1972q4

In [518]: x.dtypes
Out[518]:
str_col    object
dtype: object

让我们创建一个新的期间"列:

let's create a new 'period' column:

In [519]: x['period'] = pd.PeriodIndex(x.str_col, freq='Q')

In [520]: x
Out[520]:
  str_col period
0  1971q1 1971Q1
1  1971q2 1971Q2
2  1971q3 1971Q3
3  1971q4 1971Q4
4  1972q1 1972Q1
5  1972q2 1972Q2
6  1972q3 1972Q3
7  1972q4 1972Q4

In [521]: x.dtypes
Out[521]:
str_col    object
period     object
dtype: object

现在我们可以进行时间代数"，例如，让我们从每个周期中减去四分之一:

now we can do "time algebra", for example let's subtract one quarter from each period:

In [525]: x.period - 1
Out[525]:
0   1970Q4
1   1971Q1
2   1971Q2
3   1971Q3
4   1971Q4
5   1972Q1
6   1972Q2
7   1972Q3
Name: period, dtype: object

或者，您可以将str_col列强制转换为常规Pandas/NumPy datetime:

alternatively you can cast the str_col column to regular Pandas/NumPy datetime:

In [527]: pd.to_datetime(x.str_col, errors='coerce')
Out[527]:
0   1971-01-01
1   1971-04-01
2   1971-07-01
3   1971-10-01
4   1972-01-01
5   1972-04-01
6   1972-07-01
7   1972-10-01
Name: str_col, dtype: datetime64[ns]

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