Pandas：使用 Unix 纪元时间戳作为日期时间索引
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Pandas: Using Unix epoch timestamp as Datetime index
提问by Nipun Batra
My application involves dealing with data (contained in a CSV) which is of the following form:
我的应用程序涉及处理以下形式的数据（包含在 CSV 中）：
Epoch (number of seconds since Jan 1, 1970), Value
1368431149,20.3
1368431150,21.4
..
Currently i read the CSV using numpy loadtxt method (can easily use read_csv from Pandas). Currently for my series i am converting the timestamps field as follows:
目前我使用 numpy loadtxt 方法读取 CSV（可以轻松使用 Pandas 中的 read_csv）。目前，对于我的系列，我正在按如下方式转换时间戳字段：
timestamp_date=[datetime.datetime.fromtimestamp(timestamp_column[i]) for i in range(len(timestamp_column))]
I follow this by setting timestamp_date as the Datetime index for my DataFrame. I tried searching at several places to see if there is a quicker (inbuilt) way of using these Unix epoch timestamps, but could not find any. A lot of applications make use of such timestamp terminology.
我通过将 timestamp_date 设置为我的 DataFrame 的日期时间索引来遵循这一点。我尝试在几个地方搜索以查看是否有使用这些 Unix 纪元时间戳的更快（内置）方法，但找不到任何方法。许多应用程序使用这样的时间戳术语。
- Is there an inbuilt method for handling such timestamp formats?
- If not, what is the recommended way of handling these formats?
- 是否有处理此类时间戳格式的内置方法？
- 如果没有，处理这些格式的推荐方法是什么？
回答by eumiro
Convert them to datetime64[s]:
将它们转换为datetime64[s]：
np.array([1368431149, 1368431150]).astype('datetime64[s]')
# array([2013-05-13 07:45:49, 2013-05-13 07:45:50], dtype=datetime64[s])
回答by queise
You can also use pandas to_datetime:
您还可以使用Pandasto_datetime：
df['datetime'] = pd.to_datetime(df["timestamp"], unit='s')
This method requires Pandas 0.18 or later.
此方法需要 Pandas 0.18 或更高版本。
回答by Eric Blum
You can also use Pandas DatetimeIndex like so
您也可以像这样使用 Pandas DatetimeIndex
pd.DatetimeIndex(df['timestamp']*10**9)
the *10**9puts it into the format it's expecting for such timestamps.
在*10**9把它转化成其期待这样的时间戳的格式。
This is nice since it allows you to use functions such as .date()or .tz_localize()on the series.
这很好，因为它允许您使用系列.date()或.tz_localize()系列之类的功能。
