python - 在 Pandas 数据框中循环和更新行的最有效方法

Question

这是我更新数据框行的代码:

def arrangeData(df):
hour_from_timestamp_list = []
date_from_timestamp_list = []
for row in df.itertuples():
    timestamp = row.timestamp
    hour_from_timestamp = datetime.fromtimestamp(
        int(timestamp) / 1000).strftime('%H:%M:%S')
    date_from_timestamp = datetime.fromtimestamp(
        int(timestamp) / 1000).strftime('%d-%m-%Y')
    hour_from_timestamp_list.append(hour_from_timestamp)
    date_from_timestamp_list.append(date_from_timestamp)
df['Time'] = hour_from_timestamp_list
df['Hour'] = pd.to_datetime(df['Time']).dt.hour
df['ChatDate'] = date_from_timestamp_list
return df

我试图从时间戳中提取时间、小时和聊天日期。代码工作正常。但是当有大量数据时，大约有 300,000 行，该功能非常慢。谁能建议一种更好的方法来更快地执行此功能？

对于循环，我尝试了更慢的 iterrows() 。

这是我正在处理的文档:

{
"_id" : ObjectId("5b9feadc32214d2b504ea6e1"),
"id" : 34176,
"timestamp" : NumberLong(1535019434998),
"platform" : "Email",
"sessionId" : LUUID("08a5caac-baa3-11e8-a508-106530216ef0"),
"intentStatus" : "NotHandled",
"botId" : "tony"
}

Answer 1

我相信这里是可能的用途:

#thanks @Chris A for another solution
t = pd.to_datetime(df['timestamp'], unit='ms')

t = pd.to_datetime(df['timestamp'].astype(int) / 1000)
#alternative
#t = pd.to_datetime(df['timestamp'].apply(int) / 1000)
#t = pd.to_datetime([int(x) / 1000 for x in df['timestamp']] )

df['Time'] = t.dt.strftime('%H:%M:%S')
df['Hour'] = t.dt.hour
df['ChatDate'] = t.dt.strftime('%d-%m-%Y')