[英]Convert a single Pandas column to DateTime
In my dataframe, I set the index of each column to 'Time' and then did frame = frame.astype(float)
to convert all the the other data to floats.在我的 dataframe 中,我将每列的索引设置为“时间”,然后
frame = frame.astype(float)
将所有其他数据转换为浮点数。 However, I now need the default indices (0, 1, 2, etc) but I still want to set the 'Time' column to a date time format.但是,我现在需要默认索引(0、1、2 等),但我仍然想将“时间”列设置为日期时间格式。 I've tried a few different ways of doing this, they either work but mess up the time (says its 1970 instead of 2021) or they result in
TypeError: Cannot cast DatetimeArray to dtype float64
我尝试了几种不同的方法,它们要么工作,但弄乱了时间(说它是 1970 年而不是 2021 年),或者它们导致
TypeError: Cannot cast DatetimeArray to dtype float64
This is similar to the dataframe I want (but with the times messed up):这类似于我想要的 dataframe (但时间搞砸了):
Time Open High Low Close
0 1970-01-01 00:27:18.185760 57141.92 57157.16 57141.92 57147.00
1 1970-01-01 00:27:18.185820 57145.48 57149.15 57124.62 57139.75
2 1970-01-01 00:27:18.185880 57126.75 57173.11 57126.74 57142.20
3 1970-01-01 00:27:18.185940 57163.42 57163.42 57079.10 57135.31
4 1970-01-01 00:27:18.186000 57084.42 57110.00 57084.42 57092.95
I've tried changing the format of the 'Time' column with:我尝试使用以下方法更改“时间”列的格式:
frame['Time'] = pd.to_datetime(frame['Time'])
And和
frame['Time'] = frame['Time'].apply(pd.to_datetime)
And I have also tried changing the types of the other columns in a similar way我也尝试过以类似的方式更改其他列的类型
frame[['Open','High','Low','Close']] = frame[['Open','High','Low','Close']].apply(frame.astype(float))
And I tried this before and after applying pd.to_datetime
我在应用
pd.to_datetime
之前和之后都试过了
EDIT编辑
Going to give some more information because I haven't been specific enough.将提供更多信息,因为我还不够具体。 The code below retrieves data from an API and puts it into a DataFrame.
下面的代码从 API 检索数据并将其放入 DataFrame。 The response from the API is a list of lists, with each sublist containing 10 elements (I think, can't remember now).
API 的响应是一个列表列表,每个子列表包含 10 个元素(我想,现在不记得了)。 I only want the data up to 'Close'.
我只希望数据达到“关闭”。
def get_historical_futures_data(symbol, interval, lookback):
frame = pd.DataFrame(client.futures_historical_klines(symbol, interval, lookback+' min ago UTC'))
frame = frame.iloc[:,:5]
frame.columns = ['Time','Open','High','Low','Close']
frame = frame.set_index('Time')
frame.index = pd.to_datetime(frame.index, unit='ms')
frame = frame.astype(float)
print(frame)
frames.append(frame)
Open High Low Close
Time
2021-11-29 14:27:00 57220.49 57220.50 57185.95 57190.01
2021-11-29 14:28:00 57190.00 57209.21 57161.74 57177.28
2021-11-29 14:29:00 57177.28 57182.61 57160.26 57164.46
2021-11-29 14:30:00 57164.46 57186.99 57154.32 57155.99
2021-11-29 14:31:00 57156.00 57179.74 57154.33 57179.74
Above is the code (and its output), I had previously, however, in another part of my code, I have realised that it is much easier for me to keep the row index numbers, so I do not want to make 'Time' the index of each row.以上是我之前的代码(及其输出),但是,在我的代码的另一部分,我意识到保留行索引号对我来说更容易,所以我不想让“时间”每行的索引。 Instead, I want the index of each row to remain, and then the rest of the data frame to come after, similar to this:
相反,我希望保留每一行的索引,然后将数据帧的 rest 放在后面,类似于这样:
Time Open High Low Close
0 1970-01-01 00:27:18.185760 57141.92 57157.16 57141.92 57147.00
1 1970-01-01 00:27:18.185820 57145.48 57149.15 57124.62 57139.75
2 1970-01-01 00:27:18.185880 57126.75 57173.11 57126.74 57142.20
3 1970-01-01 00:27:18.185940 57163.42 57163.42 57079.10 57135.31
4 1970-01-01 00:27:18.186000 57084.42 57110.00 57084.42 57092.95
My issue is, that I am unable to make the 'Time' column into a DateTime type as well as make the other columns (Open, High, Low, Close) into float type.我的问题是,我无法将“时间”列设为 DateTime 类型,也无法将其他列(Open、High、Low、Close)设为浮点类型。 I either get errors about type casting, or the Time column gets messed up and says 1970 instead of 2021.
我要么收到有关类型转换的错误,要么时间列搞砸了,说 1970 而不是 2021。
How do I make every column (EXCEPT FOR TIME) float type, and make the Time column DateTime type?如何使每一列(时间除外)浮点类型,并使时间列 DateTime 类型?
I believe this issue might be happening because the format is not easy to find by pandas.我相信这个问题可能会发生,因为 pandas 不容易找到该格式。 Perhaps you can try using
infer_datetime_format=True
to enhance the formats being detected.也许您可以尝试使用
infer_datetime_format=True
来增强检测到的格式。
Kindly try:请尝试:
frame['Time'] = pd.to_datetime(frame['Time'],infer_datetime_format=True)
This outputs这输出
Time
0 1970-01-01 00:27:18.185760
1 1970-01-01 00:27:18.185820
2 1970-01-01 00:27:18.185880
And by using df.info()
we can check it's an actual datetime format:通过使用
df.info()
我们可以检查它是一个实际的日期时间格式:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Time 3 non-null datetime64[ns]
dtypes: datetime64[ns](1)
memory usage: 152.0 bytes
None
This is the sample data used for this example:这是用于此示例的示例数据:
df = pd.DataFrame({'Time':['1970-01-01 00:27:18.185760',
'1970-01-01 00:27:18.185820',
'1970-01-01 00:27:18.185880']})
So I figured out what I was doing wrong.所以我弄清楚我做错了什么。 My times were being changed from the year 2021 to 1970 because I wasn't specifying that the unit was in milliseconds.
我的时间从 2021 年更改为 1970 年,因为我没有指定单位为毫秒。 My code is very similar to what I had initially, and the solution is actually really simple:
我的代码与我最初的代码非常相似,解决方案实际上非常简单:
def get_historical_futures_data(symbol, interval, lookback):
frame = pd.DataFrame(client.futures_historical_klines(symbol, interval, lookback+' min ago UTC'))
frame = frame.iloc[:,:5]
frame.columns = ['Time','Open','High','Low','Close']
frame['Time'] = pd.to_datetime(frame['Time'], unit='ms')
print(frame)
frames.append(frame)
Output is: Output 是:
Time Open High Low Close
0 2021-11-29 19:17:00 58388.41 58401.33 58357.30 58359.75
1 2021-11-29 19:18:00 58359.74 58365.33 58270.00 58290.95
2 2021-11-29 19:19:00 58290.95 58291.80 58173.28 58188.67
3 2021-11-29 19:20:00 58188.68 58317.02 58174.30 58308.70
4 2021-11-29 19:21:00 58309.32 58365.75 58309.31 58330.55
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