我正在尝试围绕各种资产的公共时间戳合并一组DataFrame。数据集包含每小时数据，但时间戳在每个相应资产中的小时略有不同。所以我将时间戳从epoch转换为datetime并删除秒和分钟
market_trading_pair ohlcv_start_date next_future_timestep_return
7073 Poloniex_DOGE_BTC 1445392800 -0.023256
7074 Poloniex_DOGE_BTC 1445396400 0.023810
7075 Poloniex_DOGE_BTC 1445400000 0.000000
7076 Poloniex_DOGE_BTC 1445403600 -0.023256
7077 Poloniex_DOGE_BTC 1445407200 0.000000
使用此代码：
TS = 'ohlcv_start_date'
df[TS] = pd.to_datetime(df[TS], unit='s').dt.strftime('%Y-%m-%d %H:00:00')
print df.groupby('market_trading_pair').get_group('Poloniex_DOGE_BTC').head()[['market_trading_pair','ohlcv_start_date']]
market_trading_pair ohlcv_start_date next_future_timestep_return
7073 Poloniex_DOGE_BTC 2015-10-21 02:00:00 -0.023256
7074 Poloniex_DOGE_BTC 2015-10-21 03:00:00 0.023810
7075 Poloniex_DOGE_BTC 2015-10-21 04:00:00 0.000000
7076 Poloniex_DOGE_BTC 2015-10-21 05:00:00 -0.023256
7077 Poloniex_DOGE_BTC 2015-10-21 06:00:00 0.000000
使用所需数据创建新的dataFrame：
timestamp DOGE
7073 2015-10-21 02:00:00 -0.023256
7074 2015-10-21 03:00:00 0.023810
7075 2015-10-21 04:00:00 0.000000
7076 2015-10-21 05:00:00 -0.023256
7077 2015-10-21 06:00:00 0.000000
然后我创建一个'骨架'时间帧DataFrame，我将能够将所有数据帧合并到并合并一个帧来测试。
timeframe = pd.date_range(start=min_time, end=max_time, freq='H')
test = DataFrame(timeframe, columns=['timestamp'])
timestamp
0 2015-10-21 02:00:00
1 2015-10-21 03:00:00
2 2015-10-21 04:00:00
3 2015-10-21 05:00:00
4 2015-10-21 06:00:00
test = pd.merge(left=test, right=to_merge, left_on='timestamp',right_on='timestamp',how='left')
timestamp DOGE
0 2015-10-21 02:00:00 NaN
1 2015-10-21 03:00:00 NaN
2 2015-10-21 04:00:00 NaN
3 2015-10-21 05:00:00 NaN
结果是nan字段我认为可能是由于格式化错误？但是我比较了时间戳字符串，它们出现了'True'
答案 0 :(得分：1)
dtypes出现问题 - 无法将列类型string与datetime类型合并，因为输出为NaN：
print df
timestamp DOGE
7073 2015-10-21 02:00:00 -0.023256
7074 2015-10-21 03:00:00 0.023810
7075 2015-10-21 04:00:00 0.000000
7076 2015-10-21 05:00:00 -0.023256
7077 2015-10-21 06:00:00 0.000000
print df.dtypes
timestamp datetime64[ns]
DOGE float64
dtype: object
min_time = df['timestamp'].min()
max_time = df['timestamp'].max()
df['timestamp'] = df['timestamp'].dt.strftime('%Y-%m-%d %H:00:00')
print df
timestamp DOGE
7073 2015-10-21 02:00:00 -0.023256
7074 2015-10-21 03:00:00 0.023810
7075 2015-10-21 04:00:00 0.000000
7076 2015-10-21 05:00:00 -0.023256
7077 2015-10-21 06:00:00 0.000000
print df.dtypes
timestamp object **************
DOGE float64
dtype: object
timeframe = pd.date_range(start=min_time, end=max_time, freq='H')
test = pd.DataFrame(timeframe, columns=['timestamp'])
print test
timestamp
0 2015-10-21 02:00:00
1 2015-10-21 03:00:00
2 2015-10-21 04:00:00
3 2015-10-21 05:00:00
4 2015-10-21 06:00:00
print test.dtypes
timestamp datetime64[ns] ****************
dtype: object
print pd.merge(left=test, right=df, on='timestamp', how='left')
timestamp DOGE
0 2015-10-21 02:00:00 NaN
1 2015-10-21 03:00:00 NaN
2 2015-10-21 04:00:00 NaN
3 2015-10-21 05:00:00 NaN
4 2015-10-21 06:00:00 NaN
<强>解决方案
删除将datetime类型的列转换为string：
变化：
df[TS] = pd.to_datetime(df[TS], unit='s').dt.strftime('%Y-%m-%d %H:00:00')
为：
df[TS] = pd.to_datetime(df[TS], unit='s')
这意味着（我评论转换为string）：
print df.dtypes
timestamp datetime64[ns] ***********
DOGE float64
dtype: object
min_time = df['timestamp'].min()
max_time = df['timestamp'].max()
#df['timestamp'] = df['timestamp'].dt.strftime('%Y-%m-%d %H:00:00')
#print df
#print df.dtypes
timeframe = pd.date_range(start=min_time, end=max_time, freq='H')
test = pd.DataFrame(timeframe, columns=['timestamp'])
print test.dtypes
timestamp datetime64[ns] ***********
dtype: object
print pd.merge(left=test, right=df, on='timestamp', how='left')
timestamp DOGE
0 2015-10-21 02:00:00 -0.023256
1 2015-10-21 03:00:00 0.023810
2 2015-10-21 04:00:00 0.000000
3 2015-10-21 05:00:00 -0.023256
4 2015-10-21 06:00:00 0.000000