使用Python，如何按小時對Dataframe中的列進行分組？

Question

我有一個python數據框（df1），它有一個列時間。我使用pd.to_datetime(df1['time'])將列轉換爲日期時間序列。現在，我得到一列是這樣的：

2016-08-24 00:00:00 2016-08-13 00:00:00 
2016-08-24 00:00:00 2016-08-13 00:00:00  
2016-08-24 00:00:00 2016-08-13 00:00:00 
2016-08-24 00:00:00 2016-08-13 00:00:00 
2016-08-24 00:00:01 2016-08-13 00:00:01 
2016-08-24 00:00:01 2016-08-13 00:00:01 
2016-08-24 00:00:02 2016-08-13 00:00:02 
2016-08-24 00:00:02 2016-08-13 00:00:02  
2016-08-24 00:00:02 2016-08-13 00:00:02  
2016-08-24 00:00:02 2016-08-13 00:00:02  
2016-08-24 00:00:02 2016-08-13 00:00:02  
2016-08-24 00:00:02 2016-08-13 00:00:02  
2016-08-24 00:00:02 2016-08-13 00:00:02  
2016-08-24 00:00:02 2016-08-13 00:00:02  
2016-08-24 00:00:02 2016-08-13 00:00:02  
.... 

2016-08-24 23:59:59 2016-08-13 00:00:02 

從本質上講，我想第一列按小時分組，這樣我可以看到有多少項目在1個小時在那裏。任何幫助都會很棒。

Answer 1

使用@jezrael設置。

df.resample(rule='H', how='count').rename(columns = {'time':'count'}) 

         count 
2016-08-24 00:00:00  1 
2016-08-24 01:00:00  3 
2016-08-24 02:00:00  1 

Answer 2

您可以按如下方式使用pandas.DatetimeIndex。

import numpy as np 
import pandas as pd 

# An example of time period 
drange = pd.date_range('2016-08-01 00:00:00', '2016-09-01 00:00:00', 
         freq='10min') 

N = len(drange) 

# The number of columns without 'time' is three. 
df = pd.DataFrame(np.random.rand(N, 3)) 
df['time'] = drange 

time_col = pd.DatetimeIndex(df['time']) 

gb = df.groupby([time_col.year, 
       time_col.month, 
       time_col.day, 
       time_col.hour]) 

for col_name, gr in gb: 
    print(gr) # If you want to see only the length, use print(len(gr)) 

[參考文獻] Python Pandas: Group datetime column into hour and minute aggregations

Answer 3

使用resample：

#pandas version 0.18.0 and higher 
df = df.resample('H').size() 

#pandas version below 0.18.0 
#df = df.resample('H', 'size') 

print (df) 
2016-08-24 00:00:00 1 
2016-08-24 01:00:00 3 
2016-08-24 02:00:00 1 
Freq: H, dtype: int64 

如果需要輸出DataFrame：

df = df.resample('H').size().rename('count').to_frame() 
print (df) 
        count 
2016-08-24 00:00:00  1 
2016-08-24 01:00:00  3 
2016-08-24 02:00:00  1 

---

或者你可以從DatetimeIndexminutes和seconds通過轉換爲<M8[h]，然後聚集size刪除：

import pandas as pd 

df = pd.DataFrame({'time': {pd.Timestamp('2016-08-24 01:00:00'): pd.Timestamp('2016-08-13 00:00:00'), pd.Timestamp('2016-08-24 01:00:01'): pd.Timestamp('2016-08-13 00:00:01'), pd.Timestamp('2016-08-24 01:00:02'): pd.Timestamp('2016-08-13 00:00:02'), pd.Timestamp('2016-08-24 02:00:02'): pd.Timestamp('2016-08-13 00:00:02'), pd.Timestamp('2016-08-24 00:00:00'): pd.Timestamp('2016-08-13 00:00:00')}}) 
print (df) 
            time 
2016-08-24 00:00:00 2016-08-13 00:00:00 
2016-08-24 01:00:00 2016-08-13 00:00:00 
2016-08-24 01:00:01 2016-08-13 00:00:01 
2016-08-24 01:00:02 2016-08-13 00:00:02 
2016-08-24 02:00:02 2016-08-13 00:00:02 

df= df.groupby([df.index.values.astype('<M8[h]')]).size() 
print (df) 
2016-08-24 00:00:00 1 
2016-08-24 01:00:00 3 
2016-08-24 02:00:00 1 
dtype: int64