Time resampling of Pandas data Visualization (iii)

Time resampling of Pandas data Visualization (iii)

Python+pandas generate the specified date and resampling-CSDN blog https://blog.csdn.net/LY_ysys629/article/details/73823803

Pandas Resample Method-Csdn Blog https://blog.csdn.net/wangshuang1631/article/details/52314944

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Time Series Conversions:

        C=PD. Series (Np.random.rand (5), index= (Pd.date_range (' 20180130 ', periods=5, freq= ' D '))  #创建时间戳系列
        d=c.to_period ( ' M ')                   #
        print (c, type (c), C.index)
        print (d, type (d), D.index)

2018-01-30 0.424927
2018-01-31 0.522582
2018-02-01 0.889830
2018-02-02 0.130641
2018-02-03 0.222065
Freq:d, Dtype:float64 <class ' pandas.core.series.Series ' > Datetimeindex ([' 2018-01-30 ', ' 2018-01-31 ', ' 2018-02-01 ', ' 2018-02-02 ', ' 2018-02-03 '], dtype= ' Datetime64[ns ', freq= ' D ')
2018-01 0.424927
2018-01 0.522582
2018-02 0.889830
2018-02 0.130641
2018-02 0.222065
Freq:m, Dtype:float64 <class ' pandas.core.series.Series ' > Periodindex ([' 2018-01 ', ' 2018-01 ', ' 2018-02 ', ' 2018-02 ', ' 2018-02 '], dtype= ' period[m ', freq= ' M ')

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groupby sum by Group (sum) or average (mean)

     C=PD. Series (Np.random.rand (5), index= (Pd.date_range (' 20180130 ', periods=5, freq= ' D '))
        d=c.to_period (' M '  ) Convert to Month
        e=d.groupby (level=0). Mean ()   #或e =d.groupby (level=0). SUM ()

Output the following results:
        2018-01    0.407399
        2018-02    0.826991

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time to convert:

The timestamp series turns into a period series ' M ', which is then turned into a timestamp series by the time series, and the "Day" of the subsequent timestamp is no longer the number of days of the original timestamp.

        C=PD. Series (Np.random.rand (3), index= (Pd.date_range (' 20180130 ', periods=3, freq= ' D '))
        d=c.to_period (' M ')
        f= D.to_timestamp (how= ' start ')   #或者how = ' End '

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time resampling: Timestamp series index, minutes to 5 minutes

C=PD. Series (np.random.randint (0, one), index= (Pd.date_range (' 2018-01-30 9:30 ', periods=11, freq= ' T '))
D=c.resam ple (' 5min ', how= ' sum ', label= ' right ') #label = ' OK ', which represents the right-hand time (the back end of the 5-minute period), defaults to the start time
e=c.resample (' 5min ', how= ' OHLC ') )
print (c, D, E)

Output results:

The new syntax is. Resample (...). OHLC ()
  e=c.resample (' 5min ', how= ' OHLC ') 2018-01-30 09:30:00-2018-01-30
09:31:00     5
2018-01-30 09:32:00
2018-01-30 09:33:00    2018-01-30 09:34:00
2018-01-30 09:35:00
2018-01-30 09:36:00
2018-01-30 09:37:00     7
2018-01-30 09:38:00     4
2018-01-30 09:39:00
2018-01-30 09:40:00    40

2018-01-30 09:35:00    140
2018-01-30 09:40:00    140     #以5分钟时期的右侧时间9:35 As a sampling point in time, The default is 9:30 to the left of this period of time as sampling point
2018-01-30 09:45:00     40

freq:5t, Dtype:int32 open High low close
2018-01-30 09:30:00    5    C.resample (' 5min ', how= ' OHLC '), the 1th price in 5 minutes for the opening price, the last 1 prices for the closed plate.
2018-01-30 09:35:00    4
2018-01-30 09:40:00     40

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Time resampling:

        C=PD. Series (np.random.randint (0, M), Index=pd.date_range (' 2018-1-24 ', periods=10, freq= ' D '))
        D=c.groupby ( Lambda x:x.month). SUM ()
        e=c.groupby (c.index.to_period (' M ')). SUM ()
        print (c) print (
        E )

Output Result: print (c)

2018-01-24
2018-01-25
2018-01-26
2018-01-27
2018-01-28
2018-01-29
2018-01-30
2018-01-31    2018-02-01
2018-02-02
freq:d, Dtype:int32

Print (d)
1    257
2
Dtype:int32

Print (e)
2018-01    257
2018-02
freq:m, Dtype:int32

        C=PD. Series (Np.random.randint (0, 2), Index=pd.date_range (' 20180401 ', periods=2, freq= ' W-fri '))
        d=c.resample (' d '), Fill_method= ' Ffill ', limit=2)   #
        e=c.resample (' W-mon ', fill_method= ' Ffill ')
        print (c)
        print (d)
        print (e)


output results:

2018-04-06
2018-04-13     1
freq:w-fri, Dtype:int32
2018-04-06    34.0
2018-04-07    34.0
2018-04-08 34.0 2018-04-09     NaN
2018-04-10     nan
2018-04-11 nan 2018-04-12 nan
2018-04-13     1.0 freq:d
, Dtype:float64
2018-04-09
2018-04-16     1
freq:w-mon, Dtype:int32

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time resampling: sampling for year, quarter

        C=PD. Dataframe (Np.random.randint (2, 4)), Index=pd.date_range (' 2018-3-2 ', periods=15, freq= ' M '), Columns=list (' ABCD ')
        d=c.resample (' A-dec ', how= ' sum ')   #
        e=c.resample (' A-mar ', how= ' sum ')
        f=c.resample (' Q-dec ') , how= ' sum ')
        print (c) print (
        d) print (
        e)
        print (f)


output results: print (c)

             a   b   c   D
2018-03-31   4
2018-04-30   3   5   6
2018-05-31   4   8
2018-06-30   3   4
2018-07-31  14   4
2018-08-31   9
2018-09-30   8   2  28
2018-10-31   9   3   4
2018-11-30   6   6
2018-12-31   6
2019-01-31
2019-02-28   5   7
2019-04-30   9   9 (2019-03-31)
2019-05-31   7  15

Output results: print (d)
              a    b    c    d
2018-12-31  129  211
2019-12-31   80

Output Result: Print (e)
              a    b    c    d
2018-03-31    4
2019-03-31  172  145  159  242
2020-03-31   24

Output Result: print (f)

A B c D
2018-03-31 20 29 4 25
2018-06-30 18 13 32 50
2018-09-30 55 49 30 76
2018-12-31 57 38 28 60
2019-03-31 42 45 69 56
2019-06-30 32 26 37 24

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stock Data Cycle conversion:

        C=pd.read_csv (' 601656 ', index_col= ' Date ', parse_dates=true)
        d=c[' adj_close '].resample (' W-fri ', how= ' OHLC ')   #收盘价按周重采样
        d[' vol ']=c[' vol '].resample (' W-fri ', how= ' sum ')   #向d追加交易量周采样列