View source for Panda: datetime cheat sheet

Sumber: https://dzone.com/articles/dates-in-pandas-cheatsheet




Dates in Pandas Cheatsheet

Check out a large collection of not-so-sloppy snippets for doing scientific computing and data visualization in Python with pandas.


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Lately, I've been working a lot with dates in Pandas, so I decided to make this little cheatsheet with the commands I use the most.
Importing a CSV using a custom function to parse dates:

 import pandas as pd

 def parse_month(month):
     """
     Converts a string from the format M in datetime format.
     Example: parse_month("2007M02") returns datetime(2007, 2, 1)
     """

     return pd.datetime(int(month[:4]), int(month[-2:]), 1)

 temperature = pd.read_csv('TempUSA.csv', parse_dates=['Date'], 
                           date_parser=parse_month, 
                           index_col=['Date'], # will become an index
                           # use a subset of the columns
                           usecols=['Date', 
                                    'LosAngelesMax', 'LosAngelesMin'])

 print temperature

             LosAngelesMax  LosAngelesMin
 Date                                    
 2000-01-01           19.6           10.0
 2000-02-01           18.9           10.1
 2000-03-01           18.6           10.1
 2000-04-01           20.2           12.5
 2000-05-01           21.9           14.2

Format the dates in a chart:

 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates

 plt.plot(temperature['LosAngelesMax'])
 plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
 plt.show()

Here's the reference of the date format directives. ISO compliant format: %Y-%m-%dT%H:%M:%S.
Group the DataFrame by month:

 print temperature.groupby([temperature.index.month]).mean()

       LosAngelesMax  LosAngelesMin
 Date                              
 1         20.092308       8.992308
 2         19.223077       9.276923
 3         19.253846      10.492308
 4         19.992308      11.461538
 5         21.076923      13.761538
 6         22.123077      15.800000
 7         23.892308      17.315385
 8         24.246154      17.530769
 9         24.384615      16.846154
 10        23.330769      14.630769
 11        21.950000      11.241667
 12        19.241667       8.683333

The resulting DataFrame is indexed by month.

Merging two DataFrames indexed with timestamps that don't match exactly:

 date_range_a = pd.date_range('2007-01-01 01:00', 
                             '2007-01-01 3:00', freq='1h')
 date_range_b = date_range_a + pd.Timedelta(10, 'm')

 df_a = pd.DataFrame(np.arange(len(date_range_a)), 
                     columns=['a'], index=date_range_a)
 df_b = pd.DataFrame(['x', 'y', 'z'], 
                     columns=['b'], index=date_range_b)
 print 'left DataFrame'
 print df_a
 print '\nright DataFrame'
 print df_b
 print '\nmerge_AsOf result'
 print pd.merge_asof(df_a, df_b, direction='nearest', 
                     left_index=True, right_index=True)

 left DataFrame
                      a
 2007-01-01 01:00:00  0
 2007-01-01 02:00:00  1
 2007-01-01 03:00:00  2
 right DataFrame
                     b
 2007-01-01 01:10:00  x
 2007-01-01 02:10:00  y
 2007-01-01 03:10:00  z

 merge_AsOf result
                      a  b
 2007-01-01 01:00:00  0  x
 2007-01-01 02:00:00  1  y
 2007-01-01 03:00:00  2  z

The DataFrames have been aligned according to the index on the left.
Aligning two DataFrames:

 aligned = df_a.align(df_b)
 print 'left aligned'
 print aligned[0]
 print '\nright aligned'
 print aligned[1]
 print '\ncombination'
 aligned[0]['b'] = aligned[1]['b']
 print aligned[0]

 left aligned
                        a   b
 2007-01-01 01:00:00  0.0 NaN
 2007-01-01 01:10:00  NaN NaN
 2007-01-01 02:00:00  1.0 NaN
 2007-01-01 02:10:00  NaN NaN
 2007-01-01 03:00:00  2.0 NaN
 2007-01-01 03:10:00  NaN NaN

 right aligned
                       a    b
 2007-01-01 01:00:00 NaN  NaN
 2007-01-01 01:10:00 NaN    x
 2007-01-01 02:00:00 NaN  NaN
 2007-01-01 02:10:00 NaN    y
 2007-01-01 03:00:00 NaN  NaN
 2007-01-01 03:10:00 NaN    z

 combination
                        a    b
 2007-01-01 01:00:00  0.0  NaN
 2007-01-01 01:10:00  NaN    x
 2007-01-01 02:00:00  1.0  NaN
 2007-01-01 02:10:00  NaN    y
 2007-01-01 03:00:00  2.0  NaN
 2007-01-01 03:10:00  NaN    z

The timestamps are now aligned according to both the DataFrames and unknown values have been filled with NaNs. The missing value can be filled with interpolation when working with numeric values:

 print aligned[0].a.interpolate()

 2007-01-01 01:00:00    0.0
 2007-01-01 01:10:00    0.5
 2007-01-01 02:00:00    1.0
 2007-01-01 02:10:00    1.5
 2007-01-01 03:00:00    2.0
 2007-01-01 03:10:00    2.0

Name: a, dtype: float64

The categorical values can be filled using the fillna method:

 print aligned[1].b.fillna(method='bfill')

 2007-01-01 01:00:00    x
 2007-01-01 01:10:00    x
 2007-01-01 02:00:00    y
 2007-01-01 02:10:00    y
 2007-01-01 03:00:00    z
 2007-01-01 03:10:00    z

Name: b, dtype: object

The method bfill propagates the next valid observation, while ffil the last valid observation.

Convert a Timedelta in hours:

 td = pd.Timestamp('2017-07-05 16:00') - pd.Timestamp('2017-07-05 12:00')

 print td / pd.Timedelta(1, unit='h')

4.0

To convert in days, months, minutes, and so on, one just needs to change the unit. Here are the values accepted: D, h, m, s, ms, us, ns.

Convert pandas timestamps in Unix timestamps:

 unix_ts = pd.date_range('2017-01-01 1:00', 
                         '2017-01-01 2:00', 
                         freq='30min').astype(np.int64) // 10**9

 print unix_ts

 Int64Index([1483232400, 1483234200, 1483236000], dtype='int64')

To convert in milliseconds, divide by 10**6 instead of 10**9.
Convert Unix timestamps in pandas timestamps:

 print pd.to_datetime(unix_ts, unit='s')
 DatetimeIndex(['2017-01-01 01:00:00', '2017-01-01 01:30:00',
                '2017-01-01 02:00:00'],
                dtype='datetime64[ns]', freq=None)

To convert from timestamps in milliseconds, change the unit to ms.







==Referensi==

* https://dzone.com/articles/dates-in-pandas-cheatsheet