Dates in Pandas Cheatsheet

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 need 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 divided 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'.