CODEWITHSUNDEEP
pythonpandas
birone
birone

Reputation: 2089

python pandas timeseries: assign value to time (currently omitted from index)

I'd like to assign a value to a time in a time series that currently doesn't exist in the index, inserting it in the correct position. ie 2014-01-02 for the following:

import pandas as pd
from numpy.random import randn as randn
rng = pd.date_range('1/3/2014', periods=6, freq='D')
ts = pd.Series(randn(len(rng)), index=rng)
ts
Out[23]:
2014-01-03    1.876969
2014-01-04   -0.460700
2014-01-05    0.587874
2014-01-06    0.205369
2014-01-07   -1.319009
2014-01-08    0.907479
Freq: D, dtype: float64

After assignment, ts should be:

2014-01-02    1 # or whatever
2014-01-03    1.876969
...
2014-01-08    0.907479
Freq: D, dtype: float64

(Or more generally in the correct position based on the time/date.) This is what I've tried:

ts['2014-01-02'] = 1  # All append date and value to end
ts[pd.to_datetime('2014-01-02')] = 1
ts[pd.datetime(2014, 1, 2)] = 1

temp = pd.Series([1], index=['2014-01-02'])
ts.append(temp)    # No effect
ts.replace(temp)   # Error
ts.update(temp)    # No effect

I think there must be a way to do something so apparently simple, but it certainly hasn't jumped out at me...

Upvotes: 1

Views: 645

Answers (1)

unutbu
unutbu

Reputation: 881037

Sometimes you may not want the index to be sorted. So Pandas does not do this automatically. If you do want to sort the index, call sort_index:

ts['2014-01-02'] = 1 
ts = ts.sort_index()

yields

In [75]: ts
Out[75]: 
2014-01-02    1.000000
2014-01-03   -0.664830
2014-01-04    0.654928
2014-01-05    0.704723
2014-01-06    0.646540
2014-01-07    0.364220
2014-01-08   -1.346799
dtype: float64

Upvotes: 1

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