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Indexing time series data in pandas

#pandas #python

Quite often the data that we want to analyze has a time based component. Think about data like daily temperatures or rainfall, stock prices, sales data, student attendance, or events like clicks or views of a web application. There is no shortage of sources of data, and new sources are being added all the time. As a result, most pandas users will need to be familiar with time series data at some point.

A time series is just a pandas DataFrame or Series that has a time based index. The values in the time series can be anything else that can be contained in the containers, they are just accessed using date or time values. A time series container can be manipulated in many ways in pandas, but for this article I will focus just on the basics of indexing. Knowing how indexing works first is important for data exploration and use of more advanced features.

DatetimeIndex

In pandas, a DatetimeIndex is used to provide indexing for pandas Series and DataFrames and works just like other Index types, but provides special functionality for time series operations. We’ll cover the common functionality with other Index types first, then talk about the basics of partial string indexing.

One word of warning before we get started. It’s important for your index to be sorted, or you may get some strange results.

Examples

To show how this functionality works, let’s create some sample time series data with different time resolutions.

import pandas as pd
import numpy as np

import datetime

# this is an easy way to create a DatetimeIndex
# both dates are inclusive
d_range = pd.date_range("2021-01-01", "2021-01-20")

# this creates another DatetimeIndex, 10000 minutes long
m_range = pd.date_range("2021-01-01", periods=10000, freq="T")

# daily data in a Series
daily = pd.Series(np.random.rand(len(d_range)), index=d_range)
# minute data in a DataFrame
minute = pd.DataFrame(np.random.rand(len(m_range), 1),
                      columns=["value"],
                      index=m_range)

# time boundaries not on the minute boundary, add some random jitter
mr_range = m_range + pd.Series([pd.Timedelta(microseconds=1_000_000.0 * s)
                                for s in np.random.rand(len(m_range))]) 
# minute data in a DataFrame, but at a higher resolution
minute2 = pd.DataFrame(np.random.rand(len(mr_range), 1),
                       columns=["value"],
                       index=mr_range)

daily.head()
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2021-01-01 0.293300
2021-01-02 0.921466
2021-01-03 0.040813
2021-01-04 0.107230
2021-01-05 0.201100
Freq: D, dtype: float64
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minute.head()
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                        value
2021-01-01 00:00:00 0.124186
2021-01-01 00:01:00 0.542545
2021-01-01 00:02:00 0.557347
2021-01-01 00:03:00 0.834881
2021-01-01 00:04:00 0.732195
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minute2.head()
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                               value
2021-01-01 00:00:00.641049 0.527961
2021-01-01 00:01:00.088244 0.142192
2021-01-01 00:02:00.976195 0.269042
2021-01-01 00:03:00.922019 0.509333
2021-01-01 00:04:00.452614 0.646703
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Resolution

A DatetimeIndex has a resolution that indicates to what level the Index is indexing the data. The three indices created above have distinct resolutions. This will have ramifications in how we index later on.

print("daily:", daily.index.resolution)
print("minute:", minute.index.resolution)
print("randomized minute:", minute2.index.resolution)
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daily: day
minute: minute
randomized minute: microsecond
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Typical indexing

Before we get into some of the “special” ways to index a pandas Series or DataFrame with a DatetimeIndex, let’s just look at some of the typical indexing functionality.

Basics

I’ve covered the basics of indexing before, so I won’t cover too many details here. However it’s important to realize that a DatetimeIndex works just like other indices in pandas, but has extra functionality. (The extra functionality can be more useful and convenient, but just hold tight, those details are next). If you already understand basic indexing, you may want to skim until you get to partial string indexing. If you haven’t read my articles on indexing, you should start with the basics and go from there.

Indexing a DatetimeIndex using a datetime-like object will use exact indexing.

getitem a.k.a the array indexing operator ([])

When using datetime-like objects for indexing, we need to match the resolution of the index.

This ends up looking fairly obvious for our daily time series.

daily[pd.Timestamp("2021-01-01")]
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0.29330017699861666
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try:
    minute[pd.Timestamp("2021-01-01 00:00:00")]
except KeyError as ke:
    print(ke)
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Timestamp('2021-01-01 00:00:00')
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This KeyError is raised because in a DataFrame, using a single argument to the [] operator will look for a column, not a row. We have a single column called value in our DataFrame, so the code above is looking for a column. Since there isn’t a column by that name, there is a KeyError. We will use other methods for indexing rows in a DataFrame.

.iloc indexing

Since the iloc indexer is integer offset based, it’s pretty clear how it works, not much else to say here. It works the same for all resolutions.

daily.iloc[0]
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0.29330017699861666
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minute.iloc[-1]
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value 0.999354
Name: 2021-01-07 22:39:00, dtype: float64
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minute2.iloc[4]
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value 0.646703
Name: 2021-01-01 00:04:00.452614, dtype: float64
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.loc indexing

When using datetime-like objects, you need to have exact matches for single indexing. It’s important to realize that when you make datetime or pd.Timestamp objects, all the fields you don’t specify explicitly will default to 0.

jan1 = datetime.datetime(2021, 1, 1)
daily.loc[jan1]
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0.29330017699861666
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minute.loc[jan1] # the defaults for hour, minute, second make this work
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value 0.124186
Name: 2021-01-01 00:00:00, dtype: float64
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try:
    # we don't have that exact time, due to the jitter
    minute2.loc[jan1] 
except KeyError as ke:
    print("Missing in index: ", ke)
# but we do have a value on that day
# we could construct it manually to the microsecond if needed
jan1_ms = datetime.datetime(2021, 1, 1, 0, 0, 0, microsecond=minute2.index[0].microsecond)
minute2.loc[jan1_ms]
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Missing in index: datetime.datetime(2021, 1, 1, 0, 0)
value 0.527961
Name: 2021-01-01 00:00:00.641049, dtype: float64
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Slicing

Slicing with integers works as expected, you can read more about regular slicing here. But here’s a few examples of “regular” slicing, which works with the array indexing operator ([]) or the .iloc indexer.

daily[0:2] # first two, end is not inclusive
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2021-01-01 0.293300
2021-01-02 0.921466
Freq: D, dtype: float64
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minute[0:2] # same
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                        value
2021-01-01 00:00:00 0.124186
2021-01-01 00:01:00 0.542545
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minute2[1:5:2] # every other
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                               value
2021-01-01 00:01:00.088244 0.142192
2021-01-01 00:03:00.922019 0.509333
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minute2.iloc[1:5:2] # works with the iloc indexer as well
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                               value
2021-01-01 00:01:00.088244 0.142192
2021-01-01 00:03:00.922019 0.509333
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Slicing with datetime-like objects also works. Note that the end item is inclusive, and the defaults for hours, minutes, seconds, and microseconds will set the cutoff for the randomized data on minute boundaries (in our case).

daily[datetime.date(2021,1,1):datetime.date(2021, 1,3)] # end is inclusive
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2021-01-01 0.293300
2021-01-02 0.921466
2021-01-03 0.040813
Freq: D, dtype: float64
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minute[datetime.datetime(2021, 1, 1): datetime.datetime(2021, 1, 1, 0, 2, 0)]
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                        value
2021-01-01 00:00:00 0.124186
2021-01-01 00:01:00 0.542545
2021-01-01 00:02:00 0.557347
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minute2[datetime.datetime(2021, 1, 1): datetime.datetime(2021, 1, 1, 0, 2, 0)]
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                               value
2021-01-01 00:00:00.641049 0.527961
2021-01-01 00:01:00.088244 0.142192
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This sort of slicing work with [] and .loc, but not .iloc, as expected. Remember, .iloc is for integer offset indexing.

minute2.loc[datetime.datetime(2021, 1, 1): datetime.datetime(2021, 1, 1, 0, 2, 0)]
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                               value
2021-01-01 00:00:00.641049 0.527961
2021-01-01 00:01:00.088244 0.142192
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try:
    # no! use integers with iloc
    minute2.iloc[datetime.datetime(2021, 1, 1): datetime.datetime(2021, 1, 1, 0, 2, 0)]
except TypeError as te:
    print(te)
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cannot do positional indexing on DatetimeIndex with these indexers [2021-01-01 00:00:00] of type datetime
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Special indexing with strings

Now things get really interesting and helpful. When working with time series data, partial string indexing can be very helpful and way less cumbersome than working with datetime objects. I know we started with objects, but now you see that for interactive use and exploration, strings are very helpful. You can pass in a string that can be parsed as a full date, and it will work for indexing.

daily["2021-01-04"]
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0.10723013753233923
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minute.loc["2021-01-01 00:03:00"]
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value 0.834881
Name: 2021-01-01 00:03:00, dtype: float64
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Strings also work for slicing.

minute.loc["2021-01-01 00:03:00":"2021-01-01 00:05:00"] # end is inclusive
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                        value
2021-01-01 00:03:00 0.834881
2021-01-01 00:04:00 0.732195
2021-01-01 00:05:00 0.291089
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Partial String Indexing

Partial strings can also be used, so you only need to specify part of the data. This can be useful for pulling out a single year, month, or day from a longer dataset.

daily["2021"] # all items match (since they were all in 2021)
daily["2021-01"] # this one as well (and only in January for our data)
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2021-01-01 0.293300
2021-01-02 0.921466
2021-01-03 0.040813
2021-01-04 0.107230
2021-01-05 0.201100
2021-01-06 0.534822
2021-01-07 0.070303
2021-01-08 0.413683
2021-01-09 0.316605
2021-01-10 0.438853
2021-01-11 0.258554
2021-01-12 0.473523
2021-01-13 0.497695
2021-01-14 0.250582
2021-01-15 0.861521
2021-01-16 0.589558
2021-01-17 0.574399
2021-01-18 0.951196
2021-01-19 0.967695
2021-01-20 0.082931
Freq: D, dtype: float64
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You can do this on a DataFrame as well.

minute["2021-01-01"]
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<ipython-input-67-96027d36d9fe>:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.
  minute["2021-01-01"]

                        value
2021-01-01 00:00:00 0.124186
2021-01-01 00:01:00 0.542545
2021-01-01 00:02:00 0.557347
2021-01-01 00:03:00 0.834881
2021-01-01 00:04:00 0.732195
... ...
2021-01-01 23:55:00 0.687931
2021-01-01 23:56:00 0.001978
2021-01-01 23:57:00 0.770587
2021-01-01 23:58:00 0.154300
2021-01-01 23:59:00 0.777973

[1440 rows x 1 columns]
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See that deprecation warning? You should no longer use [] for DataFrame string indexing (as we saw above, [] should be used for column access, not rows). Depending on whether the value is found in the index or not, you may get an error or a warning. Use .loc instead so you can avoid the confusion.

minute2.loc["2021-01-01"]
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                               value
2021-01-01 00:00:00.641049 0.527961
2021-01-01 00:01:00.088244 0.142192
2021-01-01 00:02:00.976195 0.269042
2021-01-01 00:03:00.922019 0.509333
2021-01-01 00:04:00.452614 0.646703
... ...
2021-01-01 23:55:00.642728 0.749619
2021-01-01 23:56:00.238864 0.053027
2021-01-01 23:57:00.168598 0.598910
2021-01-01 23:58:00.103543 0.107069
2021-01-01 23:59:00.687053 0.941584

[1440 rows x 1 columns]
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If using string slicing, the end point includes all times in the day.

minute2.loc["2021-01-01":"2021-01-02"]
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                               value
2021-01-01 00:00:00.641049 0.527961
2021-01-01 00:01:00.088244 0.142192
2021-01-01 00:02:00.976195 0.269042
2021-01-01 00:03:00.922019 0.509333
2021-01-01 00:04:00.452614 0.646703
... ...
2021-01-02 23:55:00.604411 0.987777
2021-01-02 23:56:00.134674 0.159338
2021-01-02 23:57:00.508329 0.973378
2021-01-02 23:58:00.573397 0.223098
2021-01-02 23:59:00.751779 0.685637

[2880 rows x 1 columns]
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But if we include times, it will include partial periods, cutting off the end right up to the microsecond if it is specified.

minute2.loc["2021-01-01":"2021-01-02 13:32:01"]
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                               value
2021-01-01 00:00:00.641049 0.527961
2021-01-01 00:01:00.088244 0.142192
2021-01-01 00:02:00.976195 0.269042
2021-01-01 00:03:00.922019 0.509333
2021-01-01 00:04:00.452614 0.646703
... ...
2021-01-02 13:28:00.925951 0.969213
2021-01-02 13:29:00.037827 0.758476
2021-01-02 13:30:00.309543 0.473163
2021-01-02 13:31:00.363813 0.846199
2021-01-02 13:32:00.867343 0.007899

[2253 rows x 1 columns]
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Slicing vs. exact matching

Our three datasets have different resolutions in their index: day, minute, and microsecond respectively. If we pass in a string indexing parameter and the resolution of the string is less accurate than the index, it will be treated as a slice. If it’s the same or more accurate, it’s treated as an exact match. Let’s use our microsecond (minute2) and minute (minute) resolution data examples. Note that every time you get a slice of the DataFrame, the value returned is a DataFrame. When it’s an exact match, it’s a Series.

minute2.loc["2021-01-01"] # slice - the entire day
minute2.loc["2021-01-01 00"] # slice - the first hour of the day
minute2.loc["2021-01-01 00:00"] # slice - the first minute of the day
minute2.loc["2021-01-01 00:00:00"] # slice - the first minute and second of the day
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                            value
2021-01-01 00:00:00.641049 0.527961
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print(str(minute2.index[0])) # note the string representation include the full microseconds
minute2.loc[str(minute2.index[0])] # slice - this seems incorrect to me, should return Series not DataFrame
minute2.loc[minute2.index[0]] # exact match
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2021-01-01 00:00:00.641049

value 0.527961
Name: 2021-01-01 00:00:00.641049, dtype: float64

minute.loc["2021-01-01"] # slice - the entire day
minute.loc["2021-01-01 00"] # slice - the first hour of the day
minute.loc["2021-01-01 00:00"] # exact match

value 0.124186
Name: 2021-01-01 00:00:00, dtype: float64
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Note that for a microsecond resolution string match, I don’t see an exact match (where the return would be a Series), but instead a slice match (because the return value is a DataFrame). On the minute resolution DataFrame it worked as I expected.

asof

One way to deal with this sort of issue is to use asof. Often, when you have data that is either randomized in time or may have missing values, getting the most recent value as of a certain time is preffered. You could do this yourself, but it looks little cleaner to use asof.

minute2.loc[:"2021-01-01 00:00:03"].iloc[-1]
# vs
minute2.asof("2021-01-01 00:00:03")
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value 0.527961
Name: 2021-01-01 00:00:03, dtype: float64
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truncate

You can also use truncate which is sort of like slicing. You specify a value of before or after (or both) to indicate cutoffs for data. Unlike slicing which includes all values that partially match the date, truncate assumes 0 for any unspecified values of the date.

minute2.truncate(after="2021-01-01 00:00:03")
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                               value
2021-01-01 00:00:00.641049 0.527961
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Summary

You can now see that time series data can be indexed a bit differently than other types of Index in pandas. Understanding time series slicing will allow you to quickly navigate time series data and quickly move on to more advanced time series analysis.

The post Indexing time series data in pandas appeared first on wrighters.io.

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