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()
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
minute.head()
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
minute2.head()
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
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)daily: day minute: minute randomized minute: microsecond
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")]0.29330017699861666
try:
minute[pd.Timestamp("2021-01-01 00:00:00")]
except KeyError as ke:
print(ke)Timestamp('2021-01-01 00:00:00')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]
0.29330017699861666
minute.iloc[-1]
value 0.999354 Name: 2021-01-07 22:39:00, dtype: float64
minute2.iloc[4]
value 0.646703 Name: 2021-01-01 00:04:00.452614, dtype: float64
.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]
0.29330017699861666
minute.loc[jan1] # the defaults for hour, minute, second make this work
value 0.124186 Name: 2021-01-01 00:00:00, dtype: float64
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] Missing in index: datetime.datetime(2021, 1, 1, 0, 0)
value 0.527961 Name: 2021-01-01 00:00:00.641049, dtype: float64
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
2021-01-01 0.293300 2021-01-02 0.921466 Freq: D, dtype: float64
minute[0:2] # same
value 2021-01-01 00:00:00 0.124186 2021-01-01 00:01:00 0.542545
minute2[1:5:2] # every other
value 2021-01-01 00:01:00.088244 0.142192 2021-01-01 00:03:00.922019 0.509333
minute2.iloc[1:5:2] # works with the iloc indexer as well
value 2021-01-01 00:01:00.088244 0.142192 2021-01-01 00:03:00.922019 0.509333
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
2021-01-01 0.293300 2021-01-02 0.921466 2021-01-03 0.040813 Freq: D, dtype: float64
minute[datetime.datetime(2021, 1, 1): datetime.datetime(2021, 1, 1, 0, 2, 0)]
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
minute2[datetime.datetime(2021, 1, 1): datetime.datetime(2021, 1, 1, 0, 2, 0)]
value 2021-01-01 00:00:00.641049 0.527961 2021-01-01 00:01:00.088244 0.142192
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)]
value 2021-01-01 00:00:00.641049 0.527961 2021-01-01 00:01:00.088244 0.142192
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)cannot do positional indexing on DatetimeIndex with these indexers [2021-01-01 00:00:00] of type datetime
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"]
0.10723013753233923
minute.loc["2021-01-01 00:03:00"]
value 0.834881 Name: 2021-01-01 00:03:00, dtype: float64
Strings also work for slicing.
minute.loc["2021-01-01 00:03:00":"2021-01-01 00:05:00"] # end is inclusive
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
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)
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
You can do this on a DataFrame as well.
minute["2021-01-01"]
<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]
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"]
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]
If using string slicing, the end point includes all times in the day.
minute2.loc["2021-01-01":"2021-01-02"]
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]
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"]
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]
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
value 2021-01-01 00:00:00.641049 0.527961
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
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
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")value 0.527961 Name: 2021-01-01 00:00:03, dtype: float64
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")
value 2021-01-01 00:00:00.641049 0.527961
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.