Time Series data is used in all types of applications ranging from weather monitoring to stock trading. Pandas DataFrame objects can natively index a Time Series data and provide performant analysis.
A common issue is that one needs only analyze a subset of dates from a larger Time Series data. In this quick example, we’ll use the
.loc method of Pandas
DataFrames to select a range of dates from Time Series data.
Selecting a Range of Time Series Dates
Before we select a range of dates from our Time Series in pandas let’s go over some quick assumptions. First and foremost I’m assuming you have loaded data into a
DataFrame per standard practices outlined in the official documentation. With that assumption, we also are making the following assumptions:
- We have data
- The data is loaded into memory as a
- The data has an associated sequence of dates
Just so everyone is on the same page, let’s take a look at our data. Below is a Time Series data containing the Adjusted Close price for Bitcoin from the dates
2021-06-26. For the curious, that reflects a relative price increase of 6668.4832%.
# Inspect our data >>> df Date 2014-09-17 457.334015 2014-09-18 424.440002 2014-09-19 394.795990 2014-09-20 408.903992 2014-09-21 398.821014 ... 2021-06-22 32505.660156 2021-06-23 33723.027344 2021-06-24 34662.437500 2021-06-25 31637.779297 2021-06-26 30954.576172 Name: Adj Close, Length: 2475, dtype: float64
Check/Set Index Value
The first thing we need to do is to ensure our data is properly indexed by the column containing the date values. This value is often set during instantiation of the
DataFrame object by using the
index_col argument. It can be set to the index value of the Date column or verbose string name (e.g. “Date”.) We double-check this as follows:
# Check the current index of our data >>> df.index DatetimeIndex(['2014-09-17', '2014-09-18', '2014-09-19', '2014-09-20', '2014-09-21', '2014-09-22', '2014-09-23', '2014-09-24', '2014-09-25', '2014-09-26', ... '2021-06-17', '2021-06-18', '2021-06-19', '2021-06-20', '2021-06-21', '2021-06-22', '2021-06-23', '2021-06-24', '2021-06-25', '2021-06-26'], dtype='datetime64[ns]', name='Date', length=2475, freq=None)
Optional: Visualize the Data
Just for kicks, let’s visualize this Time Series as a line plot quickly:
While not strictly necessary for our example here I find plots helpful in assessing which ranges of data one might want to further analyze. In those cases, the period between 2017 and 2019 looks pretty interesting. Let’s isolate that range of Time Series Data in Pandas.
Slice Indexes via .loc Method
There are a number of ways to select ranges from Time Series data in pandas. I find the easiest to be using the .loc operator. It is highly optimized and offers a friendly syntactic use. Let’s slice into our Time Series:
# Select range >>> data.loc['2017-06-01':'2019-01-01'] Date 2017-06-01 2407.879883 2017-06-02 2488.550049 2017-06-03 2515.350098 2017-06-04 2511.810059 2017-06-05 2686.810059 ... 2018-12-28 3923.918701 2018-12-29 3820.408691 2018-12-30 3865.952637 2018-12-31 3742.700439 2019-01-01 3843.520020 Name: Adj Close, Length: 580, dtype: float64
Our DataFrame Time Series object now contains data restricted to the range of June 1st, 2017 through January 1st, 2019 (the interesting part of the chart above.) Let’s take another look at this chart to confirm visually.
This quick example demonstrates how Pandas provides an easy sub-selection of Time Series data by a range of dates. Here we’ve used what Pandas refers to as Partial String Indexing to achieve our desired results. Alternative approaches include using the mask feature. One parting word of advice is that, for those loading Time Series data via CSV files, making use of the
index_col arguments will save much heartache.