Skip to main content

Handling Time Series Data

Introduction​

Time series data is a type of data that is indexed in time order. It's often used for analyzing trends, forecasting, etc. In this tutorial, we will learn how to handle time series data using pandas.

Required Libraries​

First, we need to import the necessary libraries.

import pandas as pd
import numpy as np

Loading Time Series Data​

Pandas can read time series data, but it needs to be in the right format. The pd.read_csv() function is commonly used to load time series data. It has an optional parse_dates parameter to convert date columns to datetime format.

df = pd.read_csv('timeseries.csv', parse_dates=['date_column'])

Checking the Datetime Index​

We can use the df.info() function to check if the date_column is indeed in datetime format.

df.info()

Setting the Date Column as Index​

For time series analysis, it is important to set the date column as the index. We can use the df.set_index() function for this.

df.set_index('date_column', inplace=True)

Resampling Time Series Data​

Resampling involves changing the frequency of your time series observations. Two types of resampling are:

  1. Downsampling: Where you decrease the frequency of the samples, such as from days to months.
  2. Upsampling: Where you increase the frequency of the samples, such as from minutes to seconds.
# Downsampling to monthly data points
df_monthly = df.resample('M').mean()

# Upsampling to daily data points
df_daily = df.resample('D').ffill()

Shifting Time Series Data​

Shifting the dataset by a certain number of periods is a common operation. This can be done using df.shift() function.

df_shifted = df.shift(periods=1)

Rolling Window Operations​

Rolling window operations are another important transformation for time series data. The df.rolling() function provides the feature of rolling window calculations.

# Calculate the rolling mean over a window of 10 periods
df_rolling = df.rolling(window=10).mean()

Time Zone Handling​

Pandas provides functionality for time zone conversions.

# Convert to another time zone
df_tz = df.tz_convert('US/Eastern')

# Convert to UTC
df_utc = df.tz_convert('UTC')

Conclusion​

These are some of the basic operations we can perform on time series data using pandas. The pandas library comes with a lot of other functions that you can explore to perform more complex operations on your time series data.

Hope you find this tutorial helpful in getting started with handling time series data in pandas!