How to Plot a Timeseries in Python

Intro

When working with time series models, we would often like to plot the data to see how it changes over time. This is a simply line plot, but the x-axis is always dates. In this article, we will learn how to plot a time series in R.

Data

Lett's load a data set of monthly milk production. We will load it from the url below. The data consists of monthly intervals and kilograms of milk produced.

import pandas as pd

data = 'https://raw.githubusercontent.com/ourcodingclub/CC-time-series/master/monthly_milk.csv'

df = pd.read_csv(data)
df.head()

Next, we want to convert our month column to a python datetime object. We can use the to_datetime method to do this.

df['month'] = pd.to_datetime(df['month'])

Plotting a Timeseries

Next, we want to set the index of our data frame to the month column. This is not required to plot that data, but is a good practice when working with time series. This new index will make plotting, indexing, and aggregating with pandas easier for dates.

Once we have a data frame indexed on a date, we can call the plot method with kind='line' to create a line plot.

import matplotlib

df_ts = df.set_index('month')
df_ts.plot(kind = 'line')

<AxesSubplot:xlabel='month'>

Now that we know how to create a basic plot, we can also customize our plot a bit. Let's say we would like to plot the yearly data instead. We could use upsampling (this will be covered in a separate article). We can also update our date column to display only the years.

We will use .dt.to_period method to change our date to years Y. Then we will drop the month column and use our new year column as our index.

df['year'] = pd.to_datetime(df['month']).dt.to_period('Y')
year_df = df.drop('month', axis=1)
year_df.head()

year_df.set_index('year').plot(kind = 'line')

<AxesSubplot:xlabel='year'>