Python time series plotting

Time Series and Date Axes in Python

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Time Series using Axes of type date ¶

Time series can be represented using either plotly.express functions ( px.line , px.scatter , px.bar etc) or plotly.graph_objects charts objects ( go.Scatter , go.Bar etc). For more examples of such charts, see the documentation of line and scatter plots or bar charts.

For financial applications, Plotly can also be used to create Candlestick charts and OHLC charts, which default to date axes.

Plotly auto-sets the axis type to a date format when the corresponding data are either ISO-formatted date strings or if they’re a date pandas column or datetime NumPy array.

# Using plotly.express import plotly.express as px df = px.data.stocks() fig = px.line(df, x='date', y="GOOG") fig.show() 

# Using graph_objects import plotly.graph_objects as go import pandas as pd df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv') fig = go.Figure([go.Scatter(x=df['Date'], y=df['AAPL.High'])]) fig.show() 

Time Series in Dash¶

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash , click «Download» to get the code and run python app.py .

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

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Different Chart Types on Date Axes¶

Any kind of cartesian chart can be placed on date axes, for example this bar chart of relative stock ticker values.

import plotly.express as px df = px.data.stocks(indexed=True)-1 fig = px.bar(df, x=df.index, y="GOOG") fig.show() 

import plotly.express as px df = px.data.stocks(indexed=True)-1 fig = px.area(df, facet_col="company", facet_col_wrap=2) fig.show() 

Configuring Tick Labels¶

By default, the tick labels (and optional ticks) are associated with a specific grid-line, and represent an instant in time, for example, «00:00 on February 1, 2018». Tick labels can be formatted using the tickformat attribute (which accepts the d3 time-format formatting strings) to display only the month and year, but they still represent an instant by default, so in the figure below, the text of the label «Feb 2018» spans part of the month of January and part of the month of February. The dtick attribute controls the spacing between gridlines, and the «M1» setting means «1 month». This attribute also accepts a number of milliseconds, which can be scaled up to days by multiplying by 24*60*60*1000 .

Date axis tick labels have the special property that any portion after the first instance of ‘\n’ in tickformat will appear on a second line only once per unique value, as with the year numbers in the example below. To have the year number appear on every tick label, ‘
‘ should be used instead of ‘\n’ .

Note that by default, the formatting of values of X and Y values in the hover label matches that of the tick labels of the corresponding axes, so when customizing the tick labels to something broad like «month», it’s usually necessary to customize the hover label to something narrower like the actual date, as below.

import plotly.express as px df = px.data.stocks() fig = px.line(df, x="date", y=df.columns, hover_data="date": "|%B %d, %Y">, title='custom tick labels') fig.update_xaxes( dtick="M1", tickformat="%b\n%Y") fig.show() 

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Plotting time-series

Time series data is data that is recorded. Visualizing this type of data helps clarify trends and illuminates relationships between data. This is the Summary of lecture «Introduction to Data Visualization with Matplotlib», via datacamp.

Jun 26, 2020 • Chanseok Kang • 6 min read

import pandas as pd import matplotlib.pyplot as plt plt.rcParams['figure.figsize'] = (10, 5) 

Plotting time-series data

Read data with a time index

Pandas DataFrame objects can have an index that denotes time. This is useful because Matplotlib recognizes that these measurements represent time and labels the values on the axis accordingly.

In this exercise, you will read data from a CSV file called climate_change.csv that contains measurements of CO2 levels and temperatures made on the 6th of every month from 1958 until 2016. You will use Pandas read_csv function.

To designate the index as a DateTimeIndex , you will use the parse_dates and index_col key-word arguments both to parse this column as a variable that contains dates and also to designate it as the index for this DataFrame.

climate_change = pd.read_csv("./dataset/climate_change.csv", parse_dates=["date"], index_col=["date"]) 

Plot time-series data

To plot time-series data, we use the Axes object plot command. The first argument to this method are the values for the x-axis and the second argument are the values for the y-axis.

This exercise provides data stored in a DataFrame called climate_change . This variable has a time-index with the dates of measurements and two data columns: «co2» and «relative_temp» .

In this case, the index of the DataFrame would be used as the x-axis values and we will plot the values stored in the «relative_temp» column as the y-axis values. We will also properly label the x-axis and y-axis.

fig, ax = plt.subplots() # Add the time-series for "relative_temp" to the plot ax.plot(climate_change.index, climate_change["relative_temp"]); # Set the x-axis label ax.set_xlabel("Time"); # Set the y-axis label ax.set_ylabel("Relative temperature (Celsius)"); 

Using a time index to zoom in

When a time-series is represented with a time index, we can use this index for the x-axis when plotting. We can also select a to zoom in on a particular period within the time-series using Pandas’ indexing facilities. In this exercise, you will select a portion of a time-series dataset and you will plot that period.

fig, ax = plt.subplots() # Create variable seventies with data from "1970-01-01" to "1979-12-31" seventies = climate_change["1970-01-01":"1979-12-31"]; # Add the time-series for "co2" data from seventies to the plot ax.plot(seventies.index, seventies["co2"]); 

Plotting time-series with different variables

Plotting two variables

If you want to plot two time-series variables that were recorded at the same times, you can add both of them to the same subplot.

If the variables have very different scales, you’ll want to make sure that you plot them in different twin Axes objects. These objects can share one axis (for example, the time, or x-axis) while not sharing the other (the y-axis).

To create a twin Axes object that shares the x-axis, we use the twinx method.

In this exercise, you’ll have access to a DataFrame that has the climate_change data loaded into it. This DataFrame was loaded with the «date» column set as a DateTimeIndex , and it has a column called «co2» with carbon dioxide measurements and a column called «relative_temp» with temperature measurements.

fig, ax = plt.subplots() # Plot the CO2 variable in blue ax.plot(climate_change.index, climate_change["co2"], color="blue"); # Create a twin Axes that shares the x-axis ax2 = ax.twinx(); # Plot the relative temperature in red ax2.plot(climate_change.index, climate_change["relative_temp"], color="red"); 

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Creating A Time Series Plot With Seaborn And Pandas

In this article, we will learn how to create A Time Series Plot With Seaborn And Pandas. Let’s discuss some concepts :

• Pandas is an open-source library that’s built on top of NumPy library. It’s a Python package that gives various data structures and operations for manipulating numerical data and statistics. It’s mainly popular for importing and analyzing data much easier. Pandas is fast and it’s high-performance & productive for users.
• Seaborn is a tremendous visualization library for statistical graphics plotting in Python. It provides beautiful default styles and color palettes to form statistical plots more attractive. It’s built on the highest of matplotlib library and also closely integrated to the info structures from pandas.
• A timeplot (sometimes called a statistic graph) displays values against the clock. They’re almost like x-y graphs, but while an x-y graph can plot a spread of “x” variables (for example, height, weight, age), timeplots can only display time on the x-axis. Unlike the pie charts and bar charts, these plots don’t have categories. Timeplots are good for showing how data changes over time. For instance, this sort of chart would work well if you were sampling data randomly times.

Steps Needed

1. Import packages
2. Import / Load / Create data.
3. Plot the time series plot over data using lineplot (as tsplot was replaced with lineplot since Sep 2020).

Examples

Here, we create a rough data for understanding the time series plot with the help of some examples. Let’s create the data :

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