Python plot time series python

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 :

Источник

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.

Sign up for Dash Club → Free cheat sheets plus updates from Chris Parmer and Adam Schroeder delivered to your inbox every two months. Includes tips and tricks, community apps, and deep dives into the Dash architecture. Join now.

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() 

Источник

How to Make a Time Series Plot with Rolling Average in Python?

Time Series Plot is used to observe various trends in the dataset over a period of time. In such problems, the data is ordered by time and can fluctuate by the unit of time considered in the dataset (day, month, seconds, hours, etc.). When plotting the time series data, these fluctuations may prevent us to clearly gain insights about the peaks and troughs in the plot. So to clearly get value from the data, we use the rolling average concept to make the time series plot.

The rolling average or moving average is the simple mean of the last ‘n’ values. It can help us in finding trends that would be otherwise hard to detect. Also, they can be used to determine long-term trends. You can simply calculate the rolling average by summing up the previous ‘n’ values and dividing them by ‘n’ itself. But for this, the first (n-1) values of the rolling average would be Nan.

In this article, we will learn how to make a time series plot with a rolling average in Python using Pandas and Seaborn libraries. Below is the syntax for computing rolling average using pandas.

Syntax: pandas.DataFrame.rolling(n).mean()

We will be using the ‘Daily Female Births Dataset’. This dataset describes the number of daily female births in California in 1959. There are 365 observations from 01-01-1959 to 31-12-1959. You can download the dataset from this link.

Let’s Implement with step-wise:

Step 1: Import the libraries.

Источник