How to generate timeseries using Pandas and Seaborn?
PANDAS CHEATSHEET DATA CLEANING PYTHON DATA MUNGING MACHINE LEARNING RECIPES     ALL TAGS

How to generate timeseries using Pandas and Seaborn?

How to generate timeseries using Pandas and Seaborn?

This recipe helps you generate timeseries using Pandas and Seaborn

0

Recipe Objective

Have you ever worked on time series data, a dataset in which targets depends on the time and data. For this you have to convert the data set into time series dataset.

This data science python source code does the following:
1.Creating your own pandas series and timestams them.
2. Visualizes the series using seaborn libraries

So this is the recipe on we can generate timeseries using Pandas and Seaborn.

Step 1 - Import the library

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns

We have imported pandas, seaborn and matplotlib.pyplot which is needed.

Step 2 - Setting up the Data

We have created a dictionary of data and passed it in pd.DataFrame to make a dataframe. In the dictionary we have many features named 'date', 'regiment_1', 'regiment_2', etc. We have set index as date and rest other as features. data = {'date': ['2014-05-01 18:47:05.069722', '2014-05-01 18:47:05.119994', '2014-05-02 18:47:05.178768', '2014-05-02 18:47:05.230071', '2014-05-02 18:47:05.230071', '2014-05-02 18:47:05.280592', '2014-05-04 18:47:05.436523', '2014-05-04 18:47:05.486877'], 'regiment_1': [14, 26, 25, 14, 31, 25, 62, 41], 'regiment_2': [52, 66, 78, 15, 25, 25, 86, 1], 'regiment_3': [13, 26, 25, 62, 24, 14, 15, 15], 'regiment_4': [44, 15, 15, 14, 54, 25, 24, 72], 'regiment_5': [25, 24, 5, 25, 25, 27, 62, 5], 'regiment_6': [14, 15, 15, 14, 26, 25, 62, 24], 'regiment_7': [46, 57, 26, 15, 26, 25, 62, 41]} df = pd.DataFrame(data, columns = ['date', 'regiment_1', 'regiment_2', 'regiment_3', 'regiment_4', 'regiment_5', 'regiment_6', 'regiment_7']) df = df.set_index(df.date) print(); print(df)

Step 3 - Making Time Series

We have passed features from sns.tsplot to make time series plot of different features with index as date. sns.tsplot([df.regiment_1, df.regiment_2, df.regiment_3, df.regiment_4, df.regiment_5, df.regiment_6, df.regiment_7]) plt.show() So the output comes as

                                                  date  regiment_1  \
date                                                                 
2014-05-01 18:47:05.069722  2014-05-01 18:47:05.069722          14   
2014-05-01 18:47:05.119994  2014-05-01 18:47:05.119994          26   
2014-05-02 18:47:05.178768  2014-05-02 18:47:05.178768          25   
2014-05-02 18:47:05.230071  2014-05-02 18:47:05.230071          14   
2014-05-02 18:47:05.230071  2014-05-02 18:47:05.230071          31   
2014-05-02 18:47:05.280592  2014-05-02 18:47:05.280592          25   
2014-05-04 18:47:05.436523  2014-05-04 18:47:05.436523          62   
2014-05-04 18:47:05.486877  2014-05-04 18:47:05.486877          41   

                            regiment_2  regiment_3  regiment_4  regiment_5  \
date                                                                         
2014-05-01 18:47:05.069722          52          13          44          25   
2014-05-01 18:47:05.119994          66          26          15          24   
2014-05-02 18:47:05.178768          78          25          15           5   
2014-05-02 18:47:05.230071          15          62          14          25   
2014-05-02 18:47:05.230071          25          24          54          25   
2014-05-02 18:47:05.280592          25          14          25          27   
2014-05-04 18:47:05.436523          86          15          24          62   
2014-05-04 18:47:05.486877           1          15          72           5   

                            regiment_6  regiment_7  
date                                                
2014-05-01 18:47:05.069722          14          46  
2014-05-01 18:47:05.119994          15          57  
2014-05-02 18:47:05.178768          15          26  
2014-05-02 18:47:05.230071          14          15  
2014-05-02 18:47:05.230071          26          26  
2014-05-02 18:47:05.280592          25          25  
2014-05-04 18:47:05.436523          62          62  
2014-05-04 18:47:05.486877          24          41  

Relevant Projects

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

Mercari Price Suggestion Challenge Data Science Project
Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.

German Credit Dataset Analysis to Classify Loan Applications
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Build a Collaborative Filtering Recommender System in Python
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

Machine Learning or Predictive Models in IoT - Energy Prediction Use Case
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Human Activity Recognition Using Multiclass Classification in Python
In this human activity recognition project, we use multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker.