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

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  

Download Materials

Relevant Projects

Census Income Data Set Project - Predict Adult Census Income
Use the Adult Income dataset to predict whether income exceeds 50K yr based on census data.

Abstractive Text Summarization using Transformers-BART Model
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.

Inventory Demand Forecasting using Machine Learning in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Digit Recognition using CNN for MNIST Dataset in Python
In this deep learning project, you will build a convolutional neural network using MNIST dataset for handwritten digit recognition.

Machine Learning Project to Forecast Rossmann Store Sales
In this machine learning project you will work on creating a robust prediction model of Rossmann's daily sales using store, promotion, and competitor data.

Loan Eligibility Prediction in Python using H2O.ai
In this loan prediction project you will build predictive models in Python using H2O.ai to predict if an applicant is able to repay the loan or not.

Data Science Project-TalkingData AdTracking Fraud Detection
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.