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.
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Table of Contents
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
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Ed Godalle
I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills... Read More