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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
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
In [2]:
## How to generate timeseries using Pandas and Seaborn
def Snippet_115():
print()
print(format('How to generate timeseries using Pandas and Seaborn','*^82'))
import warnings
warnings.filterwarnings("ignore")
# load libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
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',
'2014-05-03 18:47:05.332662', '2014-05-03 18:47:05.385109',
'2014-05-04 18:47:05.436523', '2014-05-04 18:47:05.486877'],
'deaths_regiment_1': [34, 43, 14, 15, 15, 14, 31, 25, 62, 41],
'deaths_regiment_2': [52, 66, 78, 15, 15, 5, 25, 25, 86, 1],
'deaths_regiment_3': [13, 73, 82, 58, 52, 87, 26, 5, 56, 75],
'deaths_regiment_4': [44, 75, 26, 15, 15, 14, 54, 25, 24, 72],
'deaths_regiment_5': [25, 24, 25, 15, 57, 68, 21, 27, 62, 5],
'deaths_regiment_6': [84, 84, 26, 15, 15, 14, 26, 25, 62, 24],
'deaths_regiment_7': [46, 57, 26, 15, 15, 14, 26, 25, 62, 41]}
df = pd.DataFrame(data, columns = ['date', 'battle_deaths', 'deaths_regiment_1',
'deaths_regiment_2', 'deaths_regiment_3', 'deaths_regiment_4',
'deaths_regiment_5', 'deaths_regiment_6', 'deaths_regiment_7'])
df = df.set_index(df.date)
# dataFrame
print(); print(df)
# Time Series Plot
sns.tsplot([df.deaths_regiment_1, df.deaths_regiment_2, df.deaths_regiment_3, df.deaths_regiment_4,
df.deaths_regiment_5, df.deaths_regiment_6, df.deaths_regiment_7])
plt.show()
Snippet_115()
In [ ]:
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