Time series may be a series of knowledge points during which each datum is related to a timestamp. an easy example is that the price of a stock within the stock exchange at different points of your time on a given day. Another example is that the amount of rainfall during a region at different months of the year.

In the below example we take the worth of stock prices a day for 1 / 4 for a specific symbol . We capture these values as a csv file then organize them to a dataframe using pandas library. We then set the date field as index of the dataframe by recreating the extra Valuedate column as index and deleting the old valuedate column.

Sample Data

Below is that the sample data for the worth of the stock on different days of a given quarter. the info is saved during a file named as stock.csv

ValueDate Price
01-01-2018, 1042.05
02-01-2018, 1033.55
03-01-2018, 1029.7
04-01-2018, 1021.3
05-01-2018, 1015.4
...
...
...
...
23-03-2018, 1161.3
26-03-2018, 1167.6
27-03-2018, 1155.25
28-03-2018, 1154

Creating statistic

from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('path_to_file/stock.csv')
df = pd.DataFrame(data, columns = ['ValueDate', 'Price'])
# Set the Date as Index
df['ValueDate'] = pd.to_datetime(df['ValueDate'])
df.index = df['ValueDate']
del df['ValueDate']
df.plot(figsize=(15, 6))
plt.show()

Its output is as follows −

So, this brings us to the end of blog. This Tecklearn ‘Time Series in Python’ blog helps you with commonly asked questions if you are looking out for a job in Python Programming. If you wish to learn Python and build a career in Data Science domain, then check out our interactive, Python with Data Science Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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