Introduction to Pandas Time Series - ( Pandas Time Series )

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Syntax

import pandas as pd

# Creating a time series object
ts = pd.Series(data, index=index)

Example

import pandas as pd

# Creating a time series object
ts = pd.Series([25, 30, 35, 40, 45], index=pd.date_range('20220101', periods=5))

print(ts)

Output

2022-01-01    25
2022-01-02    30
2022-01-03    35
2022-01-04    40
2022-01-05    45
Freq: D, dtype: int64

Explanation

Pandas Time Series is a powerful tool for working with time series data in Python. It allows you to manipulate, analyze and visualize time-series data easily and efficiently. In Pandas, the pd.Series() function can be used to create a time series object by passing in a list of data and an index in the form of pd.date_range().

In the above example, we create a time series object ts containing 5 values from 25 to 45 with daily frequency, starting from 2022-01-01 using pd.Series() and pd.date_range() functions.

Use

Pandas Time Series can be used to perform various operations on time series data such as data cleaning and manipulation, resampling, aggregation, and visualization. It is widely used in industries such as finance, economics, and healthcare to analyze and predict trends in time-series data.

Important Points

• Pandas Time Series is a powerful tool for working with time series data in Python
• It allows you to manipulate, analyze and visualize time-series data easily and efficiently
• In Pandas, the pd.Series() function can be used to create a time series object by passing in a list of data and an index in the form of pd.date_range()
• Time series data can be easily manipulated, resampled, aggregated and visualized using Pandas Time Series
• Pandas Time Series is widely used in industries such as finance, economics, and healthcare

Summary

In conclusion, Pandas Time Series is a powerful tool for working with time series data in Python. It allows you to manipulate, analyze and visualize time-series data easily and efficiently. The pd.Series() function can be used to create a time series object in Pandas by passing in a list of data and an index. Pandas Time Series is widely used in industries such as finance, economics, and healthcare to analyze and predict trends in time-series data.