How to do time series DATA Wrangling in a Pandas DataFrame in Python
In this Learn through Codes example, you will learn: How to do time series DATA Wrangling in a Pandas DataFrame in Python.
The code you provided is a Python script that demonstrates how to use the Pandas library to work with time series data in a DataFrame. The script starts by importing the Pandas library, datetime library and turning off warning messages. It also imports pyplot which is a matplotlib library that is used to plot the graph of car sales.
The script creates a DataFrame called “df” from a dictionary containing the date and car sales. The DataFrame is then printed to show its contents.
Next, it uses the pd.to_datetime() function to convert the ‘date’ column from a string to a datetime object, so that it can be used as the index of the DataFrame. Then it sets the ‘date’ column as the index and deletes the column. This allows for easy indexing and selecting of data based on the date. The Dataframe is then printed to confirm the changes.
The script then shows how to select specific portions of the time series data using the various indexing methods provided by Pandas. It uses the indexing of a pandas Dataframe to select all observations that occurred in 2014, May 2014, after May 3rd, 2014, between May 3rd and May 4th, etc.
It also demonstrates how to use the truncate() function to remove all observations after May 2nd, 2014. After that, it shows how to group the data by a specific column and how to count the number of observations per timestamp.
It then shows how to use the resample() function to resample the data by day and calculate the mean and sum of car sales per day. Finally, it plots the total car sales per day using the plot function of the pyplot library.
In conclusion, this script demonstrates how to use the Pandas library to work with time series data in a DataFrame. It shows how to load and manipulate time series data, how to select specific portions of the data based on the date, how to group and aggregate data, and how to resample the data at different frequencies. It also shows how to plot the data using matplotlib library. This is a powerful technique for working with time series data and provides many useful methods for data analysis and manipulation.
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