How to handle large datasets and clean a dataset to prepare it for analysis in python
The goal of this project is to clean a dataset in order to make it suitable for study. It looks to be an attribute of a wider machine-learning project.
The process of data cleaning:
The script starts by importing the required libraries and then loading the data using the Pandas library from a CSV file. It then goes on to carry out data cleaning operations, including deleting pointless columns, dealing with missing values, and changing data types.
The first step is to eliminate a column that is thought to be superfluous to the analysis. Following that, the script finds any missing values in the dataset and deals with them by either adding the proper values or deleting the offending rows.
The next step is to change some columns' data types to ones that are more suitable. The script converts a column of date strings to a DateTime format, which is necessary for time-series analysis, using the Pandas function datetime().
The script also handles duplicate values and clarifies some columns by renaming some of them. The cleaned dataset is then exported to a fresh CSV file for additional investigation.
The tasks listed below in this project were accomplished according to the dataset:
- Put the raw data.csv file into a Pandas DataFrame.
- Examine the data for duplicate records and missing values.
- Clear the data of any duplicate records.
- Use the mode of the corresponding columns to fill in missing values in the gender, marital status, and city columns.
- Add a new column called age that lists each customer's age according to their DOB.
- Add a new column called income group that divides clients into three categories depending on their income values: "low," "medium," and "high," based on each 33% percentile.
- Add a new column called score group that divides clients into three categories depending on their score values: "bad," "fair," and "excellent" based on each 33% percentile.
- Eliminate any entries whose last purchase date is earlier than 2019. Save the cleaned data to a new file with the CSV extension clean data.csv.
Overall, this provided notebook or script offers a succinct and straightforward illustration of how to use Python and Pandas to carry out data-cleaning tasks. The code is organized and well-commented, making it simple to read and comprehend.
Source Code: https://shortyget.com/tdodBs4
