Data Cleaning Operations: A Step-by-Step Video Tutorial322

Data cleaning is an essential step in any data analysis workflow. It involves removing duplicate data, correcting errors, and transforming data into a consistent format. By performing data cleaning operations, you can improve the quality of your data and make it more useful for analysis.

In this video tutorial, we will walk you through the most common data cleaning operations. We will show you how to use open-source tools such as Python and R to perform these operations on real-world datasets.

Step 1: Import the Data

The first step in data cleaning is to import the data into your analysis environment. You can do this using the read_csv() function in Python or the () function in R.```python
import pandas as pd
data = pd.read_csv('')
```
```r
data

2025-01-27

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