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Working with inaccurate or poor quality data may result in flawed outcomes. Hence it is essential to review the data and ensure its quality before beginning the analysis process. Ignoring DataCleaningData cleansing is an important step to correct errors and removes duplication of data.
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In this blog, we’ll be using Python to perform exploratory data analysis (EDA) on a Netflix dataset that we’ve found on Kaggle. We’ll be using various Python libraries, including Pandas, Matplotlib, Seaborn, and Plotly, to visualize and analyze the data. The type column tells us if it is a TV show or a movie. df.isnull().sum()
Top 15 Data Analytics Projects in 2023 for Beginners to Experienced Levels: Data Analytics Projects allow aspirants in the field to display their proficiency to employers and acquire job roles. Kaggle datasets) and use Python’s Pandas library to perform datacleaning, data wrangling, and exploratory data analysis (EDA).
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