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Showing posts with the label Cleaning data

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Top Questions People Ask About Pandas, NumPy, Matplotlib & Scikit-learn — Answered!

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 Whether you're a beginner or brushing up on your skills, these are the real-world questions Python learners ask most about key libraries in data science. Let’s dive in! 🐍 🐼 Pandas: Data Manipulation Made Easy 1. How do I handle missing data in a DataFrame? df.fillna( 0 ) # Replace NaNs with 0 df.dropna() # Remove rows with NaNs df.isna(). sum () # Count missing values per column 2. How can I merge or join two DataFrames? pd.merge(df1, df2, on= 'id' , how= 'inner' ) # inner, left, right, outer 3. What is the difference between loc[] and iloc[] ? loc[] uses labels (e.g., column names) iloc[] uses integer positions df.loc[ 0 , 'name' ] # label-based df.iloc[ 0 , 1 ] # index-based 4. How do I group data and perform aggregation? df.groupby( 'category' )[ 'sales' ]. sum () 5. How can I convert a column to datetime format? df[ 'date' ] = pd.to_datetime(df[ 'date' ]) ...

10 Excusive Steps You need for Web Scrapping

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Here're ten Python technics to clean the scraped data. The scraped  Text has unwanted hidden data . So, as part of cleaning it try to remove these ten in your data. 10 Steps for Web scrapping Data is prime input for  text analytics projects . After cleaning, you can feed to Machine/Deep Learning systems. Removing HTML tags Tokenization Removing unnecessary tokens and stop-words Handling contractions Correcting spelling errors Stemming Lemmatization Tagging Chunking Parsing 10 Technics to Clean Text in Python 1. Removing HTML tags The unstructured text contains a lot of noise ( data from web pages, blogs, and online repositories.)when you use web/screen scraping.  The HTML tags, JavaScript, and Iframe tags typically don't add much value to understanding and analyzing text. Our purpose is to remove HTML tags, and other noise. 2. Tokenization Tokens are independent and minimal textual components. And have a definite syntax and semantics. A paragraph of text or a text documen...