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Showing posts with the label Data visualization

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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' ]) ...
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What is so Trendy in Data Visualization and Reporting

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Data Visualization: Data visualization is the process that defines any effort to assist people to understand the importance of data by placing it in a visual context.  Patterns, trends, and correlations that might be missed in text-based data can be represented and identified with data visualization software. It is a graphical representation of numerical data. This is one of the Hot skills in the market, you will get the highest salary. Types of data visualization Visual Reporting Visual reporting uses charts and graphics to represent business performance, usually defined by metrics and time-series information. The best dashboards and scorecards enable the users to drill down one or more levels to view more detailed information about a metric A dashboard is a visual exception report that signifies the ambiguities in performances using visualization techniques Visual Analysis Visual-analysis allows users to visually explore the data to observe the data and dis...
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5 Emerging Trends in Data Visualization

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Nowadays, we deeply drowned in data of diverse kinds due to the increased computational power and accessibility. Specifically, in addition to public data available on the Internet (e.g., census, demographics, environmental data), data pertaining personal daily activities are now more easily collected. For example, through mobile devices that can log people's running distances and time or their manual record of nutrition consumption. Due to such expanded sources of data, there appear new applications that involve data collection, visualization, exploration, and distribution in daily contexts. These applications do, not only display static information but also let users navigate the data in forms of interactive visualizations. 5 Emerging Trends in Data Visualization. #1: This emerging trend has brought both opportunities and challenges to interaction designers to develop new approaches to designing data-based applications. #2: Conveying information has been one of main functio...
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