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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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Big Data:Top Hadoop Interview Questions (1 of 5)

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Looking out for Hadoop Interview Questions that are frequently asked by employers? Here is the first list of Hadoop Interview Questions which  covers HDFS…

hadoop 1 0f 5


1. What is BIG DATA?
Big Data is nothing but an assortment of such a huge and complex data that it becomes very tedious to capture, store, process, retrieve and analyze it with the help of on-hand database management tools or traditional data processing techniques.

2. Can you give some examples of Big Data?
There are many real life examples of Big Data! Facebook is generating 500+ terabytes of data per day, NYSE (New York Stock Exchange) generates about 1 terabyte of new trade data per day, a jet airline collects 10 terabytes of censor data for every 30 minutes of flying time. All these are day to day examples of  Big Data!

3. Can you give a detailed overview about the Big Data being generated by Facebook?
As of December 31, 2012, there are 1.06 billion monthly active users on facebook and 680 million mobile users. On an average, 3.2 billion likes and comments are posted every day on Facebook. 72% of web audience is on Facebook. And why not! There are so many activities going on facebook from wall posts, sharing images, videos, writing comments and liking posts, etc.  In fact, Facebook started using Hadoop in mid-2009 and was one of the initial users of Hadoop.

4. According to IBM, what are the three characteristics of Big Data?
According to IBM, the three characteristics of Big Data are:
Volume: Facebook generating 500+ terabytes of data per day.
Velocity: Analyzing 2 million records each day to identify the reason for losses.
Variety: images, audio, video, sensor data, log files, etc.

5. How Big is ‘Big Data’?
With time, data volume is growing exponentially. Earlier we used to talk about Megabytes or Gigabytes. But time has arrived when we talk about data volume in terms of terabytes, petabytes and also zettabytes! Global data volume was around 1.8ZB in 2011 and is expected to be 7.9ZB in 2015. It is also known that the global information doubles in every two years!

6. How analysis of Big Data is useful for organizations?
Effective analysis of Big Data provides a lot of business advantage as organizations will learn which areas to focus on and which areas are less important. Big data analysis provides some early key indicators that can prevent the company from a huge loss or help in grasping a great opportunity with open hands! A precise analysis of Big Data helps in decision making!

For instance, nowadays people rely so much on Facebook and Twitter before buying any product or service. All thanks to the Big Data explosion.

7. Who are ‘Data Scientists’?
Data scientists are soon replacing business analysts or data analysts. Data scientists are experts who find solutions to analyze data. Just as web analysis, we have data scientists who have good business insight as to how to handle a business challenge. Sharp data scientists are not only involved in dealing business problems, but also choosing the relevant issues that can bring value-addition to the organization.

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