# Multiple conditions: NOT - dataframe with all emovies rated greater than 8 or having a metascore more than 90 have to be excluded # Multiple conditions: OR - dataframe with all movies rated greater than 8 or having a metascore more than 90 # Multiple conditions: AND - dataframe with all movies rated greater than 8 and having more than 100000 votesĪnd_df = df>8) & (df>100000)] # Single condition: dataframe with all movies rated greater than 8 Nothing as easy as splitting with a series appears to be possible. You’ll get an error if you try the below.
What would you do if you were in control of the operation?
#Python lambda movie
Let’s say we want to find rows where the number of words in the movie title is higher than or equal to four. However, complicated filtering techniques may be required at times.Īnd there are situations when we need to do operations that the above format will not allow us to execute. Will be using a data set of 1,000 popular movies on IMDB in the last 10 years. The goal of this post is to show you how powerful apply and lambda are. When a company comes to you with a special request, this happens frequently. When I’m stuck creating complex logic for a new column or filter, I turn to apply and lambda. Some of the most useful pandas features I’ve discovered are ‘apply()’ and ‘lambda()’. However, I’ve discovered that adhering to some of the conventions I’ve learned has been beneficial over time. I’ve been using Pandas for a long time, and it never ceases to amaze me with new features, shortcuts, and many methods to accomplish a task. Pandas is a most powerful, easy, and flexible to use open-source data analysis tool, built on top of the Python programming language. This article was published as a part of the Data Science Blogathon Overview
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