It uses Cython backend which transpiles into C for optimized code.
Here's how a dataframe looks like,
Pandas is an amazing data analysis and manipulation library for python, Really popular when working with ML, Data science or more.
It has a robust data structure, Dataframe for manipulation and analyzing data.
Here's some tips to help you work better with pandas. Let's go! ↓
It uses Cython backend which transpiles into C for optimized code.
Here's how a dataframe looks like,
Here are the tips, Let's go!
We all have struggled, when we deal with pandas series. It's always easier to work with Dataframes, rather than series. Here is how you can convert series to dataframe easily.
We always need dataframes for testing and analysing normally, if we do not have data ready. Here is how you can use Pandas API to generate different types of data.
We sometimes need to remove a column from dataframe, and use it separately for other use. Here is how you can do that!
In order to rename specific columns, or Override everything, or just change things a bit easily, Here is how you can do renaming in 2 major ways, along with a tip to lowercase column names.
Sometimes, We need to add specific naming patterns to the columns names in a dataframe, such as adding `_train` to end, or `test_` in front. Here's the easiest way to do that!
Sometimes, We have the need to reverse a dataframe, either by columns, or rows. This is the perfect way to do it!
Sometimes columns are in the wrong data type which needs to be fixed, and We need to create a column from existing ones for several purpose.
An easy way to do it, is this,
There are several instances, where we need to select column by data type. For example, processing and cleaning categorical and numerical variables separately. Here is how to select them differently, really easily!
When a column is given the type of "object", It may or may not have multiple types of data. Here is an easy way to check that!
There are a lot of instances, where we need to organize continuous variable into categories. Such as age column can be organized by Giving categories.
0-18: Child
18-66: Adult
66-100: Seniors
Here's how to do it,
Sometimes, When we have Date, Month and Year as different columns, It can get pretty hard, since it's Time-Series data. In order to combine them, Here is how that can be done.
There are a lot of times, When we need to compare between pandas Series, or Dataframes, and Check if they're same. Here is the right way to do it!
Pandas provides a REALLY EASY way to perform plotting and create graphs combined with matplotlib library. Matplotlib's a story, for another day.
Here is the link to official docs for tutorial on that: https://t.co/Embc3jwD8u
Let's see how to!
A lot of instances, Large data when loaded, take up a lot of memory. Here is an easy and quick way to analyse the memory usage.
Real world, does have a lot of missing data. Time-Series is a one type of data, which has that too! Pandas makes it really easy to impute and fill them. Here's how to
Shuffling rows is really useful, especially when randomizing the dataset before Splitting and feeding into model to ensure randomness. Here's the easiest way to do.
When working with ML or Data science, Usually we need to split data into 2 sets, Training and Test. Sometimes even validation.
Here is how you can do that using Pandas.
PS: Scikit learn can do this too using train_test_split
Warnings are usually redundant, except some cases. So, Here is how you can directly ignore them from Python.
There are some libraries which can perform auto EDA, gather insights automatically from the dataset, and Display it! This is effective when you have done a lot of EDA, Analyze rest of it easily, and quickly.
We'll be using Pandas profiling. Here's how to do it.
Really enjoyed sharing these useful functions in Pandas with you! More amazing content, Coming soon, next time!
Let me know, what you think! And If you're feeling generous, Do retweet to share with everyone else!
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Stay safe and take care! Thank you for everything, See you next time ❤️ Bye!
