Selecting, Extracting and Slicing Dataframes Pandas
Overview
Selecting, Extracting and Slicing dataframe in Pandas are frequently used to retrieve data. These are actively applied in many domains, including data exploration, analysis, and visualization. It is easier to carry out actions with these customized dataframes when just the relevant fields are provided.
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Introduction
When we need a specific subset of a dataframe, we use Slicing. Slicing dataframe Pandas, as the name suggests, separates the dataframe as required by our parameters. Dataframes can be sliced either by rows or columns.
Selecting Dataframe in Pandas is quite similar to slicing, where the difference is the indexing of the dataframe when the method is being applied. Another difference is that selecting is inclusive of the parameters used, like the row index or location of separation, while slicing the dataframe is not.
Whereas Extracting is simply returning the group of values based on a pattern or location.
Slicing a Pandas Dataframe
Let us consider the Spotify top 50 songs in the 2021 dataset for Dataframe Slicing.
Output :
The dataset has 18 columns and 50 rows
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Loc :
It is used to get a group of rows or coloums in a specific location. This method can be used with coloums and arrays.
Example - 1 :
Example - 2 :
Example - 3 :
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Iloc :
Iloc is used to get values from a dataframe based on indices. It only takes in integer values, unlike loc, which uses column names as well for locating values.
Example - 1 :
Example - 2 :
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Selecting in a Pandas Dataframe
Let us consider the Harry Potter Movies dataset for this example.
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Using Labels :
Labels or coloum names can be directly used to extract values from a dataframe To understand each one of them, let us look at the below examples.
Example - 1 :
Example - 2 :
Example - 3 :
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Using Index Position :
Loc is also used to extract multiple rows in specific columns.
Example - 1 :
Example - 2 :
Extracting Information from Pandas Dataframe
Let us consider the Google Doodles dataset for this example.
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Using Loc
Among various other uses of loc as mentioned above, here is the one focusing on extracting multiple values from a dataframe.
Example :
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Using iat
Iat is used when we need to extract values using specific indices. This is quite similar to iloc. We also specifically use iat when we require a set of single values or a single value as the returned output.
Example :
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Using at
At is used to get single values from rows or columns. This method is similar to iat, although here, we can also extract values using column names.
Example :
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Conclusion
- Selecting, Slicing, and Extraction are the main tools for data retrieving in Pandas.
- Selecting Dataframe in Pandas is retrieving values y indexing.
- Slicing Dataframe Pandas is similar to digging into a pizza slice and choosing the required slice.
- Extraction in Pandas is purely indexing without using actual values.
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