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Does anyone know the fundamental difference between the .filter method and the .loc method in Pandas? They seem to do the same thing. Thanks.
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We can specify a single label or a list of labels to filter the data in loc []. The filtered data can be of different types: a single value, a Series, or a DataFrame, as shown in the examples, respectively. When only a label or a list of labels is set, it will return all columns. Another common method is using the ranges of row and column labels.
Filter Pandas Dataframe by Column Value. Pandas makes it incredibly easy to select data by a column value. This can be accomplished using the index chain method. Select Dataframe Values Greater Than Or Less Than. For example, if you wanted to select rows where sales were over 300, you could write:
Pandas library of python is very useful for the manipulation of mathematical data and is widely used in the field of machine learning. It comprises of many methods for its proper functioning. loc () and iloc () are one of those methods.
These are used in slicing of data from the Pandas DataFrame. They help in the convenient selection of data from the DataFrame. They are used in filtering the data according to some conditions. Working of both of these methods is explained in the sample dataset of cars.
.loc[] is a Purely label-location based indexer for selection by label. It fails when the selection isn't found, only accepts certain types of input and works on only one axis of your dataframe.
df.filter() returns Subset rows or columns of dataframe according to labels in the specified index. You can filter along either axis, and you can filter in more advanced ways than with loc.
filter will return the same type of object as the caller, whereas loc will return the value specified by the label (so a Series if caller is a DF, a scalar if caller is a Series).
In short, .loc is for accessing a specific item within the caller, .filter() is for applying a filter to the caller and returning only items which match that filter.
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