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I would like to create a class from a pandas dataframe that is created from csv. Is the best way to do it, by using a @staticmethod? so that I do not have to read in dataframe separately for each object
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In most cases, you'll use the DataFrame constructor and provide the data, labels, and other information. You can pass the data as a two-dimensional list, tuple, or NumPy array. You can also pass it as a dictionary or Pandas Series instance, or as one of several other data types not covered in this tutorial.
Arbitrary functions can be applied along the axes of a DataFrame or Panel using the apply() method, which, like the descriptive statistics methods, takes an optional axis argument. By default, the operation performs column wise, taking each column as an array-like.
Overview: The Python Data Analysis library pandas, provides the DataFrame class as a container for storing and manipulating two-dimensional data. In a nutshell a pandas DataFrame is a two-dimensional array with versatile computing capabilities. The DataFrame class encapsulates a two-dimensional array – a numpy.
The command to convert Dataframe to list is pd. DataFrame. values. tolist().
You don't need a @staticmethod for this. You can pass the pandas DataFrame whenever you're creating instances of the class:
class MyClass:
def __init__(self, my_dataframe):
self.my_dataframe = my_dataframe
a = MyClass(my_dataframe)
b = MyClass(my_dataframe)
At this point, both a and b have access to the DataFrame that you've passed and you don't have to read the DataFrame each time. You can read the data from the CSV file once, create the DataFrame and construct as many instances of your class as you like (which all have access to the DataFrame).
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I would think you could create the dataframe in the first instance with
a = MyClass(my_dataframe)
and then just make a copy
b = a.copy()
Then b is independent of a
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