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Output:
                  Original DataFrame:

                       Name   Age Address Qualification
                  0    Adit  27.0   Delhi           M.Sc.
                  1    Ekam   NaN  Kanpur            MA
                  2  Sakshi  25.0     NaN            MCA
                  3     Anu  30.0  Indore           Ph.D.

                  Missing values in each column:
                  Name             0
                  Age              1
                  Address          1
                  Qualification    0
                  dtype: int64
                  Total number of NaN values:

                  2

                  DataFrame after dropping rows with NaN values:
                     Name   Age Address Qualification
                  0  Adit  27.0   Delhi           M.Sc.
                  3   Anu  30.0  Indore           Ph.D.

                  DataFrame after filling NaN values:
                       Name   Age  Address Qualification

                  0    Adit  27.0    Delhi           M.Sc.
                  1    Ekam  27.0   Kanpur            MA
                  2  Sakshi  25.0  Chennai            MCA
                  3     Anu  30.0   Indore           Ph.D.

              Attributes of DataFrames
              In Pandas, attributes are properties that describe various aspects of the DataFrame's structure and content. Attributes
              do not perform any computation or modification on the data; instead, they provide access to metadata or properties
              of the object. They are accessed using dot notation and do not require parentheses to invoke. The syntax for using an
              attribute is:


                                                    DataFrame_name.attribute

                Program 55: To create sample DataFrame to illustrate its common attributes and methods

                   import pandas as pd
                   # Create a DataFrame with some product data
                   data = {

                       'Product': ['Laptop', 'Tablet', 'Smartphone', 'Monitor'],
                       'Price': [1000, 500, 800, 300],
                       'Stock': [50, 150, 200, 100],


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