How can we replace or modify an existing Excel dataframe in pandas?

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There are several ways to replace or modify an existing Excel dataframe in pandas:

1. Replace the entire dataframe: To replace the entire dataframe, simply reassign a new dataframe to the same variable. For example, if the existing dataframe is called "df", you can replace it with a new dataframe "new_df" using the following code:

   df = new_df
   

2. Modify a single value: To modify a single value in the dataframe, use the .loc accessor to select the row and column of the value you want to modify, and then assign a new value to it. For example, to modify the value in row 2 and column 'Name', you can use the following code:

   df.loc[2, 'Name'] = 'New Name'
   

3. Modify a subset of the dataframe: To modify a subset of the dataframe that meets certain conditions, use the .loc accessor with a boolean mask. For example, to modify the 'Age' column for all rows where the 'Gender' column is 'Male', you can use the following code:

   df.loc[df['Gender'] == 'Male', 'Age'] = 30
   

4. Add a new column: To add a new column to the dataframe, simply assign a new column name to the dataframe with a list of values that correspond to each row. For example, to add a new column 'City' with the same value 'New York City' for every row, you can use the following code:

   df['City'] = 'New York City'
   

5. Rename a column: To rename a column in the dataframe, use the .rename() method and pass a dictionary that maps the old column names to the new ones. For example, to rename the 'Age' column to 'Years', you can use the following code:

   df = df.rename(columns={'Age': 'Years'})