When working with pandas Dataframes, there might be instances where you need to modify the original data, rather than creating a copy of it. This is where the inplace parameter comes in.
With this feature, it is possible to make changes to the original Dataframe without creating a new one. In this article, we will explore what inplace parameter is and how it works.
Meaning of Inplace Parameter:
Inplace parameter is a feature in pandas that allows you to modify a Dataframe directly.
This means that instead of creating a new Dataframe, you can modify the existing one. It is set as a Boolean value and is often found as an argument in many pandas functions.
Example of Removing Rows with NA Entries:
One common use of inplace parameter is in the dropna() function. This function is used to eliminate rows with NA entries in the Dataframe.
By default, dropna() does not modify the original Dataframe but instead returns a copy with the rows removed. However, if you set inplace=True when calling the function, you can modify the original Dataframe directly.
For example, consider the following code snippet:
data = {'name':['John', 'Tom', 'Harry', None, 'Bob'], 'age':[23, 24, None, 27, 22]}
df = pd.DataFrame(data)
df.dropna(inplace=True)
print(df)
In this code, we create a Dataframe with five rows, two of which have NA entries. The dropna() function is then called with inplace=True.
As a result, rows with NA entries are removed directly from the original Dataframe. The output of the above code will be:
name age
0 John 23
1 Tom 24
4 Bob 22
Understanding Inplace Parameter:
Functions with Inplace Parameter:
Many pandas functions have the inplace parameter, and some of these include dropna(), fillna(), and drop_duplicates(). These functions perform specific tasks on the Dataframe and can modify the original Dataframe if inplace=True is set.
Working of Inplace Parameter:
When the inplace parameter is set to True, the function performs the desired operation on the original Dataframe instead of creating a new one. This means that any modification made by the function will be reflected directly on the original Dataframe.
It is important to note that when using inplace parameter, some operations result in the loss of data, and once the modification is made, no undo operation exists. Therefore, it is crucial to ensure that the correct operation is performed before turning the parameter to True.
Copy, Assign, Modify, Original Dataframe:
When a function with inplace parameter is called, pandas makes a copy of the original Dataframe. This copy is then modified, and the changes are reflected back to the original Dataframe if inplace=True is set.
If inplace=True is not set and the function returns a new Dataframe, the modified copy is returned, leaving the original Dataframe untouched.
3) Inplace=True in Action:
Sorting Operation on IRIS Dataset:
The IRIS dataset is a popular dataset used for classification tasks in machine learning. This dataset contains information about different species of flowers, namely iris setosa, iris virginica, and iris versicolor.
To illustrate how inplace parameter works, let us consider sorting the IRIS dataset by the petal_width column. To do this, we first import the iris dataset using the popular seaborn library and create a Dataframe:
import seaborn as sns
iris = sns.load_dataset('iris')
df = pd.DataFrame(iris)
Next, we sort the Dataframe by the petal_width column and set inplace=True:
df.sort_values(by='petal_width', inplace=True)
This command sorts the Dataframe by the petal_width column and modifies the original Dataframe without creating a new one. This is useful if you want to sort the dataset and continue working with the sorted version.
Default Value of Inplace Parameter:
The default value of inplace parameter is False. This means that when a function with inplace=True is not specified, the function does not modify the original Dataframe but returns a new one with the desired changes.
For instance, consider the following code snippet, which shows how to group Dataframe df by the species column and assign the result to a new Dataframe called grouped_df.
grouped_df = df.groupby('species')
The grouped_df Dataframe is created without modifying the original df Dataframe.
If inplace=True is not specified in the groupby function, we can assign the result of the function to a new Dataframe.
Modifying Original Dataframe with Inplace=True:
As we have seen, setting inplace=True allows us to modify the original Dataframe instead of working on a copy.
This can be useful in situations where we want to make changes to the original data without creating a new version. However, it is crucial to be careful when using inplace=True since it does not create a copy of the original Dataframe before making the change.
This means that once a modification is made, it cannot be undone. Therefore, it is important to double-check before making any modifications to the original Dataframe.
For example, consider the following code snippet, in which we use inplace parameter to drop a column from the IRIS dataset:
df.drop(['sepal_length'], axis=1, inplace=True)
This code drops the sepal_length column from the IRIS dataset directly without creating a new Dataframe. The modification is made directly to the original Dataframe, meaning that the changes are permanent.
Therefore, it is important to verify that the modification is correct before using inplace=True.
4) Conclusion:
Inplace parameter is a useful feature in pandas that allows users to modify Dataframes without creating a new one.
This feature can be used to sort, group data, or remove rows or columns directly from the original Dataframe. However, it is important to be careful when using inplace=True to avoid any permanent damage to the original dataset.
Users should double-check the operation before setting inplace=True since the change cannot be undone once it is made. In conclusion, inplace=True is a powerful tool when used correctly.
By understanding how it works and using it cautiously, pandas users can effectively update the original Dataframe. Inplace parameter is a powerful feature of pandas that allows users to modify Dataframes without creating a new one.
With inplace=True, users can efficiently sort, group, or remove rows or columns directly from the original Dataframe. However, users should be careful when using inplace=True since the changes made to the original Dataframe are permanent and cannot be undone.
By understanding how this feature works and using it cautiously, pandas users can effectively update the original Dataframe without facing any unexpected data loss. It is crucial to verify before making any changes to the original Dataframe.