Must-Know DataFrame Manipulation Techniques for Data Analysts

The pandas DataFrames are a core component of data analysis in Python. They provide an effective way to handle and manipulate tabular data. However, data in a DataFrame is not always presented in a format that suits the analysis task at hand. Often, it is necessary to restructure the DataFrame for more effective analysis. In this article, we will explore three essential DataFrame manipulation techniques that can enhance your data analysis tasks.

1. Inserting a Column at a Specific Place

By default, appending a new column to a DataFrame will add the column to the end of the DataFrame. However, sometimes, you need to insert a new column at a specific position in your DataFrame, rather than appending it to the end. This is usually important if you want to maintain a logical order of columns. To demonstrate how this can be done, we are going to use a dataset from Kaggle. First, let's load pandas and then load the dataset.

Now we want to calculate the total score by summing the "Math_Score", "Reading_Score", "Writing_Score", and "Placement_Score" columns and inserting the column (total_score) at index 4 (between the "Placement_Score" and "Club_Join_Date" columns). We are going to use the insert() method. We pass the index (4), the name of the column (total_score) the data (df[columns_to_sum].sum(axis=1)) to this method. Here is the complete code:

You can see that the "total_score" column has been inserted between the "Placement_Score" and "Club_Join_Date" columns. This technique ensures that the columns are in a logical and preferred order, making the DataFrame easier to read and analyze.

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2. Changing the Order of Columns

Columns in a dataset may not always be presented in an order that is presentation-friendly or that aligns with a specific schema. Let's continue with the DataFrame from the previous example. We want to change the order of the columns by making "Club_join_Date" the first column (index 0). The order of the other columns will not be changed. To change the order, we use the reindex() method. This method is used to change the index of rows and columns of a DataFrame. In the code below, we pass the columns to the method in the order that we want them to appear in the DataFrame.

You can see in the output that the "Club_join_Date" column is now the first column of the DataFrame and the order of the other columns has not changed.

3. Reshape DataFrame from Wide to Long Format

For reasons such as better analysis and visualization, you may want to reshape your DataFrame from wide to long format. Let's assume you want to expose the relationship between "Club_Join_Date" and the "total_score. " Let's say you want to analyze how the total score changes based on the "Club_Join_Date." We can use the pd.melt() function to reshape the DataFrame from wide to long format. Here is the code below;

In the code, id_vars=['Club_Join_Date'] specifies the columns to keep as identifiers. In this case, we are keeping the "Club_join_Date" column as an identifier. The value_vars=['total_score'] is the columns that we are pivoting (i.e., convert from columns to rows). We are giving this pivoted variable name "description. " The values of the pivoted column are in the column "Values" (value_name='Values'). This create a new DataFrame that we have saved to a variable called "df_melted."

This long format makes it easy to analyze the relationship between the "Club_Join_Date" and the "total_score" columns. For example, we may use this long format to visualize the relationship between the two variables. Let's create a bar plot using Seaborn:

Just by looking at the plot, the total scores for each year (2018, 2019, 2020, and 2021) appear to be relatively similar, with values around 300. There has been no significant increase or decrease in total scores over the years, indicating stable performance.

Conclusion

Learning the techniques used in manipulating DataFrames, will greatly enhance your analysis capabilities. These are three essential DataFrame manipulation techniques that can greatly enhance your data analysis tasks. Whether you need to insert a column at a specific place, change the order of columns, or reshape your DataFrame, pandas provides the necessary functions to perform these tasks efficiently. Thanks for reading.

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