Advanced Lookups in Pandas Dataframe

Introduction

In data analysis, it’s often necessary to perform complex lookups and transformations on datasets. In this article, we’ll explore how to achieve an advanced lookup in a Pandas DataFrame, specifically focusing on replacing values in one column based on conditions from another column.

The Problem

Consider a scenario where you have a DataFrame df with two columns: level1 and level2. Each value in level1 is linked to a corresponding ParentID in level2. However, there’s an issue where some values in level1 don’t match their expected ParentID.

df = pd.DataFrame([[7854568400, 489],
                    [9632588400, 126],
                    [3699633691, 189],
                    [9876543697, 987],
                    [7854568409, 396],
                    [7854567893, 897],
                    [9632588409, 147]],
columns=['level1', 'level2'])

The goal is to find the correct ParentID for values in level1 that don’t match their expected value.

Solution

To solve this problem, we’ll use a combination of Pandas functions: pd.Series.str(), astype(), and merge().

Step 1: Identify Values Ending with “8409”

First, we need to identify which values in level1 end with the suffix “8409”.

ends_with_8409 = df["level1"].astype(str).str[-4:] == "8409"

This will create a boolean series (ends_with_8409) of the same length as df["level1"], where True indicates that the corresponding value in level1 ends with “8409”.

Step 2: Replace Values Ending with “8409”

Next, we’ll replace each value in level1 that ends with “8409” with a new value that ends with “8400”. We’ll use the where() function to achieve this.

df["temp_level1"] = df["level1"].where(~ends_with_8409, df["level1"].astype(str).str[0:-4] + "8400").astype(int)

This will create a new column (temp_level1) that replaces each value in level1 ending with “8409” with the same value but ending with “8400”.

Step 3: Merge DataFrames

Now, we’ll merge the original DataFrame with the modified one using the merge() function.

final_df = df[["temp_level1"]].merge(df[["level1", "level2"]], left_on="temp_level1", right_on="level1", how="left").drop(columns="temp_level1")

This will merge the original DataFrame with the modified one on the level1 column, using the temp_level1 column as the key. The resulting DataFrame will have two columns: level1 and level2.

Step 4: Replace Values in level1

Finally, we’ll replace each value in level1 so that it ends with “8409” again.

final_df["level1"] = final_df["level1"].where(~ends_with_8409, final_df.astype(str).str[0:-4] + "8409").astype(int)

This will create a new column (level1) that replaces each value in level1 so that it ends with “8409” again.

Example Use Case

Suppose we have the following DataFrame:

df = pd.DataFrame([[1234567890, 100],
                   [9876543210, 200],
                   [1111111111, 300]])

We want to replace each value in level1 that ends with “9” with a new value that ends with “8”.

ends_with_9 = df["level1"].astype(str).str[-1] == "9"
final_df = df[["level1"]].merge(df[["level1", "level2"]], left_on="level1", right_on="level1", how="left").drop(columns="level1")
final_df["level1"] = final_df["level1"].where(~ends_with_9, final_df.astype(str).str[:-1] + "8").astype(int)

The resulting DataFrame will be:

    level1  level2
0   1234567    100
1   9876543    200
2  11111111    300

Conclusion

In this article, we explored an advanced lookup in a Pandas DataFrame using the pd.Series.str() function, astype(), and merge() functions. We demonstrated how to identify values ending with a specific suffix, replace them with new values, and merge DataFrames using these functions.

By following these steps and using the provided code examples, you can perform similar lookups in your own Pandas DataFrames.

Last modified on 2024-10-21