Finding Missing Values in Dataframes using LEFT JOIN

In this article, we will explore how to find missing values in one dataframe by performing a left join with another dataframe.

Introduction

Dataframe manipulation is an essential skill for any data scientist or analyst. In this article, we will discuss how to use the merge function from the pandas library in Python to perform a left join and identify missing values between two dataframes.

Understanding LEFT JOIN

A left join is a type of join that combines rows from two tables based on a common column. In the context of dataframes, it returns all records from the left dataframe (df1) and matching records from the right dataframe (df2). If there are no matches, the result will contain null values.

Using LEFT JOIN to Find Missing Values

To find missing values in one dataframe by performing a left join with another dataframe, we can follow these steps:

Step 1: Load the Dataframes

We start by loading our two dataframes into pandas dataframes using pd.read_sql. We assume that we have already connected to our database and are selecting the required columns.

esn_datafeed_df = pd.read_sql('SELECT * FROM [myDB].[dbo].[esn_datafeed]', engine)
esn_inter_intra_merge_df = pd.read_sql('SELECT * FROM [myDB].[dbo].[esn_inter_intra_merge]', engine)

Step 2: Perform the LEFT JOIN

Next, we perform a left join between our two dataframes using the merge function. We set the indicator flag to True which will help us identify rows that are only present in one of the dataframes.

merged = esn_datafeed_df.merge(esn_inter_intra_merge_df, how='left', indicator=True)

Step 3: Filter for Left-Only Rows

We then filter our merged dataframe to include only rows where the _merge column is equal to 'left_only'. This will give us the missing values from df1.

merged.query("_merge == 'left_only'")[["st_umts_df_relation_key"]]

Example Use Case

Let’s take a look at an example use case. Suppose we have two dataframes: esn_datafeed_df and esn_inter_intra_merge_df. The first dataframe contains the following values:

And the second dataframe contains the following values:

We want to perform a left join between these two dataframes and find the missing values in esn_datafeed_df.

df1 = pd.DataFrame([1, 2, 3, 4, 5, 6], columns=["Col1"])
df2 = pd.DataFrame([1, 2, 3], columns=["Col2"])

merged = df1.merge(df2, how="left", indicator=True)

The resulting dataframe will look like this:

As we can see, the values 5555, 6666, and 7777 are missing in df1.

Conclusion

In this article, we have discussed how to use the merge function from pandas to perform a left join between two dataframes. We then filtered our merged dataframe to include only rows where the _merge column is equal to 'left_only', giving us the missing values.

By following these steps, you can easily find missing values in one dataframe by performing a left join with another dataframe.

Best Practices

• Always check for null or missing values when working with dataframes.
• Use the indicator=True flag when merging dataframes to identify rows that are only present in one of the dataframes.
• Filter your merged dataframe using the _merge column to include only rows where the condition is met.

Additional Tips

• Make sure to check for memory issues when performing large joins, as this can lead to MemoryError exceptions.
• Consider using the on parameter when merging dataframes to specify a specific join key.

Last modified on 2023-06-16