Performing Union on Three Group By Resultant Dataframes with Same Columns, Different Order

In this article, we’ll explore how to perform union (excluding duplicates) on three group by resultant dataframes that have the same columns but different orders. We’ll use pandas as our data manipulation library and cover various approaches to achieve this goal.

Introduction

When working with grouped data in pandas, it’s often necessary to combine multiple dataframes into a single dataframe while excluding duplicate rows. In this case, we’ll focus on three dataframes that have the same columns but different orders. We’ll delve into different methods for achieving this union and discuss their strengths and limitations.

Background

To understand the problem at hand, let’s first create our sample dataframes using pandas:

# Create the first dataframe (df1)
import pandas as pd

Resultdf = SessionDev.query(AppDetails).filter(text(" A in ('20170727L00319')")).all()
df1 = Resultdf.groupby(["A", "B","C"]).size().reset_index(name='Count')

# Create the second dataframe (df2)
df2 = Resultdf.groupby(["A","C"]).size().reset_index(name='Count')

# Create the third dataframe (df3)
df3 = Resultdf.groupby(["B","C"]).size().reset_index(name='Count')

These dataframes will be our starting point for performing union and excluding duplicates.

Approaches to Union

Now that we have our three dataframes, let’s explore different methods for achieving a union of these dataframes while excluding duplicates:

Method 1: Concatenation with Drop_duplicates

One approach is to concatenate the three dataframes using pd.concat() and then remove duplicate rows using drop_duplicates(). Here’s an example implementation:

# Create a list of dataframes
dataframes = [df1, df2, df3]

# Concatenate the dataframes
FinalUnion = pd.concat(dataframes, ignore_index=True)

# Remove duplicates (excluding first occurrence)
FinalUnion.drop_duplicates(['B','C'], keep='first', inplace=True)

However, we may notice that this approach doesn’t always produce the desired result. Let’s see why.

Why Concatenation and Drop_duplicates Fails

The issue lies in the data types of the columns involved. Specifically, for the first two dataframes (df1 and df2), column [C] has a datatype of object (i.e., varchar), while for the third dataframe (df3), column [C] has a datatype of int64. When we concatenate these dataframes using pd.concat(), pandas attempts to reconcile the differing datatypes by converting them to a common format. However, this conversion process can lead to unexpected results when trying to remove duplicates.

Method 2: Converting Datatype

To resolve this issue, we need to ensure that all columns involved have consistent datatypes before performing union and removing duplicates. One way to do this is by dynamically converting the datatype of column [C] in the first two dataframes using pd.to_numeric():

# Convert dtype of column [C] for df1 and df2
df1[["C"]] = df1[["C"]].apply(pd.to_numeric)
df2[["C"]] = df2[["C"]].apply(pd.to_numeric)

# Create a list of dataframes
dataframes = [df1, df2, df3]

# Concatenate the dataframes
FinalUnion = pd.concat(dataframes, ignore_index=True)

# Remove duplicates (excluding first occurrence)
FinalUnion.drop_duplicates(['B','C'], keep='first', inplace=True)

With this modification, we should now obtain a consistent result for removing duplicate rows.

Alternative Approaches

While concatenation and removing duplicates is an effective approach, there may be other methods that achieve the same goal. Here are some additional strategies:

Method 3: Union with set.union() and sort_values()

Another way to perform union while excluding duplicates is by using set.union() from pandas’ set data structure. We can create sets of indices for each dataframe and then use set.union() to combine them:

# Create a list of dataframes
dataframes = [df1, df2, df3]

# Create sets of indices
indices_set1 = df1.set_index(['B','C']).index
indices_set2 = df2.set_index(['A','C']).index
indices_set3 = df3.set_index(['B','C']).index

# Perform union on indices
union_indices = set(indices_set1).union(set(indices_set2)).union(set(indices_set3))

# Create a new dataframe with the combined index
FinalUnion = pd.DataFrame({'A':[], 'B': [], 'C': []}, index=union_indices)

# Populate the new dataframe
for df in dataframes:
    if df.index.isin(union_indices):
        FinalUnion.update(df.set_index(['B','C']).to_dict('list'))

This approach allows us to control which columns are involved in the union and removes duplicates based on those columns.

Method 4: Using merge() with outer join

Another alternative is to use merge() from pandas’ merging functions. We can create a left-outer join of each dataframe against itself, which will effectively remove duplicate rows:

# Create a list of dataframes
dataframes = [df1, df2, df3]

# Perform left-outer joins
for i in range(len(dataframes)):
    for j in range(i+1, len(dataframes)):
        FinalUnion = pd.merge(FinalUnion, dataframes[j], how='left', on=['B','C'])

However, this approach may not always produce the expected result due to differences in datatypes and other factors.

Conclusion

Performing union while excluding duplicates on grouped dataframes can be a challenging task. In this article, we explored different methods for achieving this goal, including concatenation with drop_duplicates(), converting datatype, using sets and set.union(), and merging with outer joins. Each approach has its strengths and limitations, and the choice of method depends on the specific requirements of your use case.

We also highlighted the importance of consistent datatypes in pandas dataframes and demonstrated how to dynamically convert datatypes using pd.to_numeric(). By taking these considerations into account, you can effectively perform union while removing duplicates on grouped dataframes with different columns.

Last modified on 2024-11-21