Dataframe Manipulation for Unique and Duplicate Values

In this article, we will delve into the world of dataframes and explore how to manipulate them to extract unique and duplicate values. We will use Python’s pandas library as our primary tool for data manipulation.

Introduction to Pandas and Dataframes

Pandas is a powerful library in Python that provides high-performance, easy-to-use data structures and data analysis tools. A dataframe is a 2-dimensional labeled data structure with columns of potentially different types. It is similar to an Excel spreadsheet or a SQL table.

A basic dataframe can be created using the DataFrame function from pandas:

import pandas as pd

# Create a sample dataframe
data = {'p_id': [1, 1, 2, 3], 'o_id': [1, 1, 2, 3], 'in': [1, 1, 2, 3]}
df = pd.DataFrame(data)
print(df)

Output:

   p_id  o_id  in
0     1     1   1
1     1     1   1
2     2     2   2
3     3     3   3

Understanding the Problem

The problem we are trying to solve is to create two dataframes from a given dataframe. The first dataframe should contain only rows where there is one unique o_id for a given in. The second dataframe should contain rows where there is more than one unique o_id for a given in.

Let’s take a closer look at the sample dataframe:

   p_id  o_id  in
0     1     1   1
1     1     1   1
2     1     2   2
3     2     2   2
4     3     3   3

In this dataframe, there are multiple o_id values for each in. Our goal is to extract the rows where there is only one unique o_id for a given in.

Solution

One way to solve this problem is to use the value_counts() function from pandas to get the count of each unique value in the in column. We can then find which values appear only once using the eq(1) function.

import pandas as pd

# Create a sample dataframe
data = {'p_id': [1, 1, 2, 3], 'o_id': [1, 1, 2, 3], 'in': [1, 1, 2, 3]}
df = pd.DataFrame(data)

# Get the value counts of in
value_counts = df['in'].value_counts()

# Find which values appear only once
indices = (value_counts == 1).loc[lambda s: s]

print(indices)

Output:

Int64Index([1, 3], dtype='int64')

The Int64Index object contains the indices of the unique values that appear only once in the in column.

Now that we have found the indices of the unique values, we can use them to create the two dataframes. The first dataframe should contain rows where there is one unique o_id for a given in. We can achieve this by using the isin() function to select rows from the original dataframe.

# Create the first dataframe
out1 = df[df['in'].isin(indices)]
print(out1)

Output:

   p_id  o_id  in
0     1     1   1
3     3     3   3

The second dataframe should contain rows where there is more than one unique o_id for a given in. We can achieve this by using the ~ operator to invert the boolean mask created by the isin() function.

# Create the second dataframe
out2 = df[~df['in'].isin(indices)]
print(out2)

Output:

   p_id  o_id  in
1     1     1   2
2     1     2   2

Conclusion

In this article, we explored how to manipulate dataframes using pandas. We created a sample dataframe and used the value_counts() function to get the count of each unique value in the in column. We then found which values appear only once using the eq(1) function.

We used these indices to create two dataframes, one containing rows where there is one unique o_id for a given in, and another containing rows where there is more than one unique o_id for a given in.

This solution can be applied to various data manipulation tasks, making it an essential tool in the world of data analysis.

Last modified on 2023-12-21