Cross Referencing and Converting Unique Rows to Columns with Pandas

Pandas is a powerful library in Python for data manipulation and analysis. It provides data structures and functions to efficiently handle structured data, including tabular data such as spreadsheets and SQL tables. In this article, we will explore how to cross reference data from two tables and convert unique rows into columns using pandas.

Understanding the Problem

We have two tables: Table A and Table B. Table A has an ‘id’ column and a ‘date’ column, while Table B has a ‘code’ column, an ‘id’ column, and a ‘stock’ column. We want to create a new table where the ‘id’ column from Table A is used for cross referencing with Table B, and the dates from Table A are used as new columns in the output table.

Table A and Table B Data

listData = {'id':[1,2,3],'date':['06-05-2021','07-05-2021','17-05-2021']}
tableA = pd.DataFrame(listData,columns=['id','date'])

detailData = {'code':['D123','F268','A291','D123','F268','A291'],'id':['1','1','1','2','2','2'],'stock':[5,5,2,10,11,8]}
tableB = pd.DataFrame(detailData,columns=['code','id','stock'])

Solving the Problem

To solve this problem, we can use the merge function to cross reference the ‘id’ column from Table A with the ‘id’ column in Table B. Then, we can use the pivot function to reshape the resulting dataframe and convert unique rows into columns.

Here is the step-by-step solution:

Step 1: Convert the ‘id’ Column in Table A to String

Before merging the two tables, we need to ensure that the data types of the ‘id’ column match. We can use the astype function to convert the ‘id’ column in Table A to string.

tableA['id'] = tableA['id'].astype(str)

Step 2: Merge Table A and Table B on the ‘id’ Column

We can use the merge function to perform an inner join between Table A and Table B on the ‘id’ column. This will create a new dataframe that contains only the rows where the ‘id’ values match in both tables.

merged_table = tableB.merge(tableA, on='id')

Step 3: Pivot the Merged Table

Finally, we can use the pivot function to reshape the merged table and convert unique rows into columns. We specify the ‘code’ column as the new index (also known as the pivot table’s row labels), the ‘date’ column as the values column, and the ‘stock’ column as the new columns.

output_table = merged_table.pivot(index='code', columns='date', values='stock')

Step 4: Get the Final Output Table

The final output table is stored in the output_table variable. We can print or display this table to see the results of our data transformation.

print(output_table)

Code Example

Here is the complete code example that combines all the steps:

import pandas as pd

# Define Table A and Table B data
listData = {'id':[1,2,3],'date':['06-05-2021','07-05-2021','17-05-2021']}
tableA = pd.DataFrame(listData,columns=['id','date'])

detailData = {'code':['D123','F268','A291','D123','F268','A291'],'id':['1','1','1','2','2','2'],'stock':[5,5,2,10,11,8]}
tableB = pd.DataFrame(detailData,columns=['code','id','stock'])

# Convert the 'id' column in Table A to string
tableA['id'] = tableA['id'].astype(str)

# Merge Table A and Table B on the 'id' column
merged_table = tableB.merge(tableA, on='id')

# Pivot the merged table
output_table = merged_table.pivot(index='code', columns='date', values='stock')

# Get the final output table
print(output_table)

Output

The output of this code example is:

            06-05-2021  07-05-2021
code            
A291           2.0     8.0
D123          5.0    10.0
F268          5.0    11.0

This output table shows the stock values for each code and date, which is the desired result of our data transformation.

We hope this article has provided a clear explanation of how to cross reference data from two tables and convert unique rows into columns using pandas. With these steps, you should be able to solve similar data transformation problems in your own projects.

Last modified on 2023-05-22