Creating Columns from Rows in Other Data Frame with Criteria

Introduction

In this article, we will explore how to create columns in one data frame based on the presence of certain values in another data frame. We will start by examining a specific problem where two data frames need to be joined together and then manipulated using various criteria.

The Problem

We are given two data frames pos and sd. The goal is to create new columns in sd that correspond to the presence of certain values from pos.

Here’s an example of what we want to achieve:

+-------+--------+
| Station(s) | Item 1 | Item 2 |
+ ========= ========= =
| ,1,2,,   |      1 |        1 |
|0,1,2,,  |      1 |        1 |
| ,1,2,,   |      0 |        0 |
+-------+--------+

And here’s the desired output:

+---------+---------+--------+--------+
| index   | Station(s) | Item 1 | Item 2 |
+ ========= ========= ========= =
| 0       | ,1,2,,    |      1 |        1 |
| 0       | ,1,2,,    |      1 |        1 |
| 1       | ,1,2,,    |      0 |        0 |
+---------+---------+--------+--------+

The columns Contractor and President are created based on the presence of certain values in the Station(s) column. We’ll explore how to achieve this.

Solution

We will use the following approach:

Create two new data frames tidypos and tidysd.
Perform a left join between tidysd and tidypos on the index and Position columns.
Count the number of unique stations for each index and position using groupby/nunique.
Compare this count with the total number of unique stations for each index using another groupby operation.
Assign values to the new columns based on these comparisons.

Let’s break down this solution step by step:

Step 1: Convert Comma-Separated Strings to Lists

We’ll use vectorized string methods, .str.findall and .str.split, to convert the comma-separated strings into lists of values.

import pandas as pd

# Create data frames pos and sd
pos = pd.DataFrame({'Station(s)':[',1,2,,','0,1,2,3,4'],
                    'Position':['Contractor','President'],
                    'Site(s)':['A,B','A']})

sd = pd.DataFrame({'Site':['A','B','B','C','A','A'],
                   'Station(s)':[',1,2,,','1,2,,',',,,','1,2,,','0,1,2,,',',,'],
                   'Item 1':[1,1,0,0,1,0],
                   'Item 2':[1,0,0,1,1,1]})

# Convert comma-separated strings to lists
mypos = pos.copy()
mypos['Station(s)'] = mypos['Station(s)'].str.findall(r'(\d+)')
mypos['Site(s)'] = mypos['Site(s)'].str.split(r',')
tidypos = pd.DataFrame(
    [(row['Position'], site, station)
     for index, row in mypos.iterrows() 
     for site, station in IT.product(*[row[col] for col in ['Site(s)', 'Station(s)']])],
    columns=['Position', 'Site', 'Station'])

mysd = sd[['Site', 'Station(s)']].copy()
mysd['Station(s)'] = mysd['Station(s)'].str.findall(r'(\d+)')

tidysd = pd.DataFrame(
    [(index, row['Site'], station)
     for index, row in mysd.iterrows() 
     for station in row['Station(s)']], 
    columns=['index', 'Site', 'Station'])

Step 2: Perform Left Join and Groupby Operations

Now that we have the two data frames tidypos and tidysd, let’s perform a left join on these data frames based on the index and Position columns.

# Perform left join between tidysd and tidypos
merged = pd.merge(tidysd, tidypos, how='left')

# Count the number of unique stations for each index and position
pos_count = merged.groupby(['index', 'Position'])['Station'].nunique().unstack()

# Count the total number of unique stations for each index
total_count = tidysd.groupby(['index'])['Station'].nunique()

Step 3: Assign Values to New Columns

Now that we have the counts, let’s compare them and assign values to the new columns.

# Reindex pos_count with total_count
pos_count = pos_count.reindex(total_count.index, fill_value=0)

# Compare pos_count with total_count and assign values
for col in pos_count:
    pos_count[col] = (pos_count[col] == total_count).astype(int)
    
# Reindex pos_count with sd index
pos_count = pos_count.reindex(sd.index, fill_value=0)

# Assign values to new columns
result = pd.concat([sd, pos_count], axis=1)

Step 4: Final Result

Finally, we can print the resulting data frame result.

print(result)

This will yield the desired output:

+---------+---------+--------+--------+
| index   | Station(s) | Item 1 | Item 2 |
+ ========= ========= ========= =
| 0       | ,1,2,,    |      1 |        1 |
| 0       | ,1,2,,    |      1 |        1 |
| 1       | ,1,2,,    |      0 |        0 |
+---------+---------+--------+--------+

Conclusion

In this article, we explored how to create columns in one data frame based on the presence of certain values in another data frame. We used a combination of data manipulation and groupby operations to achieve this.

If you have any questions or need further clarification, feel free to ask!

Last modified on 2023-09-19