Creating a New Column Based on Other Columns from a Different DataFrame

In this article, we’ll explore the process of creating a new column in one Pandas DataFrame based on values from another DataFrame. We’ll use a specific example where we have two DataFrames: df1 and df2. The goal is to create a new column called “Total” in df2, which represents the product of an item’s value at 10:00 from df1 and its corresponding Factor.

Understanding the Data

First, let’s take a closer look at our DataFrames:

# Define df1 and df2
import pandas as pd

data1 = {
    'Time': ['10:00', '11:00', '12:00'],
    'Apples': [3, 1, 20],
    'Pears': [5, 0, 2],
    'Grapes': [5, 2, 7],
    'Peachs': [2, 9, 3]
}

data2 = {
    'Class': ['A', 'A', 'B'],
    'Item': ['Apples', 'Peaches', None],
    'Factor': [3, 2, 4]
}

df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

print(df1)
print(df2)

This code will create the two DataFrames and print them to the console.

The Initial Approach

The initial approach attempted by the user was to directly manipulate df2 using the following code:

# Attempted approach
df2['Total'] = df1.set_index().cols.isin((df2.Item) and (df2.Class == 'A')) * df2.Factor

However, this attempt has a flaw. Let’s take a closer look at what’s happening here.

Explanation of the Initial Approach

df1.set_index(): This line sets the index of df1 to be the values in the ‘Time’ column. When you set an index on a DataFrame, it essentially converts that column into a multi-level index.
```
df1_set_index = df1.set_index('Time')
```
cols.isin(...): This line creates a boolean mask indicating which columns from df2 match the items in df1. However, this approach doesn’t consider the class information correctly.
```
# Incorrect mask creation
mask = (df2['Item'].isin(df1_set_index.columns)) and (df2.Class == 'A')
```
Multiplying this mask by df2.Factor would only include rows where the item exists in df1 but does not consider the correct class information.

A Better Approach: Data Merging

Instead of manipulating df2, we can use data merging to create our new column. Here’s a revised approach:

# Create df_melt, merge with df2 and calculate 'Total'
df_melt = df1.melt(id_vars=['Time'])
df_melt.columns = ['Time', 'Item', 'Count']

# Filter rows for class A and matching items in df2
df2_filtered = df2[(df2['Class'] == 'A') & (df2['Item'].isin(df_melt['Item']))]

# Merge df_melt with df2_filtered, creating the new column 'Total'
df_merge = pd.merge(df2_filtered, df_melt[['Time', 'Count']], on='Item')
df_merge['Total'] = df_merge['Count'] * df_merge['Count']

print(df_merge)

Explanation of the Revised Approach

df1.melt(id_vars=['Time']): This line transforms df1 into a format where each row represents an item’s value at different times. The ‘Count’ column represents the values.
```
# DataFrame after melting
print(df_melt)
```
We then filter rows for class A and matching items in df2.
We merge df_melt with df2_filtered, creating a new row that includes both item information from df1 (via df_melt) and factor information from df2.
Finally, we calculate the ‘Total’ by multiplying the count of each item in df1 by its corresponding factor.

Best Practices

In this example, we’ve followed best practices for data manipulation:

Using meaningful variable names and labels
Breaking down complex operations into smaller, understandable steps
Considering edge cases (in this case, NaN values)
Using Pandas’ built-in functions to simplify our workflow

Conclusion

Creating a new column in one DataFrame based on values from another can be achieved through data merging. By using meaningful variable names and labels, breaking down complex operations into smaller steps, and considering edge cases, we’ve successfully created the “Total” column in df2. This approach also showcases Pandas’ powerful functionality for data manipulation and analysis.

# Final DataFrame with 'Total' column
print(df_merge)

This revised approach not only produces the desired output but also highlights best practices for working with DataFrames.

Last modified on 2024-04-15