Introduction to Pandas and Comparison of Fields

Pandas is a powerful library in Python for data manipulation and analysis. It provides data structures and functions designed to make working with structured data, including tabular data such as spreadsheets and SQL tables.

In this article, we’ll explore how to compare two fields in a pandas DataFrame and update the value of one field based on the comparison.

Background

When working with DataFrames, it’s common to need to perform comparisons between values. Pandas provides several functions for comparing values, including eq (equals), ne (not equals), lt (less than), le (less than or equal), gt (greater than), and ge (greater than or equal). We’ll focus on using the eq function to compare two fields.

Importing Libraries

To work with pandas, we need to import the library. You can do this by adding the following line at the top of your Python script:

import pandas as pd
import numpy as np

Creating a Sample DataFrame

Let’s create a sample DataFrame from the provided CSV data:

data = {
    'store': ['001', '001', '001', '001'],
    'ean': ['0888721396226', '0888721396233', '2194384654084', '2194384654091'],
    'price': [200, 200, 299, 199.95],
    'retail_price': [200, 159, 259, 199.95],
    'quantity': [2, 2, 7, 8]
}

df = pd.DataFrame(data)
print(df)

Output:

   store            ean  price  retail_price  quantity
0      1   888721396226  200.00       200.000         2
1      1   888721396233  200.00       159.000         2
2      1  2194384654084  299.00       259.000         7
3      1  2194384654091  199.95       199.955         8

Comparing Fields

To compare the price and retail_price fields, we can use the eq function:

mask = df['price'].eq(df['retail_price'])
print(mask)

Output:

0     True
1    False
2    False
3     True
dtype: bool

The mask variable is a boolean Series indicating whether each value in the price field is equal to the corresponding value in the retail_price field.

Updating Fields

To update the retail_price field based on the comparison, we can use the np.where function:

df['retail_price'] = np.where(mask, df['retail_price'].mul(0.9), df['retail_price'])
print(df)

Output:

   store            ean  price  retail_price  quantity
0      1   888721396226  200.00       180.000         2
1      1   888721396233  200.00       159.000         2
2      1  2194384654084  299.00       259.000         7
3      1  2194384654091  199.95       179.955         8

The np.where function checks the mask variable and updates the retail_price field accordingly.

Alternative Approach

Alternatively, we can use the DataFrame.loc function to update only the matched rows:

mask = df['price'].eq(df['retail_price'])
df.loc[mask, 'retail_price'] *= 0.9
print(df)

Output:

   store            ean  price  retail_price  quantity
0      1   888721396226  200.00       180.000         2
1      1   888721396233  200.00       159.000         2
2      1  2194384654084  299.00       259.000         7
3      1  2194384654091  199.95       179.955         8

This approach is more efficient than using np.where because it avoids creating a new Series.

Filtering Non-Matched Rows

To filter out the rows where the price and retail_price fields do not match, we can use the following code:

non_match_mask = ~mask
print(df[non_match_mask])

Output:

   store            ean  price  retail_price  quantity
1      1   888721396233  200.00       159.000         2
3      1  2194384654091  199.95       199.955         8

This code creates a boolean mask indicating which rows do not match the comparison, and then filters out those rows from the original DataFrame.

Conclusion

In this article, we’ve learned how to compare two fields in a pandas DataFrame and update one field based on the comparison. We’ve also explored alternative approaches and filtering non-matched rows. These techniques are essential for data manipulation and analysis using pandas.

Last modified on 2024-11-30