Finding the First Non-zero Value in Each Row of a Pandas DataFrame

In this article, we will explore different ways to find the first non-zero value in each row of a Pandas DataFrame. We’ll examine various approaches, including using lookup, .apply, and filling missing values with the smallest possible value.

Overview of Pandas DataFrames

Before diving into the solution, let’s briefly review how Pandas DataFrames are structured and some fundamental operations you can perform on them.

A Pandas DataFrame is a two-dimensional data structure consisting of rows and columns. It’s similar to an Excel spreadsheet or a table in a relational database. Each row represents a single record, while each column represents a field or attribute of that record.

The idxmax function returns the indices of the maximum values along an axis of the DataFrame. The max function with the axis=1 argument finds the maximum value for each row, and skipna=True ignores any missing (NaN) values when calculating this max.

Current Solution

The current solution using lookup is shown in the original question:

first_nonzero_colnames = (df > 0).idxmax(axis=1, skipna=True)
df.lookup(first_nonzero_colnames.index, first_nonzero_colnames.values)

[ 2.   1.  13.]

However, we can improve this solution to avoid using lookup and .apply.

Filling Missing Values with NaNs

To find the first non-zero value in each row, we need all non-zero values replaced with missing (NaN) values before filling them. We can use the .replace function for this purpose.

# Replace all non-zeros with NaNs
df.replace(0, np.nan)

This code replaces all non-zero values (0) in the DataFrame with np.nan, which is a special value representing missing data in Pandas.

Filling Missing Values from the Right

Next, we’ll fill these missing values with the smallest possible value using the .bfill function. Since NaN values are already present, this operation will effectively “move” all non-zero values to the right.

# Fill missing values with NaNs and then with the first available non-NaN value from the right
res = df[df != 0.0].bfill(axis=1)['A']

Here, df is our DataFrame with replaced zeros (np.nan) filled in on the left side of each row.

Using .replace for a Quicker Solution

As suggested by @piRSquared, we can use both replace and .bfill in one step. Here’s how it works:

# Replace all non-zeros with NaNs and then fill from the right
df.replace(0, np.nan).bfill(1).iloc[:, 0]

This solution is quicker because it reduces code repetition.

Conclusion

We’ve covered a few methods for finding the first non-zero value in each row of a Pandas DataFrame. We began with lookup, but improved upon this approach by using .replace and .bfill. This allows us to easily fill missing values, which can lead to cleaner data analysis results.

These steps provide an efficient and clean way to find your desired answer - the first non-zero value in each row of a Pandas DataFrame.

Last modified on 2024-02-24