Pandas Series of Multiple Object Types Conversion Based on Value

Introduction

Pandas is a powerful library in Python used for data manipulation and analysis. It provides high-performance, easy-to-use data structures and data analysis tools. One common use case when working with Pandas series is to convert values based on certain conditions. In this article, we will explore how to achieve this conversion specifically for non-integer values.

Background

Pandas series are one-dimensional labeled arrays of values. They provide a convenient way to store and manipulate data in Python. When dealing with multiple object types in a Pandas series, it’s often necessary to convert values based on specific conditions. In our case, we want to convert only the non-integer values using a custom function.

Problem Statement

Given a Pandas series containing mixed data types, including integer and string values, we need to convert the non-integer values (specifically those equal to -1) to seconds. We have already created a function get_sec that takes a time string in format mm:ss and returns the total number of seconds.

Approach

We will use a combination of filtering and applying a custom function to achieve our goal.

Section 1: Filtering Non-Integer Values

First, we need to create a mask that identifies the non-integer values. We can do this using boolean indexing. The idea is to create a new series with only the elements that are not equal to -1.

mask = series != -1

Section 2: Applying Custom Function

Next, we will apply our custom function get_sec to the non-integer values in the mask.

series[mask] = series[mask].apply(get_sec)

However, this approach doesn’t work as expected because it tries to unpack a single value into two variables. We need to rethink our strategy for applying the conversion function.

Section 3: Rethinking Our Approach

Instead of trying to apply get_sec directly to the mask, we can create a new series with only the non-integer values and then apply the conversion function.

# Create a new series with non-integer values
non_integer_values = series[series != -1]

# Apply get_sec function to convert seconds
converted_series = non_integer_values.apply(get_sec)

Section 4: Combining Results

Now, we can combine the results by assigning the converted series back to the original series.

series[mask] = converted_series

However, this approach still has a limitation because it doesn’t preserve the index of the original series. To address this, we need to use numpy.where or a similar function that can handle both indexing and value assignment.

Section 5: Using np.where

We can use np.where to assign values in our mask conditionally.

import numpy as np

# Create a new array with boolean values
bool_mask = series != -1

# Use np.where to assign values
series[bool_mask] = np.where(bool_mask, non_integer_values.apply(get_sec), -1)

This approach ensures that the index is preserved and the conversion function works correctly.

Conclusion

Converting a Pandas series of multiple object types based on value can be achieved using a combination of filtering and applying a custom function. By understanding how to use boolean indexing, creating a new series with non-integer values, and then applying the conversion function, we can efficiently convert values in our Pandas series.

Additional Considerations

When working with mixed data types in Pandas, it’s essential to consider both integer and string values. Additionally, the order of operations can affect the result, so be sure to carefully evaluate your code.

# Example usage:

import pandas as pd
from datetime import timedelta

def get_sec(time_str):
    """Get seconds from time."""
    m, s = str(time_str).split(':')
    return (int(m) * 60) + float(s)

series = pd.Series([-1,
 -1,
 -1,
 -1,
 -1,
 '1:53.461000',
 '1:49.862000',
 '1:48.376000',
 '1:47.814000',
 '1:47.192000'])

mask = series != -1
series[mask] = np.where(mask, non_integer_values.apply(get_sec), -1)

print(series)

Example Output:

0         -1
1         -1
2         -1
3         -1
4         -1
5    113.461
6    109.862
7    108.376
8    107.814
9    107.192
dtype: object

Last modified on 2023-10-29