Applying Operations on Rows of a DataFrame with Variable Columns Affected

Introduction

In this article, we will explore how to apply operations on rows of a pandas DataFrame but with variable columns affected. We will use the provided example as a starting point and walk through the steps needed to achieve our goal.

The original question is asking for a faster way to replace certain values in a DataFrame, where the replacement values depend on the column being processed. This problem can be approached using various techniques such as list comprehension, numpy broadcasting, or even vectorized operations using pandas’ built-in functions.

Problem Statement

Given a DataFrame df with columns that need to be processed based on another column SystemStart. The goal is to replace certain values in the rows of df with NaN (Not a Number) where the corresponding row’s SystemStart value falls below a certain threshold.

For example, consider the following representative DataFrame:

In this case, the SystemStart values are ‘2016-01-05’, ‘2016-01-06’, and ‘2016-01-07’ respectively. We want to replace the values in the columns that fall before the SystemStart date with NaN.

Step 1: Convert SystemStart Column to datetime Format

To make further operations easier, we first need to convert the SystemStart column to a datetime format using pandas’ pd.to_datetime() function:

df['SystemStart'] = pd.to_datetime(df['SystemStart'])

This ensures that all date strings are in a consistent format.

Step 2: Strip Out SystemStart Column

Next, we strip out the SystemStart column from our DataFrame using pandas’ drop() method. This will leave us with only the columns whose values need to be processed based on the SystemStart value.

st = df['SystemStart']
d1 = df.drop('SystemStart', 1)

Step 3: Convert Remaining Columns to datetime Format

Since some of the remaining columns are also date strings, we want to convert them to a consistent format as well. We use pandas’ pd.to_datetime() function again:

d1.columns = pd.to_datetime(d1.columns)

This ensures that all column names are in a consistent format.

Step 4: Use NumPy Broadcasting to Mask Values

Now, we can use numpy broadcasting to mask the values in the columns that fall before the SystemStart date. We create an array of dates from one day prior to the SystemStart date using pandas’ pd.date_range() function:

zero_date_range = pd.date_range(start='2016-01-04', end=df.loc[msn,'SystemStart'] - pd.Timedelta(days=1), freq='D')

We then use numpy broadcasting to create a boolean mask where the values are less than or equal to the SystemStart date. This will give us a boolean array with shape (3, 3), where each value corresponds to the row and column in our DataFrame.

mask = d1.columns.values[:, None] >= st.values[None, :]

Note that we use None as a singleton dimension to create an array of shape (3,). We also need to add the outer dimension using [:, None].

Step 5: Join SystemStart Back into DataFrame

Finally, we can join the original DataFrame with the boolean mask and the SystemStart values. This will give us our final result:

d1.where(mask).join(st)

This operation replaces the values in the columns that fall before the SystemStart date with NaN.

Conclusion

In this article, we have explored how to apply operations on rows of a pandas DataFrame but with variable columns affected. We used a combination of list comprehension, numpy broadcasting, and pandas’ built-in functions to achieve our goal. The resulting code is concise and efficient, making it suitable for large datasets.

Example Code

import pandas as pd

# Create example DataFrame
df = pd.DataFrame({
    'index': ['one', 'two', 'three'],
    '2016-01-05 00:00:00': [24, 21, 62],
    '2016-01-06 00:00:00': [23, 91, 77],
    '2016-01-07 00:00:00': [65, 59, 2]
})

# Convert SystemStart column to datetime format
df['SystemStart'] = pd.to_datetime(df['SystemStart'])

# Strip out SystemStart column
st = df['SystemStart']
d1 = df.drop('SystemStart', 1)

# Convert remaining columns to datetime format
d1.columns = pd.to_datetime(d1.columns)

# Create boolean mask using numpy broadcasting
zero_date_range = pd.date_range(start='2016-01-04', end=df.loc[msn,'SystemStart'] - pd.Timedelta(days=1), freq='D')
mask = d1.columns.values[:, None] >= st.values[None, :]

# Join SystemStart back into DataFrame using boolean mask
result = d1.where(mask).join(st)

print(result)

Note that this code assumes that the SystemStart column is in datetime format. If it’s not, you’ll need to convert it first using pandas’ pd.to_datetime() function.

Last modified on 2023-11-03