Python How to Replace Negative Value with Zero in Pandas DataFrame

This tutorial will go three ways to replace negative values with zero in a Pandas DataFrame.

This simplest way to do it is:

# Import pandas module

import pandas as pd
  
# Create pandas DataFrame

df = pd.DataFrame({"X": [4, 5, -3, 4, -5, 6],
                   "Y": [3, -5, -6, 7, 3, -2],
                   "Z": [-4, 5, 6, -7, 5, 4]})

df[df<0] = 0

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Example #1

Let’s look at an example of using boolean indexing to replace all negative values in a DataFrame with 0. First, we will import the pandas module and create the DataFrame.

# Import pandas module

import pandas as pd
  
# Create pandas DataFrame

df = pd.DataFrame({"X": [4, 5, -3, 4, -5, 6],
                   "Y": [3, -5, -6, 7, 3, -2],
                   "Z": [-4, 5, 6, -7, 5, 4]})

print(df)

df[df<0] = 0

print(df)

   X  Y  Z
0  4  3 -4
1  5 -5  5
2 -3 -6  6
3  4  7 -7
4 -5  3  5
5  6 -2  4

Next, we will define a condition and apply it to all the values in the DataFrame:

# Replace all elements < 0 with 0

df[df<0] = 0

# View DataFrame

print(df)

Let’s run the code to get the result:

   X  Y  Z
0  4  3  0
1  5  0  5
2  0  0  6
3  4  7  0
4  0  3  5
5  6  0  4

We can see that the negative values in the DataFrame were replaced by zeros.

Example #2

Let’s look at an example where one of the columns is not numeric.

# Import pandas module

import pandas as pd
  
# Create pandas DataFrame

df = pd.DataFrame({"X": [4, 5, -3, 4, -5, 6],
                   "Y": [3, -5, -6, 7, 3, -2],
                   "Z": ["do", "re", "mi", "fa", "so", "la"]})

print(df)

   X  Y   Z
0  4  3  do
1  5 -5  re
2 -3 -6  mi
3  4  7  fa
4 -5  3  so
5  6 -2  la

In this case, we can use the _get_numeric_data() method to get the numeric columns from the DataFrame. Then, we can apply boolean indexing to those columns.

# Get numeric columns from DataFrame

num_cols = df._get_numeric_data()

# Replace all elements < 0 with 0

num_cols[num_cols < 0 ] = 0

# View DataFrame

print(df)

Let’s run the code to get the result:

   X  Y   Z
0  4  3  do
1  5  0  re
2  0  0  mi
3  4  7  fa
4  0  3  so
5  6  0  la

We can see that the negative values in the DataFrame were replaced by zeros.

Example #3

Let’s look at an example of a DataFrame with dtype timedelta columns. We can convert numerical columns to timedelta using the to_timedelta() method. In this case, we will express the numeric values as number of days.

# Import pandas module

import pandas as pd
  
# Create pandas DataFrame

df = pd.DataFrame({"X": pd.to_timedelta([4, 5, -3, 4, -5, 6], 'd'),
                   "Y": pd.to_timedelta([3, -5, -6, 7, 3, -2], 'd')})

print(df)

        X       Y   Z
0  4 days  3 days  do
1  5 days -5 days  re
2 -3 days -6 days  mi
3  4 days  7 days  fa
4 -5 days  3 days  so
5  6 days -2 days  la

We can use pd.Timedelta in the comparison as follows:

# Replace all values with timedelta < 0 days with 0

df[df < pd.Timedelta(0)] = 0

# View DataFrame

print(df)

Let’s run the code to get the result:

          X                Y
0  4 days 00:00:00  3 days 00:00:00
1  5 days 00:00:00                0
2                0                0
3  4 days 00:00:00  7 days 00:00:00
4                0  3 days 00:00:00
5  6 days 00:00:00                0

We can see that the values with negative days were replaced by zeros.

Summary

Congratulations on reading to the end of this tutorial!

For further reading on converting negative values to zeros, go to the following article:

Python How to Replace Negative Value with Zero in Numpy Array

To learn more about Python for data science and machine learning, go to the online courses page on Python for the most comprehensive courses available.

Have fun and happy researching!