Write a Python program to trim the minimum and maximum threshold value in a dataframe

Sometimes you need to limit values in a DataFrame to fall within specific minimum and maximum thresholds. Pandas provides the clip() method to trim values that exceed these boundaries.

Understanding DataFrame Clipping

The clip() method constrains values between a lower and upper limit:

• lower parameter sets the minimum threshold

• upper parameter sets the maximum threshold

• Values below the lower limit are replaced with the lower limit

• Values above the upper limit are replaced with the upper limit

Syntax

DataFrame.clip(lower=None, upper=None, axis=None)

Creating Sample Data

Let's create a DataFrame with values that need trimming :

import pandas as pd

data = {"Column1": [12, 34, 56, 78, 28],
        "Column2": [23, 30, 25, 50, 90]}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

Original DataFrame:
   Column1  Column2
0       12       23
1       34       30
2       56       25
3       78       50
4       28       90

Applying Minimum Threshold

Setting a lower bound of 30 replaces all values below 30 :

import pandas as pd

data = {"Column1": [12, 34, 56, 78, 28],
        "Column2": [23, 30, 25, 50, 90]}
df = pd.DataFrame(data)

min_threshold = df.clip(lower=30)
print("Minimum threshold (lower=30):")
print(min_threshold)

Minimum threshold (lower=30):
   Column1  Column2
0       30       30
1       34       30
2       56       30
3       78       50
4       30       90

Applying Maximum Threshold

Setting an upper bound of 50 replaces all values above 50 :

import pandas as pd

data = {"Column1": [12, 34, 56, 78, 28],
        "Column2": [23, 30, 25, 50, 90]}
df = pd.DataFrame(data)

max_threshold = df.clip(upper=50)
print("Maximum threshold (upper=50):")
print(max_threshold)

Maximum threshold (upper=50):
   Column1  Column2
0       12       23
1       34       30
2       50       25
3       50       50
4       28       50

Applying Both Thresholds

Combining both lower and upper bounds constrains all values between 30 and 50 :

import pandas as pd

data = {"Column1": [12, 34, 56, 78, 28],
        "Column2": [23, 30, 25, 50, 90]}
df = pd.DataFrame(data)

clipped_df = df.clip(lower=30, upper=50)
print("Clipped DataFrame (30 ? values ? 50):")
print(clipped_df)

Clipped DataFrame (30 ? values ? 50):
   Column1  Column2
0       30       30
1       34       30
2       50       30
3       50       50
4       30       50

Complete Example

Here's the complete program demonstrating all three clipping operations :

import pandas as pd

data = {"Column1": [12, 34, 56, 78, 28],
        "Column2": [23, 30, 25, 50, 90]}
df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)
print("\nMinimum threshold (lower=30):")
print(df.clip(lower=30))
print("\nMaximum threshold (upper=50):")
print(df.clip(upper=50))
print("\nClipped DataFrame (30 ? values ? 50):")
print(df.clip(lower=30, upper=50))

Original DataFrame:
   Column1  Column2
0       12       23
1       34       30
2       56       25
3       78       50
4       28       90

Minimum threshold (lower=30):
   Column1  Column2
0       30       30
1       34       30
2       56       30
3       78       50
4       30       90

Maximum threshold (upper=50):
   Column1  Column2
0       12       23
1       34       30
2       50       25
3       50       50
4       28       50

Clipped DataFrame (30 ? values ? 50):
   Column1  Column2
0       30       30
1       34       30
2       50       30
3       50       50
4       30       50

Conclusion

The clip() method is useful for data preprocessing when you need to constrain values within specific bounds. Use lower for minimum thresholds, upper for maximum thresholds, or both together for range limiting.