Python - Calculate the maximum of column values of a Pandas DataFrame

To find the maximum value in Pandas DataFrame columns, use the max() function. This method works on individual columns or across the entire DataFrame to identify the highest values.

Basic DataFrame Creation

First, let's create a sample DataFrame with car data ?

import pandas as pd

# Create DataFrame with car data
car_data = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90]
})

print("Car DataFrame:")
print(car_data)

Car DataFrame:
      Car  Units
0     BMW    100
1   Lexus    150
2    Audi    110
3   Tesla     80
4 Bentley    110
5  Jaguar     90

Finding Maximum of a Single Column

Use max() to find the highest value in a specific column ?

import pandas as pd

car_data = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90]
})

# Find maximum units sold
max_units = car_data['Units'].max()
print(f"Maximum Units sold: {max_units}")

Maximum Units sold: 150

Finding Maximum Across Multiple Columns

You can find the maximum values for all numeric columns at once ?

import pandas as pd

# Create DataFrame with multiple numeric columns
product_data = pd.DataFrame({
    "Product": ['TV', 'PenDrive', 'HeadPhone', 'EarPhone', 'HDD', 'SSD'],
    "Price": [8000, 500, 3000, 1500, 3000, 4000],
    "Stock": [50, 200, 100, 150, 80, 120]
})

print("Product DataFrame:")
print(product_data)

print("\nMaximum values for all numeric columns:")
print(product_data.max(numeric_only=True))

Product DataFrame:
   Product  Price  Stock
0       TV   8000     50
1 PenDrive    500    200
2 HeadPhone   3000    100
3 EarPhone   1500    150
4      HDD   3000     80
5      SSD   4000    120

Maximum values for all numeric columns:
Price    8000
Stock     200
dtype: int64

Finding Maximum with Row Information

To get the entire row containing the maximum value, use idxmax() ?

import pandas as pd

product_data = pd.DataFrame({
    "Product": ['TV', 'PenDrive', 'HeadPhone', 'EarPhone', 'HDD', 'SSD'],
    "Price": [8000, 500, 3000, 1500, 3000, 4000],
    "Stock": [50, 200, 100, 150, 80, 120]
})

# Find index of row with maximum price
max_price_index = product_data['Price'].idxmax()
print(f"Index of maximum price: {max_price_index}")

# Get the complete row with maximum price
max_price_row = product_data.loc[max_price_index]
print(f"\nProduct with highest price:")
print(max_price_row)

Index of maximum price: 0

Product with highest price:
Product    TV
Price    8000
Stock      50
Name: 0, dtype: object

Comparison of Methods

Conclusion

Use max() to find maximum column values in Pandas DataFrames. Combine with idxmax() to retrieve complete rows containing maximum values for detailed analysis.