Python - Search DataFrame for a specific value with pandas

Searching a DataFrame for specific values is a common operation in data analysis. Pandas provides several methods to locate and retrieve rows based on specific criteria using boolean indexing and iloc.

Creating the DataFrame

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

import pandas as pd

# Creating DataFrame with car data
dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],
    "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],
    "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],
    "Units_Sold": [100, 120, 150, 110, 200, 250]
})

print("DataFrame:")
print(dataFrame)

DataFrame:
       Car  Cubic_Capacity  Reg_Price  Units_Sold
0      BMW            2000       7000         100
1    Lexus            1800       1500         120
2    Tesla            1500       5000         150
3  Mustang            2500       8000         110
4 Mercedes            2200       9000         200
5   Jaguar            3000       6000         250

Method 1: Using Boolean Indexing

The most efficient way to search for specific values is using boolean indexing ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],
    "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],
    "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],
    "Units_Sold": [100, 120, 150, 110, 200, 250]
})

# Search for car with Registration Price 5000
result = dataFrame[dataFrame['Reg_Price'] == 5000]
print("Car with Registration Price 5000:")
print(result)

Car with Registration Price 5000:
     Car  Cubic_Capacity  Reg_Price  Units_Sold
2  Tesla            1500       5000         150

Method 2: Using Loop with iloc

You can also use a loop to find the index and then use iloc to retrieve the row ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],
    "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],
    "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],
    "Units_Sold": [100, 120, 150, 110, 200, 250]
})

# Search for car with Registration Price 5000 using loop
for i in range(len(dataFrame)):
    if 5000 == dataFrame.iloc[i]['Reg_Price']:
        indx = i
        break

# Display the found value
print("Found car at index", indx)
print(dataFrame.iloc[indx])

Found car at index 2
Car               Tesla
Cubic_Capacity     1500
Reg_Price          5000
Units_Sold          150
Name: 2, dtype: object

Searching Multiple Values

You can search for multiple conditions using logical operators ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],
    "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],
    "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],
    "Units_Sold": [100, 120, 150, 110, 200, 250]
})

# Search for cars with price greater than 6000 and units sold greater than 120
result = dataFrame[(dataFrame['Reg_Price'] > 6000) & (dataFrame['Units_Sold'] > 120)]
print("Cars with price > 6000 and units sold > 120:")
print(result)

Cars with price > 6000 and units sold > 120:
       Car  Cubic_Capacity  Reg_Price  Units_Sold
2    Tesla            1500       5000         150
4 Mercedes            2200       9000         200
5   Jaguar            3000       6000         250

Comparison

Conclusion

Boolean indexing is the most efficient method for searching DataFrames. Use iloc with loops only when you need the specific index position or complex conditional logic.