Selecting rows based on multiple conditions in Pandas DataFrame

Last update on December 21 2024 07:43:24 (UTC/GMT +8 hours)

Select rows from a DataFrame based on multiple conditions.

Sample Solution:

Python Code:

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Teodosija', 'Sutton', 'Taneli', 'Ravshan', 'Ross'],
        'Age': [26, 32, 25, 31, 28],
        'Salary': [50000, 60000, 45000, 70000, 55000]}

df = pd.DataFrame(data)

# Select rows based on multiple conditions
selected_rows = df[(df['Age'] > 25) & (df['Salary'] > 50000)]

# Display the selected rows
print(selected_rows)

Output:

      Name  Age  Salary
1   Sutton   32   60000
3  Ravshan   31   70000
4     Ross   28   55000

Explanation:

• Importing Pandas:
  import pandas as pd
  Imports the Pandas library and aliases it as "pd" for convenience.
• Creating a Sample DataFrame:
  data = {'Name': ['Teodosija', 'Sutton', 'Taneli', 'Ravshan', 'Ross'], 'Age': [26, 32, 25, 31, 28], 'Salary': [50000, 60000, 45000, 70000, 55000]} df = pd.DataFrame(data)
  Creates a sample DataFrame (df) with columns 'Name', 'Age', and 'Salary'.
• Selecting Rows Based on Multiple Conditions:
  selected_rows = df[(df['Age'] > 25) & (df['Salary'] > 50000)]
  Uses boolean indexing to select rows where both conditions are true: age is greater than 25 and salary is greater than 50000.
• Displaying the Selected Rows:
  print(selected_rows)
  Prints the selected rows to the console.

Flowchart:

Python Code Editor:

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