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Topics CoveredPrice (USD)Time (Days)
Data Manipulation Basics$402
Data Cleaning & Preparation$503
Data Merging & Joining$452
Data Visualization with Pandas$604
Time Series Analysis$553
Advanced Indexing Techniques$654
Performance Optimization$705
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Key Topics
  • Pandas Assignment Help
  • Data Filtering and Sorting Using Pandas
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  • Topics Covered by Our Pandas Assignment Experts
  • Adaptable Payment Options in Our Pandas Assignment Help Service
  • Exclusive Savings for Returning Students on Our Pandas Assignment Help Service

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Data Filtering and Sorting Using Pandas

import pandas as pd # Sample data (you can replace this with your actual data source) data = { 'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emily'], 'Age': [25, 30, 35, 28, 32], 'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Boston'] } # Create a DataFrame from the sample data df = pd.DataFrame(data) # Filter the DataFrame for people aged 30 and above filtered_df = df[df['Age'] >= 30] # Sort the filtered DataFrame by Age in descending order sorted_df = filtered_df.sort_values(by='Age', ascending=False) # Display the sorted DataFrame print(sorted_df)

In this example:

  • We create a Pandas DataFrame df from a dictionary data.
  • We filter df to include only rows where 'Age' is 30 or above (filtered_df = df[df['Age'] >= 30]).
  • We sort filtered_df by 'Age' in descending order (sorted_df = filtered_df.sort_values(by='Age', ascending=False)).
  • Finally, we print sorted_df, which displays the sorted DataFrame.

This code demonstrates how Pandas can be used for basic data manipulation tasks like filtering and sorting data.

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