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| Topics Covered | Price (USD) | Time (Days) |
|---|---|---|
| Data Manipulation Basics | $40 | 2 |
| Data Cleaning & Preparation | $50 | 3 |
| Data Merging & Joining | $45 | 2 |
| Data Visualization with Pandas | $60 | 4 |
| Time Series Analysis | $55 | 3 |
| Advanced Indexing Techniques | $65 | 4 |
| Performance Optimization | $70 | 5 |
- Pandas Assignment Help
- Data Filtering and Sorting Using Pandas
- Key Aspects of our service at programminghomeworkhelp.com
- Topics Covered by Our Pandas Assignment Experts
- Adaptable Payment Options in 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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